I would like to ask how to normalize synaptic weights during STDP learning.
What I want to do is keeping the total amount of synaptic weights to/from each neuron constant.
I try to describe my goal a bit more precisely:
i: The index of a pre-synaptic neuron.
j: The index of a post-synaptic neuron.
w_ij(t): The synaptic weight from i-th neuron to j-th neuron at time=t.
s_i(t): the sum of all incoming synaptic weights to i-th neuron at time=t. i.e. the sum of w_ij(t) over j.
The goal is to set the sum of incoming weights (s_i(*)) to 1 by normalization (1 is just for simplicity).
By STDP, we can get updated synaptic weight matrix w_ij(t+1), and the sum s_i(t+1) is not necessarily 1. So, I want to set
w_ij(t+1) <- w_ij(t+1) / s_i(t+1)
I briefly read the documentation for synaptic models(https://nest-simulator.readthedocs.io/en/stable/models/synapses.html).
I am not very confident if my understanding is correct, but it seems that existing synaptic models handle only one synaptic weight.
How can I gather data of all incoming(or outgoing as well) weights to a neuron and use the summation value inside of the STDP synapse model?
Keiko Fujii_______________________________________________NEST Users mailing list -- firstname.lastname@example.orgTo unsubscribe send an email to email@example.com