29 Nov
2021
29 Nov
'21
10:59 a.m.
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_ac…
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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