Hello,

Thank you very much for your answer.

The model I am trying to implement in NEST is explained in details in the article in attachment of this email. In case it is more convenient for you, here is also the reference :  Vincent Fontanier, Matthieu Sarazin, Frederic Stoll, Bruno Delord, Emmanuel Procyk. Local in-
hibitory control of frontal network metastability underlies the temporal signature of cognitive states.
2021. hal-03094565

The article is quite long, there is no need at all to read it entirely. The only part related to my model is starting at the middle of page 40 and ending page 42. It is the "Model of local recurrent neural networks in frontal areas" (bold tittle), not the "Cellular model of pyramidal neurons in frontal areas" that starts page 38.

Thus, to answer your questions:

Le 2021-11-28 21:56, Charl Linssen a écrit :

Hi,

 

Thanks for writing in. Just some questions to make sure I understand it right. Does the channel opening probability in a given synapse only depend on the history of presynaptic spikes? In that case, it would be computationally most efficient to compute these probabilities in the (presynaptic) neuron objects, rather than in the synapses, because each synapse downstream from that neuron would compute the same probabilities anyway.

 

It is possible to use the postsynaptic membrane potential to modulate synaptic plasticity, but this ignores the synaptic delay associated with the connection. For an example, please see: https://nestml.readthedocs.io/en/latest/tutorials/active_dendrite/nestml_active_dendrite_tutorial.html

 

It might help if you have a link to the paper, or a full description of the model you would like to implement.

 

With kind regards,

Charl

 

 

On Thu, Nov 25, 2021, at 15:24, barthelemy wrote:

Dear all,

 

I am a new NEST user. I have a question concerning the range of neuron/synapses model possibilities of NEST.

I would like to implement my own neuron/synapse model with NESTML, but I am unsure that it would be possible.

Indeed, in my model, synaptic currents are not only relying on pre-synaptic spikes. To compute synaptic currents, the opening probability of pre-synaptic channel receptors are required.

Those pre-synaptic channel receptors opening probabilities are evolving according to differential equations involving second order dynamics, with specific decays and taking into account the pre-synaptic spikes arrivals times at this specific synapse.

Those differential equations for the opening probabilities are relying on different parameters, according to the neurotransmitter type (GABA A,GABA B, NMDA, AMPA ).

Furthermore, additionally to the input spikes and the pre-synaptic channel receptors opening probabilities, the current membrane potential of the post-synaptic neuron is also required to compute the synaptic currents.

Do you know if one of the NEST models implement similar dynamics? Is it possible to compute such synaptic dynamics with NESTML by creating a synapse or (and) a neuron model? Or is it not, due to specific limitations?

Thank you,

Best regards,

JB

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