(pd_nestml) wdg562@SUN1027631 NESTML_components % python3 create_gap_dopamod_synapse.py -- N E S T -- Copyright (C) 2004 The NEST Initiative Version: 3.8.0 Built: Aug 27 2024 04:38:39 This program is provided AS IS and comes with NO WARRANTY. See the file LICENSE for details. Problems or suggestions? Visit https://www.nest-simulator.org Type 'nest.help()' to find out more about NEST. WARNING:root:PyGSL is not available. The stiffness test will be skipped. WARNING:root:Error when importing: No module named 'pygsl' [1,GLOBAL, INFO]: List of files that will be processed: [2,GLOBAL, INFO]: /opt/miniconda3/envs/pd_nestml/models/neurons/aeif_cond_alpha_neuron.nestml [3,GLOBAL, INFO]: /opt/miniconda3/envs/pd_nestml/models/synapses/dopamine_synapse_NEW.nestml [4,GLOBAL, INFO]: Target platform code will be generated in directory: '/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new' -- N E S T -- Copyright (C) 2004 The NEST Initiative Version: 3.8.0 Built: Aug 27 2024 04:38:39 This program is provided AS IS and comes with NO WARRANTY. See the file LICENSE for details. Problems or suggestions? Visit https://www.nest-simulator.org Type 'nest.help()' to find out more about NEST. [5,GLOBAL, INFO]: The NEST Simulator version was automatically detected as: v3.8.0 [6,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/opt/miniconda3/envs/pd_nestml/lib/python3.12/site-packages/pynestml/codegeneration/resources_nest/point_neuron' [7,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/opt/miniconda3/envs/pd_nestml/lib/python3.12/site-packages/pynestml/codegeneration/resources_nest/point_neuron' [8,GLOBAL, INFO]: Given template root path is not an absolute path. Creating the absolute path with default templates directory '/opt/miniconda3/envs/pd_nestml/lib/python3.12/site-packages/pynestml/codegeneration/resources_nest/point_neuron' [9,GLOBAL, INFO]: The NEST Simulator installation path was automatically detected as: /opt/miniconda3/envs/pd_nestml [10,GLOBAL, INFO]: Start processing '/opt/miniconda3/envs/pd_nestml/models/neurons/aeif_cond_alpha_neuron.nestml'! [11,aeif_cond_alpha_neuron_nestml, INFO, [63:28;63:55]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [12,aeif_cond_alpha_neuron_nestml, INFO, [64:30;64:83]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [13,aeif_cond_alpha_neuron_nestml, INFO, [65:30;65:83]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [14,GLOBAL, INFO]: Start processing '/opt/miniconda3/envs/pd_nestml/models/synapses/dopamine_synapse_NEW.nestml'! [15,dopamine_synapse_nestml, INFO, [28:27;28:27]]: Implicit casting from (compatible) type 'integer' to 'real'. [16,dopamine_synapse_nestml, INFO, [31:24;31:24]]: Implicit casting from (compatible) type 'integer' to 'real'. [17,dopamine_synapse_nestml, INFO, [46:21;46:29]]: Implicit casting from (compatible) type '1 / ms' to 'real'. [18,aeif_cond_alpha_neuron_nestml, WARNING, [83:8;83:17]]: Variable 'a' has the same name as a physical unit! [19,aeif_cond_alpha_neuron_nestml, INFO, [67:16;67:39]]: Implicit magnitude conversion from mV nS to pA with factor 1.0 [20,aeif_cond_alpha_neuron_nestml, INFO, [67:16;67:43]]: Implicit magnitude conversion from mV nS to pA with factor 1.0 [21,aeif_cond_alpha_neuron_nestml, INFO, [67:16;67:53]]: Implicit magnitude conversion from mV nS to pA with factor 1.0 [22,aeif_cond_alpha_neuron_nestml, INFO, [67:16;67:65]]: Implicit magnitude conversion from mV nS to pA with factor 1.0 [23,aeif_cond_alpha_neuron_nestml, INFO, [67:16;67:77]]: Implicit magnitude conversion from mV nS to pA with factor 1.0 [24,aeif_cond_alpha_neuron_nestml, INFO, [67:16;67:81]]: Implicit magnitude conversion from mV nS to pA buffer with factor 1.0 [25,aeif_cond_alpha_neuron_nestml, INFO, [68:14;68:34]]: Implicit magnitude conversion from mV nS to pA with factor 1.0 [26,aeif_cond_alpha_neuron_nestml, INFO, [63:28;63:55]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [27,aeif_cond_alpha_neuron_nestml, INFO, [64:30;64:83]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [28,aeif_cond_alpha_neuron_nestml, INFO, [65:30;65:83]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [29,dopamine_synapse_nestml, WARNING, [29:8;29:28]]: Variable 'd' has the same name as a physical unit! [30,dopamine_synapse_nestml, INFO, [28:27;28:27]]: Implicit casting from (compatible) type 'integer' to 'real'. [31,dopamine_synapse_nestml, INFO, [31:24;31:24]]: Implicit casting from (compatible) type 'integer' to 'real'. [32,dopamine_synapse_nestml, INFO, [46:21;46:29]]: Implicit casting from (compatible) type '1 / ms' to 'real'. [33,GLOBAL, INFO]: State variables that will be moved from synapse to neuron: [] [34,GLOBAL, INFO]: Parameters that will be copied from synapse to neuron: [] [35,GLOBAL, INFO]: Synaptic state variables moved to neuron that will need buffering: [] [36,GLOBAL, INFO]: In synapse: replacing ``continuous`` type input ports that are connected to postsynaptic neuron with external variable references [37,GLOBAL, INFO]: Copying parameters from synapse to neuron... [38,GLOBAL, INFO]: Adding suffix to variables in spike updates [39,GLOBAL, INFO]: In synapse: replacing variables with suffixed external variable references [40,GLOBAL, INFO]: Successfully constructed neuron-synapse pair aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml, dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml [41,GLOBAL, INFO]: Analysing/transforming model 'aeif_cond_alpha_neuron_nestml' [42,aeif_cond_alpha_neuron_nestml, INFO, [49:0;128:0]]: Starts processing of the model 'aeif_cond_alpha_neuron_nestml' INFO:root:Analysing input: INFO:root:{ "dynamics": [ { "expression": "V_m' = ((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m", "initial_values": { "V_m": "E_L" } }, { "expression": "w' = (a * ((min(V_m, V_peak)) - E_L) - w) / tau_w", "initial_values": { "w": "0" } }, { "expression": "refr_t' = (-1000.0) * 1.0 / 1000.0", "initial_values": { "refr_t": "0" } }, { "expression": "g_inh__X__inh_spikes = (e / tau_syn_inh) * t * exp(-t / tau_syn_inh)", "initial_values": {} }, { "expression": "g_exc__X__exc_spikes = (e / tau_syn_exc) * t * exp(-t / tau_syn_exc)", "initial_values": {} } ], "options": { "output_timestep_symbol": "__h" }, "parameters": { "C_m": "281.0", "Delta_T": "2.0", "E_L": "(-70.6)", "E_exc": "0", "E_inh": "(-85.0)", "I_e": "0", "V_peak": "0", "V_reset": "(-60.0)", "V_th": "(-50.4)", "a": "4", "b": "80.5", "g_L": "30.0", "refr_T": "2", "tau_syn_exc": "0.2", "tau_syn_inh": "2.0", "tau_w": "144.0" } } INFO:root:Processing global options... INFO:root:Processing input shapes... INFO:root: Processing differential-equation form shape V_m with defining expression = "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m" DEBUG:root:Splitting expression (Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T) + I_e + I_stim - g_L*(-E_L + Min(V_m, V_peak)) - 1.0*g_exc__X__exc_spikes*(-E_exc + Min(V_m, V_peak)) - 1.0*g_inh__X__inh_spikes*(-E_inh + Min(V_m, V_peak)) - w)/C_m (symbols [V_m]) DEBUG:root: linear factors: Matrix([[0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Created Shape with symbol V_m, derivative_factors = [0], inhom_term = E_L*g_L/C_m + I_e/C_m, nonlin_term = Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Returning shape: Shape "V_m" of order 1 INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Processing differential-equation form shape w with defining expression = "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" DEBUG:root:Splitting expression (a*(-E_L + Min(V_m, V_peak)) - w)/tau_w (symbols [w]) DEBUG:root: linear factors: Matrix([[-1/tau_w]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w DEBUG:root:Created Shape with symbol w, derivative_factors = [-1/tau_w], inhom_term = -E_L*a/tau_w, nonlin_term = a*Min(V_m, V_peak)/tau_w INFO:root: Returning shape: Shape "w" of order 1 INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w INFO:root: Processing differential-equation form shape refr_t with defining expression = "(-1000.0) * 1.0 / 1000.0" DEBUG:root:Splitting expression -1.00000000000000 (symbols [refr_t]) DEBUG:root: linear factors: Matrix([[0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol refr_t, derivative_factors = [0], inhom_term = -1.00000000000000, nonlin_term = 0.0 INFO:root: Returning shape: Shape "refr_t" of order 1 INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 INFO:root: Processing function-of-time shape "g_inh__X__inh_spikes" with defining expression = "e*t*exp(-t/tau_syn_inh)/tau_syn_inh" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_inh__X__inh_spikes, derivative_factors = Matrix([[-1/tau_syn_inh**2], [-2/tau_syn_inh]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh INFO:root: Processing function-of-time shape "g_exc__X__exc_spikes" with defining expression = "e*t*exp(-t/tau_syn_exc)/tau_syn_exc" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_exc__X__exc_spikes, derivative_factors = Matrix([[-1/tau_syn_exc**2], [-2/tau_syn_exc]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc INFO:root:All known variables: [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes', g_exc__X__exc_spikes, g_exc__X__exc_spikes'], all parameters used in ODEs: {C_m, E_exc, a, E_L, I_stim, g_L, tau_syn_exc, tau_w, Delta_T, E_inh, V_th, V_peak, tau_syn_inh, I_e} INFO:root:No numerical value specified for parameter "I_stim" INFO:root: Processing differential-equation form shape V_m with defining expression = "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m" DEBUG:root:Splitting expression (Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T) + I_e + I_stim - g_L*(-E_L + Min(V_m, V_peak)) - 1.0*g_exc__X__exc_spikes*(-E_exc + Min(V_m, V_peak)) - 1.0*g_inh__X__inh_spikes*(-E_inh + Min(V_m, V_peak)) - w)/C_m (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m]) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m DEBUG:root:Created Shape with symbol V_m, derivative_factors = [0], inhom_term = E_L*g_L/C_m + I_e/C_m + I_stim/C_m, nonlin_term = Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Returning shape: Shape "V_m" of order 1 INFO:root: Processing differential-equation form shape w with defining expression = "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" DEBUG:root:Splitting expression (a*(-E_L + Min(V_m, V_peak)) - w)/tau_w (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, w]) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w DEBUG:root:Created Shape with symbol w, derivative_factors = [-1/tau_w], inhom_term = -E_L*a/tau_w, nonlin_term = a*Min(V_m, V_peak)/tau_w INFO:root: Returning shape: Shape "w" of order 1 INFO:root: Processing differential-equation form shape refr_t with defining expression = "(-1000.0) * 1.0 / 1000.0" DEBUG:root:Splitting expression -1.00000000000000 (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, w, refr_t]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol refr_t, derivative_factors = [0], inhom_term = -1.00000000000000, nonlin_term = 0 INFO:root: Returning shape: Shape "refr_t" of order 1 INFO:root: Processing function-of-time shape "g_inh__X__inh_spikes" with defining expression = "e*t*exp(-t/tau_syn_inh)/tau_syn_inh" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_inh__X__inh_spikes, derivative_factors = Matrix([[-1/tau_syn_inh**2], [-2/tau_syn_inh]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root: Processing function-of-time shape "g_exc__X__exc_spikes" with defining expression = "e*t*exp(-t/tau_syn_exc)/tau_syn_exc" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_exc__X__exc_spikes, derivative_factors = Matrix([[-1/tau_syn_exc**2], [-2/tau_syn_exc]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Splitting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w DEBUG:root:Splitting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 DEBUG:root:Splitting expression -1.00000000000000 (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh DEBUG:root:Splitting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh**2], [-2/tau_syn_inh], [0], [0]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc DEBUG:root:Splitting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [-1/tau_syn_exc**2], [-2/tau_syn_exc]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Initializing system of shapes with x = Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]), A = Matrix([[0, -1/C_m, 0, 1.0*E_inh/C_m, 0, 1.0*E_exc/C_m, 0], [0, -1/tau_w, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1.00000000000000, 0, 0], [0, 0, 0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 0, 0, 1.00000000000000], [0, 0, 0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]), b = Matrix([[E_L*g_L/C_m + I_e/C_m + I_stim/C_m], [-E_L*a/tau_w], [-1.00000000000000], [0], [0], [0], [0]]), c = Matrix([[Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m], [a*Min(V_m, V_peak)/tau_w], [0], [0], [0], [0], [0]]) INFO:root:Finding analytically solvable equations... INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Splitting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w DEBUG:root:Splitting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 DEBUG:root:Splitting expression -1.00000000000000 (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh DEBUG:root:Splitting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh**2], [-2/tau_syn_inh], [0], [0]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc DEBUG:root:Splitting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [-1/tau_syn_exc**2], [-2/tau_syn_exc]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Generating propagators for the following symbols: refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d DEBUG:root:Initializing system of shapes with x = Matrix([[refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]), A = Matrix([[0, 0, 0, 0, 0], [0, 0, 1.00000000000000, 0, 0], [0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 1.00000000000000], [0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]), b = Matrix([[-1.00000000000000], [0], [0], [0], [0]]), c = Matrix([[0], [0], [0], [0], [0]]) DEBUG:root:System of equations: DEBUG:root:x = Matrix([[refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]) DEBUG:root:A = Matrix([ [0, 0, 0, 0, 0], [0, 0, 1.0, 0, 0], [0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 1.0], [0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]) DEBUG:root:b = Matrix([[-1.00000000000000], [0], [0], [0], [0]]) DEBUG:root:c = Matrix([[0], [0], [0], [0], [0]]) INFO:root:update_expr[refr_t] = __P__refr_t__refr_t*refr_t - 1.0*__h INFO:root:update_expr[g_inh__X__inh_spikes] = __P__g_inh__X__inh_spikes__g_inh__X__inh_spikes*g_inh__X__inh_spikes + __P__g_inh__X__inh_spikes__g_inh__X__inh_spikes__d*g_inh__X__inh_spikes__d INFO:root:update_expr[g_inh__X__inh_spikes__d] = __P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes*g_inh__X__inh_spikes + __P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes__d*g_inh__X__inh_spikes__d INFO:root:update_expr[g_exc__X__exc_spikes] = __P__g_exc__X__exc_spikes__g_exc__X__exc_spikes*g_exc__X__exc_spikes + __P__g_exc__X__exc_spikes__g_exc__X__exc_spikes__d*g_exc__X__exc_spikes__d INFO:root:update_expr[g_exc__X__exc_spikes__d] = __P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes*g_exc__X__exc_spikes + __P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes__d*g_exc__X__exc_spikes__d INFO:root:Generating numerical solver for the following symbols: V_m, w DEBUG:root:Initializing system of shapes with x = Matrix([[V_m], [w]]), A = Matrix([[0, -1/C_m], [0, -1/tau_w]]), b = Matrix([[E_L*g_L/C_m + I_e/C_m + I_stim/C_m], [-E_L*a/tau_w]]), c = Matrix([[Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m], [a*Min(V_m, V_peak)/tau_w]]) WARNING:root:Not preserving expression for variable "refr_t" as it is solved by propagator solver INFO:root:Preserving expression for variable "V_m" INFO:root:Preserving expression for variable "w" INFO:root:In ode-toolbox: returning outdict = INFO:root:[ { "initial_values": { "g_exc__X__exc_spikes": "0", "g_exc__X__exc_spikes__d": "e/tau_syn_exc", "g_inh__X__inh_spikes": "0", "g_inh__X__inh_spikes__d": "e/tau_syn_inh", "refr_t": "0" }, "parameters": { "tau_syn_exc": "0.200000000000000", "tau_syn_inh": "2.00000000000000" }, "propagators": { "__P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes": "-1.0*__h*exp(-__h/tau_syn_exc)/tau_syn_exc**2", "__P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes__d": "1.0*(-__h + tau_syn_exc)*exp(-__h/tau_syn_exc)/tau_syn_exc", "__P__g_exc__X__exc_spikes__g_exc__X__exc_spikes": "1.0*(__h + tau_syn_exc)*exp(-__h/tau_syn_exc)/tau_syn_exc", "__P__g_exc__X__exc_spikes__g_exc__X__exc_spikes__d": "1.0*__h*exp(-__h/tau_syn_exc)", "__P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes": "-1.0*__h*exp(-__h/tau_syn_inh)/tau_syn_inh**2", "__P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes__d": "1.0*(-__h + tau_syn_inh)*exp(-__h/tau_syn_inh)/tau_syn_inh", "__P__g_inh__X__inh_spikes__g_inh__X__inh_spikes": "1.0*(__h + tau_syn_inh)*exp(-__h/tau_syn_inh)/tau_syn_inh", "__P__g_inh__X__inh_spikes__g_inh__X__inh_spikes__d": "1.0*__h*exp(-__h/tau_syn_inh)", "__P__refr_t__refr_t": "1.00000000000000" }, "solver": "analytical", "state_variables": [ "refr_t", "g_inh__X__inh_spikes", "g_inh__X__inh_spikes__d", "g_exc__X__exc_spikes", "g_exc__X__exc_spikes__d" ], "update_expressions": { "g_exc__X__exc_spikes": "__P__g_exc__X__exc_spikes__g_exc__X__exc_spikes*g_exc__X__exc_spikes + __P__g_exc__X__exc_spikes__g_exc__X__exc_spikes__d*g_exc__X__exc_spikes__d", "g_exc__X__exc_spikes__d": "__P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes*g_exc__X__exc_spikes + __P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes__d*g_exc__X__exc_spikes__d", "g_inh__X__inh_spikes": "__P__g_inh__X__inh_spikes__g_inh__X__inh_spikes*g_inh__X__inh_spikes + __P__g_inh__X__inh_spikes__g_inh__X__inh_spikes__d*g_inh__X__inh_spikes__d", "g_inh__X__inh_spikes__d": "__P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes*g_inh__X__inh_spikes + __P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes__d*g_inh__X__inh_spikes__d", "refr_t": "__P__refr_t__refr_t*refr_t - 1.0*__h" } }, { "initial_values": { "V_m": "E_L", "w": "0" }, "parameters": { "C_m": "281.000000000000", "Delta_T": "2.00000000000000", "E_L": "-70.6000000000000", "E_exc": "0", "E_inh": "-85.0000000000000", "I_e": "0", "V_peak": "0", "V_th": "-50.4000000000000", "a": "4.00000000000000", "g_L": "30.0000000000000", "tau_w": "144.000000000000" }, "solver": "numeric", "state_variables": [ "V_m", "w" ], "update_expressions": { "V_m": "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m", "w": "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" } } ] INFO:root:Analysing input: INFO:root:{ "dynamics": [ { "expression": "V_m' = ((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m", "initial_values": { "V_m": "E_L" } }, { "expression": "w' = (a * ((min(V_m, V_peak)) - E_L) - w) / tau_w", "initial_values": { "w": "0" } }, { "expression": "refr_t' = (-1000.0) * 1.0 / 1000.0", "initial_values": { "refr_t": "0" } }, { "expression": "g_inh__X__inh_spikes = (e / tau_syn_inh) * t * exp(-t / tau_syn_inh)", "initial_values": {} }, { "expression": "g_exc__X__exc_spikes = (e / tau_syn_exc) * t * exp(-t / tau_syn_exc)", "initial_values": {} } ], "options": { "output_timestep_symbol": "__h" }, "parameters": { "C_m": "281.0", "Delta_T": "2.0", "E_L": "(-70.6)", "E_exc": "0", "E_inh": "(-85.0)", "I_e": "0", "V_peak": "0", "V_reset": "(-60.0)", "V_th": "(-50.4)", "a": "4", "b": "80.5", "g_L": "30.0", "refr_T": "2", "tau_syn_exc": "0.2", "tau_syn_inh": "2.0", "tau_w": "144.0" } } INFO:root:Processing global options... INFO:root:Processing input shapes... INFO:root: Processing differential-equation form shape V_m with defining expression = "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m" DEBUG:root:Splitting expression (Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T) + I_e + I_stim - g_L*(-E_L + Min(V_m, V_peak)) - 1.0*g_exc__X__exc_spikes*(-E_exc + Min(V_m, V_peak)) - 1.0*g_inh__X__inh_spikes*(-E_inh + Min(V_m, V_peak)) - w)/C_m (symbols [V_m]) DEBUG:root: linear factors: Matrix([[0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Created Shape with symbol V_m, derivative_factors = [0], inhom_term = E_L*g_L/C_m + I_e/C_m, nonlin_term = Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Returning shape: Shape "V_m" of order 1 INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Processing differential-equation form shape w with defining expression = "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" DEBUG:root:Splitting expression (a*(-E_L + Min(V_m, V_peak)) - w)/tau_w (symbols [w]) DEBUG:root: linear factors: Matrix([[-1/tau_w]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w DEBUG:root:Created Shape with symbol w, derivative_factors = [-1/tau_w], inhom_term = -E_L*a/tau_w, nonlin_term = a*Min(V_m, V_peak)/tau_w INFO:root: Returning shape: Shape "w" of order 1 INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w INFO:root: Processing differential-equation form shape refr_t with defining expression = "(-1000.0) * 1.0 / 1000.0" DEBUG:root:Splitting expression -1.00000000000000 (symbols [refr_t]) DEBUG:root: linear factors: Matrix([[0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol refr_t, derivative_factors = [0], inhom_term = -1.00000000000000, nonlin_term = 0.0 INFO:root: Returning shape: Shape "refr_t" of order 1 INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 INFO:root: Processing function-of-time shape "g_inh__X__inh_spikes" with defining expression = "e*t*exp(-t/tau_syn_inh)/tau_syn_inh" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_inh__X__inh_spikes, derivative_factors = Matrix([[-1/tau_syn_inh**2], [-2/tau_syn_inh]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh INFO:root: Processing function-of-time shape "g_exc__X__exc_spikes" with defining expression = "e*t*exp(-t/tau_syn_exc)/tau_syn_exc" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_exc__X__exc_spikes, derivative_factors = Matrix([[-1/tau_syn_exc**2], [-2/tau_syn_exc]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc INFO:root:All known variables: [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes', g_exc__X__exc_spikes, g_exc__X__exc_spikes'], all parameters used in ODEs: {C_m, E_exc, a, E_L, I_stim, g_L, tau_syn_exc, tau_w, Delta_T, E_inh, V_th, V_peak, tau_syn_inh, I_e} INFO:root:No numerical value specified for parameter "I_stim" INFO:root: Processing differential-equation form shape V_m with defining expression = "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m" DEBUG:root:Splitting expression (Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T) + I_e + I_stim - g_L*(-E_L + Min(V_m, V_peak)) - 1.0*g_exc__X__exc_spikes*(-E_exc + Min(V_m, V_peak)) - 1.0*g_inh__X__inh_spikes*(-E_inh + Min(V_m, V_peak)) - w)/C_m (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m]) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m DEBUG:root:Created Shape with symbol V_m, derivative_factors = [0], inhom_term = E_L*g_L/C_m + I_e/C_m + I_stim/C_m, nonlin_term = Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Returning shape: Shape "V_m" of order 1 INFO:root: Processing differential-equation form shape w with defining expression = "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" DEBUG:root:Splitting expression (a*(-E_L + Min(V_m, V_peak)) - w)/tau_w (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, w]) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w DEBUG:root:Created Shape with symbol w, derivative_factors = [-1/tau_w], inhom_term = -E_L*a/tau_w, nonlin_term = a*Min(V_m, V_peak)/tau_w INFO:root: Returning shape: Shape "w" of order 1 INFO:root: Processing differential-equation form shape refr_t with defining expression = "(-1000.0) * 1.0 / 1000.0" DEBUG:root:Splitting expression -1.00000000000000 (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, w, refr_t]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol refr_t, derivative_factors = [0], inhom_term = -1.00000000000000, nonlin_term = 0 INFO:root: Returning shape: Shape "refr_t" of order 1 INFO:root: Processing function-of-time shape "g_inh__X__inh_spikes" with defining expression = "e*t*exp(-t/tau_syn_inh)/tau_syn_inh" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_inh__X__inh_spikes, derivative_factors = Matrix([[-1/tau_syn_inh**2], [-2/tau_syn_inh]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root: Processing function-of-time shape "g_exc__X__exc_spikes" with defining expression = "e*t*exp(-t/tau_syn_exc)/tau_syn_exc" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_exc__X__exc_spikes, derivative_factors = Matrix([[-1/tau_syn_exc**2], [-2/tau_syn_exc]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Splitting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w DEBUG:root:Splitting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 DEBUG:root:Splitting expression -1.00000000000000 (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh DEBUG:root:Splitting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh**2], [-2/tau_syn_inh], [0], [0]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc DEBUG:root:Splitting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [-1/tau_syn_exc**2], [-2/tau_syn_exc]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Initializing system of shapes with x = Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]), A = Matrix([[0, -1/C_m, 0, 1.0*E_inh/C_m, 0, 1.0*E_exc/C_m, 0], [0, -1/tau_w, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1.00000000000000, 0, 0], [0, 0, 0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 0, 0, 1.00000000000000], [0, 0, 0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]), b = Matrix([[E_L*g_L/C_m + I_e/C_m + I_stim/C_m], [-E_L*a/tau_w], [-1.00000000000000], [0], [0], [0], [0]]), c = Matrix([[Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m], [a*Min(V_m, V_peak)/tau_w], [0], [0], [0], [0], [0]]) INFO:root:Finding analytically solvable equations... INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Splitting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w DEBUG:root:Splitting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 DEBUG:root:Splitting expression -1.00000000000000 (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh DEBUG:root:Splitting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh**2], [-2/tau_syn_inh], [0], [0]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc DEBUG:root:Splitting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [-1/tau_syn_exc**2], [-2/tau_syn_exc]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Generating numerical solver for the following symbols: w, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, refr_t, g_inh__X__inh_spikes DEBUG:root:Initializing system of shapes with x = Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]), A = Matrix([[0, -1/C_m, 0, 1.0*E_inh/C_m, 0, 1.0*E_exc/C_m, 0], [0, -1/tau_w, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1.00000000000000, 0, 0], [0, 0, 0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 0, 0, 1.00000000000000], [0, 0, 0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]), b = Matrix([[E_L*g_L/C_m + I_e/C_m + I_stim/C_m], [-E_L*a/tau_w], [-1.00000000000000], [0], [0], [0], [0]]), c = Matrix([[Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m], [a*Min(V_m, V_peak)/tau_w], [0], [0], [0], [0], [0]]) INFO:root:Preserving expression for variable "V_m" INFO:root:Preserving expression for variable "w" INFO:root:Preserving expression for variable "refr_t" INFO:root:In ode-toolbox: returning outdict = INFO:root:[ { "initial_values": { "V_m": "E_L", "g_exc__X__exc_spikes": "0", "g_exc__X__exc_spikes__d": "e/tau_syn_exc", "g_inh__X__inh_spikes": "0", "g_inh__X__inh_spikes__d": "e/tau_syn_inh", "refr_t": "0", "w": "0" }, "parameters": { "C_m": "281.000000000000", "Delta_T": "2.00000000000000", "E_L": "-70.6000000000000", "E_exc": "0", "E_inh": "-85.0000000000000", "I_e": "0", "V_peak": "0", "V_th": "-50.4000000000000", "a": "4.00000000000000", "g_L": "30.0000000000000", "tau_syn_exc": "0.200000000000000", "tau_syn_inh": "2.00000000000000", "tau_w": "144.000000000000" }, "solver": "numeric", "state_variables": [ "V_m", "w", "refr_t", "g_inh__X__inh_spikes", "g_inh__X__inh_spikes__d", "g_exc__X__exc_spikes", "g_exc__X__exc_spikes__d" ], "update_expressions": { "V_m": "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m", "g_exc__X__exc_spikes": "1.0*g_exc__X__exc_spikes__d", "g_exc__X__exc_spikes__d": "-g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc", "g_inh__X__inh_spikes": "1.0*g_inh__X__inh_spikes__d", "g_inh__X__inh_spikes__d": "-g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh", "refr_t": "(-1000.0) * 1.0 / 1000.0", "w": "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" } } ] [43,aeif_cond_alpha_neuron_nestml, WARNING, [83:8;83:17]]: Variable 'a' has the same name as a physical unit! [44,aeif_cond_alpha_neuron_nestml, INFO, [65:8;67:8]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [45,aeif_cond_alpha_neuron_nestml, INFO, [65:8;67:8]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [46,aeif_cond_alpha_neuron_nestml, INFO, [65:8;67:8]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [47,GLOBAL, INFO]: Analysing/transforming model 'aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml' [48,aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml, INFO, [49:0;128:0]]: Starts processing of the model 'aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml' INFO:root:Analysing input: INFO:root:{ "dynamics": [ { "expression": "V_m' = ((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m", "initial_values": { "V_m": "E_L" } }, { "expression": "w' = (a * ((min(V_m, V_peak)) - E_L) - w) / tau_w", "initial_values": { "w": "0" } }, { "expression": "refr_t' = (-1000.0) * 1.0 / 1000.0", "initial_values": { "refr_t": "0" } }, { "expression": "g_inh__X__inh_spikes = (e / tau_syn_inh) * t * exp(-t / tau_syn_inh)", "initial_values": {} }, { "expression": "g_exc__X__exc_spikes = (e / tau_syn_exc) * t * exp(-t / tau_syn_exc)", "initial_values": {} } ], "options": { "output_timestep_symbol": "__h" }, "parameters": { "C_m": "281.0", "Delta_T": "2.0", "E_L": "(-70.6)", "E_exc": "0", "E_inh": "(-85.0)", "I_e": "0", "V_peak": "0", "V_reset": "(-60.0)", "V_th": "(-50.4)", "a": "4", "b": "80.5", "g_L": "30.0", "refr_T": "2", "tau_syn_exc": "0.2", "tau_syn_inh": "2.0", "tau_w": "144.0" } } INFO:root:Processing global options... INFO:root:Processing input shapes... INFO:root: Processing differential-equation form shape V_m with defining expression = "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m" DEBUG:root:Splitting expression (Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T) + I_e + I_stim - g_L*(-E_L + Min(V_m, V_peak)) - 1.0*g_exc__X__exc_spikes*(-E_exc + Min(V_m, V_peak)) - 1.0*g_inh__X__inh_spikes*(-E_inh + Min(V_m, V_peak)) - w)/C_m (symbols [V_m]) DEBUG:root: linear factors: Matrix([[0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Created Shape with symbol V_m, derivative_factors = [0], inhom_term = E_L*g_L/C_m + I_e/C_m, nonlin_term = Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Returning shape: Shape "V_m" of order 1 INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Processing differential-equation form shape w with defining expression = "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" DEBUG:root:Splitting expression (a*(-E_L + Min(V_m, V_peak)) - w)/tau_w (symbols [w]) DEBUG:root: linear factors: Matrix([[-1/tau_w]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w DEBUG:root:Created Shape with symbol w, derivative_factors = [-1/tau_w], inhom_term = -E_L*a/tau_w, nonlin_term = a*Min(V_m, V_peak)/tau_w INFO:root: Returning shape: Shape "w" of order 1 INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w INFO:root: Processing differential-equation form shape refr_t with defining expression = "(-1000.0) * 1.0 / 1000.0" DEBUG:root:Splitting expression -1.00000000000000 (symbols [refr_t]) DEBUG:root: linear factors: Matrix([[0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol refr_t, derivative_factors = [0], inhom_term = -1.00000000000000, nonlin_term = 0.0 INFO:root: Returning shape: Shape "refr_t" of order 1 INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 INFO:root: Processing function-of-time shape "g_inh__X__inh_spikes" with defining expression = "e*t*exp(-t/tau_syn_inh)/tau_syn_inh" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_inh__X__inh_spikes, derivative_factors = Matrix([[-1/tau_syn_inh**2], [-2/tau_syn_inh]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh INFO:root: Processing function-of-time shape "g_exc__X__exc_spikes" with defining expression = "e*t*exp(-t/tau_syn_exc)/tau_syn_exc" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_exc__X__exc_spikes, derivative_factors = Matrix([[-1/tau_syn_exc**2], [-2/tau_syn_exc]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc INFO:root:All known variables: [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes', g_exc__X__exc_spikes, g_exc__X__exc_spikes'], all parameters used in ODEs: {C_m, E_exc, a, E_L, I_stim, g_L, tau_syn_exc, tau_w, Delta_T, E_inh, V_th, V_peak, tau_syn_inh, I_e} INFO:root:No numerical value specified for parameter "I_stim" INFO:root: Processing differential-equation form shape V_m with defining expression = "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m" DEBUG:root:Splitting expression (Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T) + I_e + I_stim - g_L*(-E_L + Min(V_m, V_peak)) - 1.0*g_exc__X__exc_spikes*(-E_exc + Min(V_m, V_peak)) - 1.0*g_inh__X__inh_spikes*(-E_inh + Min(V_m, V_peak)) - w)/C_m (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m]) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m DEBUG:root:Created Shape with symbol V_m, derivative_factors = [0], inhom_term = E_L*g_L/C_m + I_e/C_m + I_stim/C_m, nonlin_term = Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Returning shape: Shape "V_m" of order 1 INFO:root: Processing differential-equation form shape w with defining expression = "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" DEBUG:root:Splitting expression (a*(-E_L + Min(V_m, V_peak)) - w)/tau_w (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, w]) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w DEBUG:root:Created Shape with symbol w, derivative_factors = [-1/tau_w], inhom_term = -E_L*a/tau_w, nonlin_term = a*Min(V_m, V_peak)/tau_w INFO:root: Returning shape: Shape "w" of order 1 INFO:root: Processing differential-equation form shape refr_t with defining expression = "(-1000.0) * 1.0 / 1000.0" DEBUG:root:Splitting expression -1.00000000000000 (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, w, refr_t]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol refr_t, derivative_factors = [0], inhom_term = -1.00000000000000, nonlin_term = 0 INFO:root: Returning shape: Shape "refr_t" of order 1 INFO:root: Processing function-of-time shape "g_inh__X__inh_spikes" with defining expression = "e*t*exp(-t/tau_syn_inh)/tau_syn_inh" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_inh__X__inh_spikes, derivative_factors = Matrix([[-1/tau_syn_inh**2], [-2/tau_syn_inh]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root: Processing function-of-time shape "g_exc__X__exc_spikes" with defining expression = "e*t*exp(-t/tau_syn_exc)/tau_syn_exc" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_exc__X__exc_spikes, derivative_factors = Matrix([[-1/tau_syn_exc**2], [-2/tau_syn_exc]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Splitting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w DEBUG:root:Splitting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 DEBUG:root:Splitting expression -1.00000000000000 (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh DEBUG:root:Splitting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh**2], [-2/tau_syn_inh], [0], [0]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc DEBUG:root:Splitting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [-1/tau_syn_exc**2], [-2/tau_syn_exc]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Initializing system of shapes with x = Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]), A = Matrix([[0, -1/C_m, 0, 1.0*E_inh/C_m, 0, 1.0*E_exc/C_m, 0], [0, -1/tau_w, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1.00000000000000, 0, 0], [0, 0, 0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 0, 0, 1.00000000000000], [0, 0, 0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]), b = Matrix([[E_L*g_L/C_m + I_e/C_m + I_stim/C_m], [-E_L*a/tau_w], [-1.00000000000000], [0], [0], [0], [0]]), c = Matrix([[Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m], [a*Min(V_m, V_peak)/tau_w], [0], [0], [0], [0], [0]]) INFO:root:Finding analytically solvable equations... INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Splitting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w DEBUG:root:Splitting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 DEBUG:root:Splitting expression -1.00000000000000 (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh DEBUG:root:Splitting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh**2], [-2/tau_syn_inh], [0], [0]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc DEBUG:root:Splitting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [-1/tau_syn_exc**2], [-2/tau_syn_exc]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Generating propagators for the following symbols: refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d DEBUG:root:Initializing system of shapes with x = Matrix([[refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]), A = Matrix([[0, 0, 0, 0, 0], [0, 0, 1.00000000000000, 0, 0], [0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 1.00000000000000], [0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]), b = Matrix([[-1.00000000000000], [0], [0], [0], [0]]), c = Matrix([[0], [0], [0], [0], [0]]) DEBUG:root:System of equations: DEBUG:root:x = Matrix([[refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]) DEBUG:root:A = Matrix([ [0, 0, 0, 0, 0], [0, 0, 1.0, 0, 0], [0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 1.0], [0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]) DEBUG:root:b = Matrix([[-1.00000000000000], [0], [0], [0], [0]]) DEBUG:root:c = Matrix([[0], [0], [0], [0], [0]]) INFO:root:update_expr[refr_t] = __P__refr_t__refr_t*refr_t - 1.0*__h INFO:root:update_expr[g_inh__X__inh_spikes] = __P__g_inh__X__inh_spikes__g_inh__X__inh_spikes*g_inh__X__inh_spikes + __P__g_inh__X__inh_spikes__g_inh__X__inh_spikes__d*g_inh__X__inh_spikes__d INFO:root:update_expr[g_inh__X__inh_spikes__d] = __P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes*g_inh__X__inh_spikes + __P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes__d*g_inh__X__inh_spikes__d INFO:root:update_expr[g_exc__X__exc_spikes] = __P__g_exc__X__exc_spikes__g_exc__X__exc_spikes*g_exc__X__exc_spikes + __P__g_exc__X__exc_spikes__g_exc__X__exc_spikes__d*g_exc__X__exc_spikes__d INFO:root:update_expr[g_exc__X__exc_spikes__d] = __P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes*g_exc__X__exc_spikes + __P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes__d*g_exc__X__exc_spikes__d INFO:root:Generating numerical solver for the following symbols: V_m, w DEBUG:root:Initializing system of shapes with x = Matrix([[V_m], [w]]), A = Matrix([[0, -1/C_m], [0, -1/tau_w]]), b = Matrix([[E_L*g_L/C_m + I_e/C_m + I_stim/C_m], [-E_L*a/tau_w]]), c = Matrix([[Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m], [a*Min(V_m, V_peak)/tau_w]]) WARNING:root:Not preserving expression for variable "refr_t" as it is solved by propagator solver INFO:root:Preserving expression for variable "V_m" INFO:root:Preserving expression for variable "w" INFO:root:In ode-toolbox: returning outdict = INFO:root:[ { "initial_values": { "g_exc__X__exc_spikes": "0", "g_exc__X__exc_spikes__d": "e/tau_syn_exc", "g_inh__X__inh_spikes": "0", "g_inh__X__inh_spikes__d": "e/tau_syn_inh", "refr_t": "0" }, "parameters": { "tau_syn_exc": "0.200000000000000", "tau_syn_inh": "2.00000000000000" }, "propagators": { "__P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes": "-1.0*__h*exp(-__h/tau_syn_exc)/tau_syn_exc**2", "__P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes__d": "1.0*(-__h + tau_syn_exc)*exp(-__h/tau_syn_exc)/tau_syn_exc", "__P__g_exc__X__exc_spikes__g_exc__X__exc_spikes": "1.0*(__h + tau_syn_exc)*exp(-__h/tau_syn_exc)/tau_syn_exc", "__P__g_exc__X__exc_spikes__g_exc__X__exc_spikes__d": "1.0*__h*exp(-__h/tau_syn_exc)", "__P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes": "-1.0*__h*exp(-__h/tau_syn_inh)/tau_syn_inh**2", "__P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes__d": "1.0*(-__h + tau_syn_inh)*exp(-__h/tau_syn_inh)/tau_syn_inh", "__P__g_inh__X__inh_spikes__g_inh__X__inh_spikes": "1.0*(__h + tau_syn_inh)*exp(-__h/tau_syn_inh)/tau_syn_inh", "__P__g_inh__X__inh_spikes__g_inh__X__inh_spikes__d": "1.0*__h*exp(-__h/tau_syn_inh)", "__P__refr_t__refr_t": "1.00000000000000" }, "solver": "analytical", "state_variables": [ "refr_t", "g_inh__X__inh_spikes", "g_inh__X__inh_spikes__d", "g_exc__X__exc_spikes", "g_exc__X__exc_spikes__d" ], "update_expressions": { "g_exc__X__exc_spikes": "__P__g_exc__X__exc_spikes__g_exc__X__exc_spikes*g_exc__X__exc_spikes + __P__g_exc__X__exc_spikes__g_exc__X__exc_spikes__d*g_exc__X__exc_spikes__d", "g_exc__X__exc_spikes__d": "__P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes*g_exc__X__exc_spikes + __P__g_exc__X__exc_spikes__d__g_exc__X__exc_spikes__d*g_exc__X__exc_spikes__d", "g_inh__X__inh_spikes": "__P__g_inh__X__inh_spikes__g_inh__X__inh_spikes*g_inh__X__inh_spikes + __P__g_inh__X__inh_spikes__g_inh__X__inh_spikes__d*g_inh__X__inh_spikes__d", "g_inh__X__inh_spikes__d": "__P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes*g_inh__X__inh_spikes + __P__g_inh__X__inh_spikes__d__g_inh__X__inh_spikes__d*g_inh__X__inh_spikes__d", "refr_t": "__P__refr_t__refr_t*refr_t - 1.0*__h" } }, { "initial_values": { "V_m": "E_L", "w": "0" }, "parameters": { "C_m": "281.000000000000", "Delta_T": "2.00000000000000", "E_L": "-70.6000000000000", "E_exc": "0", "E_inh": "-85.0000000000000", "I_e": "0", "V_peak": "0", "V_th": "-50.4000000000000", "a": "4.00000000000000", "g_L": "30.0000000000000", "tau_w": "144.000000000000" }, "solver": "numeric", "state_variables": [ "V_m", "w" ], "update_expressions": { "V_m": "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m", "w": "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" } } ] INFO:root:Analysing input: INFO:root:{ "dynamics": [ { "expression": "V_m' = ((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m", "initial_values": { "V_m": "E_L" } }, { "expression": "w' = (a * ((min(V_m, V_peak)) - E_L) - w) / tau_w", "initial_values": { "w": "0" } }, { "expression": "refr_t' = (-1000.0) * 1.0 / 1000.0", "initial_values": { "refr_t": "0" } }, { "expression": "g_inh__X__inh_spikes = (e / tau_syn_inh) * t * exp(-t / tau_syn_inh)", "initial_values": {} }, { "expression": "g_exc__X__exc_spikes = (e / tau_syn_exc) * t * exp(-t / tau_syn_exc)", "initial_values": {} } ], "options": { "output_timestep_symbol": "__h" }, "parameters": { "C_m": "281.0", "Delta_T": "2.0", "E_L": "(-70.6)", "E_exc": "0", "E_inh": "(-85.0)", "I_e": "0", "V_peak": "0", "V_reset": "(-60.0)", "V_th": "(-50.4)", "a": "4", "b": "80.5", "g_L": "30.0", "refr_T": "2", "tau_syn_exc": "0.2", "tau_syn_inh": "2.0", "tau_w": "144.0" } } INFO:root:Processing global options... INFO:root:Processing input shapes... INFO:root: Processing differential-equation form shape V_m with defining expression = "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m" DEBUG:root:Splitting expression (Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T) + I_e + I_stim - g_L*(-E_L + Min(V_m, V_peak)) - 1.0*g_exc__X__exc_spikes*(-E_exc + Min(V_m, V_peak)) - 1.0*g_inh__X__inh_spikes*(-E_inh + Min(V_m, V_peak)) - w)/C_m (symbols [V_m]) DEBUG:root: linear factors: Matrix([[0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Created Shape with symbol V_m, derivative_factors = [0], inhom_term = E_L*g_L/C_m + I_e/C_m, nonlin_term = Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Returning shape: Shape "V_m" of order 1 INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Processing differential-equation form shape w with defining expression = "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" DEBUG:root:Splitting expression (a*(-E_L + Min(V_m, V_peak)) - w)/tau_w (symbols [w]) DEBUG:root: linear factors: Matrix([[-1/tau_w]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w DEBUG:root:Created Shape with symbol w, derivative_factors = [-1/tau_w], inhom_term = -E_L*a/tau_w, nonlin_term = a*Min(V_m, V_peak)/tau_w INFO:root: Returning shape: Shape "w" of order 1 INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w INFO:root: Processing differential-equation form shape refr_t with defining expression = "(-1000.0) * 1.0 / 1000.0" DEBUG:root:Splitting expression -1.00000000000000 (symbols [refr_t]) DEBUG:root: linear factors: Matrix([[0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol refr_t, derivative_factors = [0], inhom_term = -1.00000000000000, nonlin_term = 0.0 INFO:root: Returning shape: Shape "refr_t" of order 1 INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 INFO:root: Processing function-of-time shape "g_inh__X__inh_spikes" with defining expression = "e*t*exp(-t/tau_syn_inh)/tau_syn_inh" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_inh__X__inh_spikes, derivative_factors = Matrix([[-1/tau_syn_inh**2], [-2/tau_syn_inh]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh INFO:root: Processing function-of-time shape "g_exc__X__exc_spikes" with defining expression = "e*t*exp(-t/tau_syn_exc)/tau_syn_exc" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_exc__X__exc_spikes, derivative_factors = Matrix([[-1/tau_syn_exc**2], [-2/tau_syn_exc]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc INFO:root:All known variables: [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes', g_exc__X__exc_spikes, g_exc__X__exc_spikes'], all parameters used in ODEs: {C_m, E_exc, a, E_L, I_stim, g_L, tau_syn_exc, tau_w, Delta_T, E_inh, V_th, V_peak, tau_syn_inh, I_e} INFO:root:No numerical value specified for parameter "I_stim" INFO:root: Processing differential-equation form shape V_m with defining expression = "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m" DEBUG:root:Splitting expression (Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T) + I_e + I_stim - g_L*(-E_L + Min(V_m, V_peak)) - 1.0*g_exc__X__exc_spikes*(-E_exc + Min(V_m, V_peak)) - 1.0*g_inh__X__inh_spikes*(-E_inh + Min(V_m, V_peak)) - w)/C_m (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m]) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m DEBUG:root:Created Shape with symbol V_m, derivative_factors = [0], inhom_term = E_L*g_L/C_m + I_e/C_m + I_stim/C_m, nonlin_term = Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m INFO:root: Returning shape: Shape "V_m" of order 1 INFO:root: Processing differential-equation form shape w with defining expression = "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" DEBUG:root:Splitting expression (a*(-E_L + Min(V_m, V_peak)) - w)/tau_w (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, w]) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w DEBUG:root:Created Shape with symbol w, derivative_factors = [-1/tau_w], inhom_term = -E_L*a/tau_w, nonlin_term = a*Min(V_m, V_peak)/tau_w INFO:root: Returning shape: Shape "w" of order 1 INFO:root: Processing differential-equation form shape refr_t with defining expression = "(-1000.0) * 1.0 / 1000.0" DEBUG:root:Splitting expression -1.00000000000000 (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, w, refr_t]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol refr_t, derivative_factors = [0], inhom_term = -1.00000000000000, nonlin_term = 0 INFO:root: Returning shape: Shape "refr_t" of order 1 INFO:root: Processing function-of-time shape "g_inh__X__inh_spikes" with defining expression = "e*t*exp(-t/tau_syn_inh)/tau_syn_inh" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_inh__X__inh_spikes, derivative_factors = Matrix([[-1/tau_syn_inh**2], [-2/tau_syn_inh]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root: Processing function-of-time shape "g_exc__X__exc_spikes" with defining expression = "e*t*exp(-t/tau_syn_exc)/tau_syn_exc" DEBUG:root:Found t: 1 DEBUG:root: Finding ode for order 1... DEBUG:root: Finding ode for order 2... DEBUG:root: checking whether shape definition is satisfied... DEBUG:root:Shape satisfies ODE of order = 2 DEBUG:root:Created Shape with symbol g_exc__X__exc_spikes, derivative_factors = Matrix([[-1/tau_syn_exc**2], [-2/tau_syn_exc]]), inhom_term = 0.0, nonlin_term = 0.0 INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Splitting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w DEBUG:root:Splitting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 DEBUG:root:Splitting expression -1.00000000000000 (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh DEBUG:root:Splitting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh**2], [-2/tau_syn_inh], [0], [0]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc DEBUG:root:Splitting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc (symbols Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]])) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [-1/tau_syn_exc**2], [-2/tau_syn_exc]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Initializing system of shapes with x = Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]), A = Matrix([[0, -1/C_m, 0, 1.0*E_inh/C_m, 0, 1.0*E_exc/C_m, 0], [0, -1/tau_w, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1.00000000000000, 0, 0], [0, 0, 0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 0, 0, 1.00000000000000], [0, 0, 0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]), b = Matrix([[E_L*g_L/C_m + I_e/C_m + I_stim/C_m], [-E_L*a/tau_w], [-1.00000000000000], [0], [0], [0], [0]]), c = Matrix([[Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m], [a*Min(V_m, V_peak)/tau_w], [0], [0], [0], [0], [0]]) INFO:root:Finding analytically solvable equations... INFO:root:Shape V_m: reconstituting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m DEBUG:root:Splitting expression Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m + E_L*g_L/C_m + 1.0*E_exc*g_exc__X__exc_spikes/C_m + 1.0*E_inh*g_inh__X__inh_spikes/C_m + I_e/C_m + I_stim/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m - w/C_m (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [-1/C_m], [0], [1.0*E_inh/C_m], [0], [1.0*E_exc/C_m], [0]]) DEBUG:root: inhomogeneous term: E_L*g_L/C_m + I_e/C_m + I_stim/C_m DEBUG:root: nonlinear term: Delta_T*g_L*exp(-V_th/Delta_T)*exp(Min(V_m, V_peak)/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m INFO:root:Shape w: reconstituting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w DEBUG:root:Splitting expression -E_L*a/tau_w + a*Min(V_m, V_peak)/tau_w - w/tau_w (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [-1/tau_w], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -E_L*a/tau_w DEBUG:root: nonlinear term: a*Min(V_m, V_peak)/tau_w INFO:root:Shape refr_t: reconstituting expression -1.00000000000000 DEBUG:root:Splitting expression -1.00000000000000 (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [0], [0]]) DEBUG:root: inhomogeneous term: -1.00000000000000 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_inh__X__inh_spikes: reconstituting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh DEBUG:root:Splitting expression -g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [-1/tau_syn_inh**2], [-2/tau_syn_inh], [0], [0]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Shape g_exc__X__exc_spikes: reconstituting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc DEBUG:root:Splitting expression -g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc (symbols [V_m, w, refr_t, g_inh__X__inh_spikes, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d]) DEBUG:root: linear factors: Matrix([[0], [0], [0], [0], [0], [-1/tau_syn_exc**2], [-2/tau_syn_exc]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Generating numerical solver for the following symbols: w, g_inh__X__inh_spikes__d, g_exc__X__exc_spikes, g_exc__X__exc_spikes__d, V_m, refr_t, g_inh__X__inh_spikes DEBUG:root:Initializing system of shapes with x = Matrix([[V_m], [w], [refr_t], [g_inh__X__inh_spikes], [g_inh__X__inh_spikes__d], [g_exc__X__exc_spikes], [g_exc__X__exc_spikes__d]]), A = Matrix([[0, -1/C_m, 0, 1.0*E_inh/C_m, 0, 1.0*E_exc/C_m, 0], [0, -1/tau_w, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1.00000000000000, 0, 0], [0, 0, 0, -1/tau_syn_inh**2, -2/tau_syn_inh, 0, 0], [0, 0, 0, 0, 0, 0, 1.00000000000000], [0, 0, 0, 0, 0, -1/tau_syn_exc**2, -2/tau_syn_exc]]), b = Matrix([[E_L*g_L/C_m + I_e/C_m + I_stim/C_m], [-E_L*a/tau_w], [-1.00000000000000], [0], [0], [0], [0]]), c = Matrix([[Delta_T*g_L*exp((-V_th + Min(V_m, V_peak))/Delta_T)/C_m - g_L*Min(V_m, V_peak)/C_m - 1.0*g_exc__X__exc_spikes*Min(V_m, V_peak)/C_m - 1.0*g_inh__X__inh_spikes*Min(V_m, V_peak)/C_m], [a*Min(V_m, V_peak)/tau_w], [0], [0], [0], [0], [0]]) INFO:root:Preserving expression for variable "V_m" INFO:root:Preserving expression for variable "w" INFO:root:Preserving expression for variable "refr_t" INFO:root:In ode-toolbox: returning outdict = INFO:root:[ { "initial_values": { "V_m": "E_L", "g_exc__X__exc_spikes": "0", "g_exc__X__exc_spikes__d": "e/tau_syn_exc", "g_inh__X__inh_spikes": "0", "g_inh__X__inh_spikes__d": "e/tau_syn_inh", "refr_t": "0", "w": "0" }, "parameters": { "C_m": "281.000000000000", "Delta_T": "2.00000000000000", "E_L": "-70.6000000000000", "E_exc": "0", "E_inh": "-85.0000000000000", "I_e": "0", "V_peak": "0", "V_th": "-50.4000000000000", "a": "4.00000000000000", "g_L": "30.0000000000000", "tau_syn_exc": "0.200000000000000", "tau_syn_inh": "2.00000000000000", "tau_w": "144.000000000000" }, "solver": "numeric", "state_variables": [ "V_m", "w", "refr_t", "g_inh__X__inh_spikes", "g_inh__X__inh_spikes__d", "g_exc__X__exc_spikes", "g_exc__X__exc_spikes__d" ], "update_expressions": { "V_m": "((-g_L) * ((min(V_m, V_peak)) - E_L) + (g_L * Delta_T * exp((((min(V_m, V_peak)) - V_th) / Delta_T))) - (g_exc__X__exc_spikes * 1.0 * ((min(V_m, V_peak)) - E_exc)) - (g_inh__X__inh_spikes * 1.0 * ((min(V_m, V_peak)) - E_inh)) - w + I_e + I_stim) / C_m", "g_exc__X__exc_spikes": "1.0*g_exc__X__exc_spikes__d", "g_exc__X__exc_spikes__d": "-g_exc__X__exc_spikes/tau_syn_exc**2 - 2*g_exc__X__exc_spikes__d/tau_syn_exc", "g_inh__X__inh_spikes": "1.0*g_inh__X__inh_spikes__d", "g_inh__X__inh_spikes__d": "-g_inh__X__inh_spikes/tau_syn_inh**2 - 2*g_inh__X__inh_spikes__d/tau_syn_inh", "refr_t": "(-1000.0) * 1.0 / 1000.0", "w": "(a * ((min(V_m, V_peak)) - E_L) - w) / tau_w" } } ] [49,aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml, WARNING, [83:8;83:17]]: Variable 'a' has the same name as a physical unit! [50,aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml, INFO, [65:8;67:8]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [51,aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml, INFO, [65:8;67:8]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [52,aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml, INFO, [65:8;67:8]]: Implicit casting from (compatible) type 'mV nS' to 'pA'. [53,GLOBAL, INFO]: Analysing/transforming synapse dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml. [54,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, INFO, [19:0;59:0]]: Starts processing of the model 'dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml' INFO:root:Analysing input: INFO:root:{ "dynamics": [ { "expression": "dopa_rate' = (-dopa_rate) / tau_dopa", "initial_values": { "dopa_rate": "0.0" } } ], "options": { "output_timestep_symbol": "__h" }, "parameters": { "Wmax": "200.0", "Wmin": "(-200.0)", "alpha": "0.2", "b": "0.0", "d": "1", "dopa_thresh": "10", "receptor": "1", "tau_dopa": "100", "w_0": "1.0" } } INFO:root:Processing global options... INFO:root:Processing input shapes... INFO:root: Processing differential-equation form shape dopa_rate with defining expression = "(-dopa_rate) / tau_dopa" DEBUG:root:Splitting expression -dopa_rate/tau_dopa (symbols [dopa_rate]) DEBUG:root: linear factors: Matrix([[-1/tau_dopa]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol dopa_rate, derivative_factors = [-1/tau_dopa], inhom_term = 0.0, nonlin_term = 0.0 INFO:root: Returning shape: Shape "dopa_rate" of order 1 INFO:root:Shape dopa_rate: reconstituting expression -dopa_rate/tau_dopa INFO:root:All known variables: [dopa_rate], all parameters used in ODEs: {tau_dopa} INFO:root: Processing differential-equation form shape dopa_rate with defining expression = "(-dopa_rate) / tau_dopa" DEBUG:root:Splitting expression -dopa_rate/tau_dopa (symbols [dopa_rate, dopa_rate]) DEBUG:root: linear factors: Matrix([[-1/tau_dopa], [0]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Created Shape with symbol dopa_rate, derivative_factors = [-1/tau_dopa], inhom_term = 0.0, nonlin_term = 0 INFO:root: Returning shape: Shape "dopa_rate" of order 1 INFO:root:Shape dopa_rate: reconstituting expression -dopa_rate/tau_dopa DEBUG:root:Splitting expression -dopa_rate/tau_dopa (symbols Matrix([[dopa_rate]])) DEBUG:root: linear factors: Matrix([[-1/tau_dopa]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 DEBUG:root:Initializing system of shapes with x = Matrix([[dopa_rate]]), A = Matrix([[-1/tau_dopa]]), b = Matrix([[0]]), c = Matrix([[0]]) INFO:root:Finding analytically solvable equations... INFO:root:Shape dopa_rate: reconstituting expression -dopa_rate/tau_dopa DEBUG:root:Splitting expression -dopa_rate/tau_dopa (symbols [dopa_rate]) DEBUG:root: linear factors: Matrix([[-1/tau_dopa]]) DEBUG:root: inhomogeneous term: 0.0 DEBUG:root: nonlinear term: 0.0 INFO:root:Generating propagators for the following symbols: dopa_rate DEBUG:root:Initializing system of shapes with x = Matrix([[dopa_rate]]), A = Matrix([[-1/tau_dopa]]), b = Matrix([[0]]), c = Matrix([[0]]) DEBUG:root:System of equations: DEBUG:root:x = Matrix([[dopa_rate]]) DEBUG:root:A = Matrix([[-1/tau_dopa]]) DEBUG:root:b = Matrix([[0]]) DEBUG:root:c = Matrix([[0]]) INFO:root:update_expr[dopa_rate] = __P__dopa_rate__dopa_rate*dopa_rate WARNING:root:Not preserving expression for variable "dopa_rate" as it is solved by propagator solver INFO:root:In ode-toolbox: returning outdict = INFO:root:[ { "initial_values": { "dopa_rate": "0.0" }, "parameters": { "tau_dopa": "100.000000000000" }, "propagators": { "__P__dopa_rate__dopa_rate": "exp(-__h/tau_dopa)" }, "solver": "analytical", "state_variables": [ "dopa_rate" ], "update_expressions": { "dopa_rate": "__P__dopa_rate__dopa_rate*dopa_rate" } } ] [55,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, WARNING, [29:8;29:28]]: Variable 'd' has the same name as a physical unit! [56,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, INFO, [28:27;28:27]]: Implicit casting from (compatible) type 'integer' to 'real'. [57,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, INFO, [31:24;31:24]]: Implicit casting from (compatible) type 'integer' to 'real'. [58,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, INFO, [46:21;46:29]]: Implicit casting from (compatible) type '1 / ms' to 'real'. [59,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, WARNING, [29:8;29:28]]: Variable 'd' has the same name as a physical unit! [60,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, INFO, [28:27;28:27]]: Implicit casting from (compatible) type 'integer' to 'real'. [61,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, INFO, [31:24;31:24]]: Implicit casting from (compatible) type 'integer' to 'real'. [62,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, INFO, [46:21;46:29]]: Implicit casting from (compatible) type '1 / ms' to 'real'. [63,GLOBAL, INFO]: Rendering template /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp [64,GLOBAL, INFO]: Rendering template /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.h [65,aeif_cond_alpha_neuron_nestml, INFO, [49:0;128:0]]: Successfully generated code for the model: 'aeif_cond_alpha_neuron_nestml' in: '/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new' ! [66,GLOBAL, INFO]: Rendering template /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp [67,GLOBAL, INFO]: Rendering template /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.h [68,aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml, INFO, [49:0;128:0]]: Successfully generated code for the model: 'aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml' in: '/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new' ! [69,GLOBAL, INFO]: Rendering template /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h [70,dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml, INFO, [19:0;59:0]]: Successfully generated code for the model: 'dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml' in: '/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new' ! [71,GLOBAL, INFO]: Rendering template /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/CMakeLists.txt [72,GLOBAL, INFO]: Rendering template /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.h [73,GLOBAL, INFO]: Rendering template /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.cpp [74,GLOBAL, INFO]: Successfully generated NEST module code in '/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new' ! CMake Warning: Ignoring empty string ("") provided on the command line. CMake Warning (dev) at CMakeLists.txt:95 (project): cmake_minimum_required() should be called prior to this top-level project() call. Please see the cmake-commands(7) manual for usage documentation of both commands. This warning is for project developers. Use -Wno-dev to suppress it. -- The CXX compiler identification is Clang 17.0.6 -- Detecting CXX compiler ABI info -- Detecting CXX compiler ABI info - done -- Check for working CXX compiler: /opt/miniconda3/envs/pd_nestml/bin/arm64-apple-darwin20.0.0-clang++ - skipped -- Detecting CXX compile features -- Detecting CXX compile features - done ------------------------------------------------------- nestml_gap_dopa_module Configuration Summary ------------------------------------------------------- C++ compiler : /opt/miniconda3/envs/pd_nestml/bin/arm64-apple-darwin20.0.0-clang++ Build static libs : OFF C++ compiler flags : -ftree-vectorize -fPIC -fstack-protector-strong -O2 -pipe -stdlib=libc++ -fvisibility-inlines-hidden -fmessage-length=0 -isystem /opt/miniconda3/envs/pd_nestml/include NEST compiler flags : -ftree-vectorize -fPIC -fstack-protector-strong -O2 -pipe -stdlib=libc++ -fvisibility-inlines-hidden -fmessage-length=0 -isystem /opt/miniconda3/envs/pd_nestml/include -fdebug-prefix-map=/Users/runner/miniforge3/conda-bld/nest-simulator_1724732993440/work=/usr/local/src/conda/nest-simulator-3.8 -fdebug-prefix-map=/opt/miniconda3/envs/pd_nestml=/usr/local/src/conda-prefix -std=c++17 -Wall -Xclang -fopenmp -O2 NEST include dirs : -I/opt/miniconda3/envs/pd_nestml/include/nest -I/opt/miniconda3/envs/pd_nestml/include -I/opt/miniconda3/envs/pd_nestml/include -I/opt/miniconda3/envs/pd_nestml/include -I/opt/miniconda3/envs/pd_nestml/include NEST libraries flags : -L/opt/miniconda3/envs/pd_nestml/lib/nest -lnest -lsli /opt/miniconda3/envs/pd_nestml/lib/libltdl.dylib /opt/miniconda3/envs/pd_nestml/lib/libreadline.dylib /opt/miniconda3/envs/pd_nestml/lib/libncurses.dylib /opt/miniconda3/envs/pd_nestml/lib/libgsl.dylib /opt/miniconda3/envs/pd_nestml/lib/libgslcblas.dylib /opt/miniconda3/envs/pd_nestml/lib/libomp.dylib ------------------------------------------------------- You can now build and install 'nestml_gap_dopa_module' using make make install The library file libnestml_gap_dopa_module.so will be installed to /opt/miniconda3/envs/pd_nestml/lib/nest The module can be loaded into NEST using (nestml_gap_dopa_module) Install (in SLI) nest.Install(nestml_gap_dopa_module) (in PyNEST) CMake Warning (dev) in CMakeLists.txt: No cmake_minimum_required command is present. A line of code such as cmake_minimum_required(VERSION 3.31) should be added at the top of the file. The version specified may be lower if you wish to support older CMake versions for this project. For more information run "cmake --help-policy CMP0000". This warning is for project developers. Use -Wno-dev to suppress it. -- Configuring done (3.6s) -- Generating done (0.0s) -- Build files have been written to: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new [ 75%] Building CXX object CMakeFiles/nestml_gap_dopa_module_module.dir/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.o [ 75%] Building CXX object CMakeFiles/nestml_gap_dopa_module_module.dir/aeif_cond_alpha_neuron_nestml.o [ 75%] Building CXX object CMakeFiles/nestml_gap_dopa_module_module.dir/nestml_gap_dopa_module.o In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp:44: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.h:376:17: warning: 'aeif_cond_alpha_neuron_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual] 376 | inline double get_C_m() const | ^ /opt/miniconda3/envs/pd_nestml/include/nest/node.h:797:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0) 797 | virtual double get_C_m( int comp ); | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp:44: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.h:406:17: warning: 'aeif_cond_alpha_neuron_nestml::get_g_L' hides overloaded virtual function [-Woverloaded-virtual] 406 | inline double get_g_L() const | ^ /opt/miniconda3/envs/pd_nestml/include/nest/node.h:798:18: note: hidden overloaded virtual function 'nest::Node::get_g_L' declared here: different number of parameters (1 vs 0) 798 | virtual double get_g_L( int comp ); | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp:44: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.h:708:3: warning: 'update' overrides a member function but is not marked 'override' [-Winconsistent-missing-override] 708 | update( nest::Time const& origin, const long from, const long to ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/node.h:296:16: note: overridden virtual function is here 296 | virtual void update( Time const&, const long, const long ) = 0; | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp:44: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.h:413:17: warning: 'aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual] 413 | inline double get_C_m() const | ^ /opt/miniconda3/envs/pd_nestml/include/nest/node.h:797:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0) 797 | virtual double get_C_m( int comp ); | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp:44: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.h:443:17: warning: 'aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml::get_g_L' hides overloaded virtual function [-Woverloaded-virtual] 443 | inline double get_g_L() constIn file included from | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.cpp:/opt/miniconda3/envs/pd_nestml/include/nest/node.h31:: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.h:376:17: warning: 'aeif_cond_alpha_neuron_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual]798 :18: 376 | note: hidden overloaded virtual function 'nest::Node::get_g_L' declared here: different number of parameters (1 vs 0) inlin e798 | d o uvbilret ugaelt _dCo_umb(l)e cgoents_tg_ L (| i ^n t comp ); | /opt/miniconda3/envs/pd_nestml/include/nest/node.h ^: 797:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0) 797 | virtual double get_C_m( int comp ); | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.cpp:31: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.h:406:In file included from 17/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp:: 44: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.h:warning: 'aeif_cond_alpha_neuron_nestml::get_g_L' hides overloaded virtual function [-Woverloaded-virtual]333 :8 406 | : inwarning: lin'register_stdp_connection' overrides a member function but is not marked 'override' [-Winconsistent-missing-override]e doubl 333 | e ge t _vgo_iLd( )r ecgoinsstter _std p| _c ^o nnecti/opt/miniconda3/envs/pd_nestml/include/nest/node.ho:n798(: 18d:o ublnote: e hidden overloaded virtual function 'nest::Node::get_g_L' declared here: different number of parameters (1 vs 0)t_ fi 798 | rst_re a dv,i rdtouuabll ed oduebllaey g)e;t_ g _| L( in ^t comp ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/archiving_node.h:95:8: note: overridden virtual function is here 95 | void register_stdp_connection( doubIn file included from l/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.cppe: 31t: _f/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.hi:r708s:t3_:r eadwarning: , d'update' overrides a member function but is not marked 'override' [-Winconsistent-missing-override]ou ble 708 | d e luapyd a)t eo(v enrersitd:e:;Ti m e| c ^o nst& origiIn file included from n/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp,: 44c: on/opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.hs:t780 :l3o:n g fwarning: rom'update' overrides a member function but is not marked 'override' [-Winconsistent-missing-override], const l o780n | g tuop d)at e (| n ^e st::Ti/opt/miniconda3/envs/pd_nestml/include/nest/node.hm:e296 :c16o:n st¬e: ooverridden virtual function is hereri gin 296 | , cons vt lirtongual from, vo coid nusptd altoen(g Ttiom e) c o n| st ^& , const/opt/miniconda3/envs/pd_nestml/include/nest/node.h: lo296n:g16,: connote: stoverridden virtual function is here l ong ) 296 | = 0; v | ^ irtual void update( Time const&, const long, const long ) = 0; | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp:224:16: warning: unused variable '__timestep' [-Wunused-variable] 224 | const double __timestep = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the timestep() function | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp:326:16: warning: unused variable '__timestep' [-Wunused-variable] 326 | const double __timestep = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the timestep() function | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp:390:10: warning: variable '__I_gap' set but not used [-Wunused-but-set-variable] 390 | double __I_gap = 0.0; | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp:505:16: warning: unused variable '__I_gap' [-Wunused-variable] 505 | double __I_gap = 0; | ^~~~~~~ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.cpp:33: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.h:413:17: warning: 'aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml::get_C_m' hides overloaded virtual function [-Woverloaded-virtual] 413 | inline double get_C_m() const | ^ /opt/miniconda3/envs/pd_nestml/include/nest/node.h:797:18: note: hidden overloaded virtual function 'nest::Node::get_C_m' declared here: different number of parameters (1 vs 0) 797 | virtual double get_C_m( int comp ); | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.cpp:33: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.h:443:17: warning: 'aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml::get_g_L' hides overloaded virtual function [-Woverloaded-virtual] 443 | inline double get_g_L() const | ^ /opt/miniconda3/envs/pd_nestml/include/nest/node.h:798:18: note: hidden overloaded virtual function 'nest::Node::get_g_L' declared here: different number of parameters (1 vs 0) 798 | virtual double get_g_L( int comp ); | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.cpp:33: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.h:333:8: warning: 'register_stdp_connection' overrides a member function but is not marked 'override' [-Winconsistent-missing-override] 333 | void register_stdp_connection( double t_first_read, double delay ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/archiving_node.h:95:8: note: overridden virtual function is here 95 | void register_stdp_connection( double t_first_read, double delay ) override; | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.cpp:33: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.h:780:3: warning: 'update' overrides a member function but is not marked 'override' [-Winconsistent-missing-override] 780 | update( nest::Time const& origin, const long from, const long to ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/node.h:296:16: note: overridden virtual function is here 296 | virtual void update( Time const&, const long, const long ) = 0; | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp:229:16: warning: unused variable '__timestep' [-Wunused-variable] 229 | const double __timestep = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the timestep() function | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp:337:16: warning: unused variable '__timestep' [-Wunused-variable] 337 | const double __timestep = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the timestep() function | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp:401:10: warning: variable '__I_gap' set but not used [-Wunused-but-set-variable] 401 | double __I_gap = 0.0; | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp:516:16: warning: unused variable '__I_gap' [-Wunused-variable] 516 | double __I_gap = 0; | ^~~~~~~ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/nestml_gap_dopa_module.cpp:36: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:480:18: warning: unused variable '__timestep' [-Wunused-variable] 480 | const double __timestep = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the timestep() function | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:505:12: warning: variable 'timestep' set but not used [-Wunused-but-set-variable] 505 | double timestep = 0; | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:580:18: warning: unused variable '_tr_t' [-Wunused-variable] 580 | const double _tr_t = __t_spike - __dendritic_delay; | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:680:12: warning: unused variable 'cd' [-Wunused-variable] 680 | double cd; | ^~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:819:16: warning: unused variable '__timestep' [-Wunused-variable] 819 | const double __timestep = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the timestep() function | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:831:16: warning: unused variable '__timestep' [-Wunused-variable] 831 | const double __timestep = nest::Time::get_resolution().get_ms(); // do not remove, this is necessary for the timestep() function | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:895:18: warning: unused variable '__timestep' [-Wunused-variable] 895 | const double __timestep = timestep; // do not remove, this is necessary for the timestep() function | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:968:18: warning: unused variable '_tr_t' [-Wunused-variable] 968 | const double _tr_t = start->t_; | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:950:10: warning: unused variable 'timestep' [-Wunused-variable] 950 | double timestep = 0; | ^~~~~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:482:10: warning: unused variable 'get_thread' [-Wunused-variable] 482 | auto get_thread = [tid]() | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/include/nest/connector_base.h:391:18: note: in instantiation of member function 'nest::dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml::send' requested here 391 | C_[ lcid ].send( e, tid, cp ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_base.h:227:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here 227 | explicit Connector( const synindex syn_id ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:311:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here 311 | thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:292:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here 292 | add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model.h:162:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here 162 | GenericConnectorModel( const std::string name ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/model_manager_impl.h:62:9: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here 62 | new GenericConnectorModel< ConnectionT< TargetIdentifierPtrRport > >( "dummy" ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/nest_impl.h:37:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here 37 | kernel().model_manager.register_connection_model< ConnectorModelT >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:651:9: note: in instantiation of function template specialization 'nest::register_connection_model' requested here 651 | nest::register_connection_model< dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:560:14: warning: unused variable 'get_t' [-Wunused-variable] 560 | auto get_t = [t_hist_entry_ms](){ return t_hist_entry_ms; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:583:12: warning: unused variable 'get_t' [-Wunused-variable] 583 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:631:12: warning: unused variable 'get_t' [-Wunused-variable] 631 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:896:10: warning: unused variable 'get_t' [-Wunused-variable] 896 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:555:9: note: in instantiation of member function 'nest::dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml::update_internal_state_' requested here 555 | update_internal_state_(t_lastspike_, (start->t_ + __dendritic_delay) - t_lastspike_, cp); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_base.h:391:18: note: in instantiation of member function 'nest::dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml::send' requested here 391 | C_[ lcid ].send( e, tid, cp ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_base.h:227:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here 227 | explicit Connector( const synindex syn_id ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:311:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here 311 | thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:292:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here 292 | add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model.h:162:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here 162 | GenericConnectorModel( const std::string name ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/model_manager_impl.h:62:9: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here 62 | new GenericConnectorModel< ConnectionT< TargetIdentifierPtrRport > >( "dummy" ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/nest_impl.h:37:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here 37 | kernel().model_manager.register_connection_model< ConnectorModelT >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:651:9: note: in instantiation of function template specialization 'nest::register_connection_model' requested here 651 | nest::register_connection_model< dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml >( name ); | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp:44: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.h:133:23: warning: inline function 'aeif_cond_alpha_neuron_nestml_dynamics' is not defined [-Wundefined-inline] 133 | extern "C" inline int aeif_cond_alpha_neuron_nestml_dynamics( double, const double ode_state[], double f[], void* pnode ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml.cpp:508:9: note: used here 508 | aeif_cond_alpha_neuron_nestml_dynamics( get_t(), S_.ode_state, f_temp, reinterpret_cast< void* >( this ) ); | ^ In file included from /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp:44: /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.h:133:23: warning: inline function 'aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml_dynamics' is not defined [-Wundefined-inline] 133 | extern "C" inline int aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml_dynamics( double, const double ode_state[], double f[], void* pnode ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml.cpp:519:9: note: used here 519 | aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml_dynamics( get_t(), S_.ode_state, f_temp, reinterpret_cast< void* >( this ) ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:463:7: warning: expression result unused [-Wunused-value] 463 | dynamic_cast< aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml& >(t); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:316:14: note: in instantiation of member function 'nest::dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml::check_connection' requested here 316 | connection.check_connection( src, tgt, receptor_type, get_common_properties() ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:292:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here 292 | add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model.h:162:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here 162 | GenericConnectorModel( const std::string name ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/model_manager_impl.h:62:9: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here 62 | new GenericConnectorModel< ConnectionT< TargetIdentifierPtrRport > >( "dummy" ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/nest_impl.h:37:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here 37 | kernel().model_manager.register_connection_model< ConnectorModelT >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:651:9: note: in instantiation of function template specialization 'nest::register_connection_model' requested here 651 | nest::register_connection_model< dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:482:10: warning: unused variable 'get_thread' [-Wunused-variable] 482 | auto get_thread = [tid]() | ^~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/include/nest/connector_base.h:391:18: note: in instantiation of member function 'nest::dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml::send' requested here 391 | C_[ lcid ].send( e, tid, cp ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_base.h:227:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here 227 | explicit Connector( const synindex syn_id ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:311:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here 311 | thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:292:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here 292 | add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model.h:162:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here 162 | GenericConnectorModel( const std::string name ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/model_manager_impl.h:103:38: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here 103 | ConnectorModel* conn_model = new GenericConnectorModel< CompleteConnectionT >( name ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/model_manager_impl.h:67:5: note: in instantiation of function template specialization 'nest::ModelManager::register_specific_connection_model_>' requested here 67 | register_specific_connection_model_< ConnectionT< TargetIdentifierIndex > >( name + "_hpc" ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/nest_impl.h:37:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here 37 | kernel().model_manager.register_connection_model< ConnectorModelT >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:651:9: note: in instantiation of function template specialization 'nest::register_connection_model' requested here 651 | nest::register_connection_model< dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:560:14: warning: unused variable 'get_t' [-Wunused-variable] 560 | auto get_t = [t_hist_entry_ms](){ return t_hist_entry_ms; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:583:12: warning: unused variable 'get_t' [-Wunused-variable] 583 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:631:12: warning: unused variable 'get_t' [-Wunused-variable] 631 | auto get_t = [__t_spike](){ return __t_spike; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:896:10: warning: unused variable 'get_t' [-Wunused-variable] 896 | auto get_t = [t_start](){ return t_start; }; // do not remove, this is in case the predefined time variable ``t`` is used in the NESTML model | ^~~~~ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:555:9: note: in instantiation of member function 'nest::dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml::update_internal_state_' requested here 555 | update_internal_state_(t_lastspike_, (start->t_ + __dendritic_delay) - t_lastspike_, cp); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_base.h:391:18: note: in instantiation of member function 'nest::dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml::send' requested here 391 | C_[ lcid ].send( e, tid, cp ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_base.h:227:12: note: in instantiation of member function 'nest::Connector>::send_to_all' requested here 227 | explicit Connector( const synindex syn_id ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:311:45: note: in instantiation of member function 'nest::Connector>::Connector' requested here 311 | thread_local_connectors[ syn_id ] = new Connector< ConnectionT >( syn_id ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:292:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here 292 | add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model.h:162:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here 162 | GenericConnectorModel( const std::string name ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/model_manager_impl.h:103:38: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here 103 | ConnectorModel* conn_model = new GenericConnectorModel< CompleteConnectionT >( name ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/model_manager_impl.h:67:5: note: in instantiation of function template specialization 'nest::ModelManager::register_specific_connection_model_>' requested here 67 | register_specific_connection_model_< ConnectionT< TargetIdentifierIndex > >( name + "_hpc" ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/nest_impl.h:37:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here 37 | kernel().model_manager.register_connection_model< ConnectorModelT >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:651:9: note: in instantiation of function template specialization 'nest::register_connection_model' requested here 651 | nest::register_connection_model< dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:463:7: warning: expression result unused [-Wunused-value] 463 | dynamic_cast< aeif_cond_alpha_neuron_nestml__with_dopamine_synapse_nestml& >(t); | ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:316:14: note: in instantiation of member function 'nest::dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml::check_connection' requested here 316 | connection.check_connection( src, tgt, receptor_type, get_common_properties() ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model_impl.h:292:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection_' requested here 292 | add_connection_( src, tgt, thread_local_connectors, syn_id, connection, actual_receptor_type ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/connector_model.h:162:3: note: in instantiation of member function 'nest::GenericConnectorModel>::add_connection' requested here 162 | GenericConnectorModel( const std::string name ) | ^ /opt/miniconda3/envs/pd_nestml/include/nest/model_manager_impl.h:103:38: note: in instantiation of member function 'nest::GenericConnectorModel>::GenericConnectorModel' requested here 103 | ConnectorModel* conn_model = new GenericConnectorModel< CompleteConnectionT >( name ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/model_manager_impl.h:67:5: note: in instantiation of function template specialization 'nest::ModelManager::register_specific_connection_model_>' requested here 67 | register_specific_connection_model_< ConnectionT< TargetIdentifierIndex > >( name + "_hpc" ); | ^ /opt/miniconda3/envs/pd_nestml/include/nest/nest_impl.h:37:26: note: in instantiation of function template specialization 'nest::ModelManager::register_connection_model' requested here 37 | kernel().model_manager.register_connection_model< ConnectorModelT >( name ); | ^ /opt/miniconda3/envs/pd_nestml/models/gap-dopamod-component-new/dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml.h:651:9: note: in instantiation of function template specialization 'nest::register_connection_model' requested here 651 | nest::register_connection_model< dopamine_synapse_nestml__with_aeif_cond_alpha_neuron_nestml >( name ); | ^ 8 warnings generated. 9 warnings generated. 28 warnings generated. [100%] Linking CXX shared module nestml_gap_dopa_module.so [100%] Built target nestml_gap_dopa_module_module [100%] Built target nestml_gap_dopa_module_module Install the project... -- Install configuration: "" -- Installing: /opt/miniconda3/envs/pd_nestml/lib/nest/nestml_gap_dopa_module.so (pd_nestml) wdg562@SUN1027631 NESTML_components %