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Hello Nest users,
I recently started to work on my bachelor thesis and the first step is to find existing simulators of neural networks and analyze them. Regarding that, I have some questions about the Nest to which I cannot find answers.
Is there some graphical interface for Nest? I have found a GitHub project called nest-desktop (https://github.com/babsey/nest-desktop), but I could not find it being mentioned anywhere in the nest simulator documentation. Does anyone have some experience with this? Is it reliable? What is it capable of?
If there is not any official Nest GUI, how do you typically work on new simulations? Do you just write plain python scripts for PyNest interface?
The last request is directed more to the developers, but maybe someone can lead me in the right way. My thesis lead would like to see some schema of the Nest architecture or some overview of how the simulator is designed.
Thank you very much,
Roman Kalivoda
Dear Nikolas,
Could the following example in NEST Simulator be what you're looking for?
https://github.com/nest/nest-simulator/blob/f28b138f469354264f65c3b5c9d05e5…
"""Check STDP protocol between two parrot_neurons connected by a stdp_synapse. Exact pre- and post-synaptic spike times are set by spike_generators connected to each parrot neuron. Additional spikes sent through the stdp_synapse are explicitly ignored in the postsynaptic parrot_neuron by setting the stdp_synapse to connect to port 1. """
With kind regards,
Charl
On Wed, Oct 30, 2019, at 11:58, Nikolaos Chrysanthidis wrote:
> Hi Charl,
> I want to test the weight change of a single synapse providing stimulation at 2 parrot neurons, so for that reason,
> If I excite two pre-,post- parrot neurons (parrot_A,B) using port 0, then the parrot_A will excite the parrot_B as well, so the parrot_B will end up having more spikes than expected.
> I think this can be overcome by using port1 of parrot_B in order to ignore the extra excitation from parrot_A.
> Please, I'd be grateful if you could provide a way to avoid extra excitation from parrot_A onto parrot_B.
>
> Kind regards,
> Nikolas
>
> *From:* Charl Linssen <nest-users(a)turingbirds.com>
> *Sent:* 28 October 2019 12:11
> *To:* users(a)nest-simulator.org
> *Subject:* [NEST Users] Re: parrot neurons PORT 1
>
> Dear Nikolaos,
>
> I'm a little confused as to why you wish to use port 1 for this. A synapse is not aware of incoming or outgoing ports, so if you want to test a particular synapse model, you can connect it between port 0 of a presynaptic parrot neuron, and port 0 of a postsynaptic parrot neuron. Could you otherwise please clarify your use case a bit?
>
> With kind regards,
> Charl
>
>
> On Thu, Oct 17, 2019, at 17:00, Nikolaos Chrysanthidis wrote:
>> Hi NEST users,
>>
>> I have a pre- and post-synaptic spike train and I want to impose these spike trains onto a plastic synapse to see the weight development.
>>
>> To this end, I thought of having 2 "parrot_neuron_ps" as pre- and post-synaptic neurons connected with the plastic synapse which uses AMPA receptor.
>> By doing this, NEST yields the following error: "NESTError: UnknownReceptorType in Connect_g_g_D_D: Receptor type 1 is not available in parrot_neuron_ps." which in my case Receptor type 1 is the AMPA receptor.
>>
>> At the nest documentation about "parrot_neuron_ps" I found out that I can have the plastic synapse onto port 1 of the postsynaptic parrot neuron
>> which I hope it will solve the error with the receptor.
>>
>> From nest documentation -> "Only spikes arriving on connections to port 0 will be repeated. Connections onto port 1 will be accepted, but spikes incoming
>> through port 1 will be ignored. This allows setting exact pre- and post-synaptic spike times for STDP protocols by connecting
>>
>> two parrot neurons spiking at desired times by, e.g., a stdp_synapse onto port 1 on the post-synaptic parrot neuron."
>>
>> My query is, how to connect the 2 neurons using port 1 of the post synaptic neuron ?
>> If this approach with parrot neurons doesn't seem the right choice, what is the most commonly used approach ?
>>
>> Thank you very much,
>> Kind regards,
>> Nikolas
>>
>>
>>
>>
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>
