About Resonate and Fire Neuron Model?

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Thanh Binh To

28 Nov 2024 28 Nov '24

4:32 p.m.

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Tom Tetzlaff

29 Nov 29 Nov

10:53 a.m.

Dear Thanh, in NEST, you could use the augmented multi-timescale adaptive threshold neuron (AMAT; https://nest-simulator.readthedocs.io/en/stable/models/amat2_psc_exp.html) or the Izhikevich neuron (https://nest-simulator.readthedocs.io/en/stable/models/izhikevich.html). Both can be parameterized such that they exhibit resonate-and-fire behavior. In https://doi.org/10.1007/s10827-018-0693-9, you'll find tables with parameter settings for the various types of neurons, the including resonating type. The advantage of the AMAT model is its linear subthreshold dynamics. It can hence be solved exactly (as it is in NEST). With the Izhikevich model, you'd need a numerical solver. Best Tom On 28.11.24 16:32, Thanh Binh To wrote:

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Dear all, I'd like to ask you if someone has a Resonate and Fire Neuron Model for NEST? Best thanks With kind regards Mit freundlichen Grüßen Thanh Binh To _______________________________________________ NEST Users mailing list -- users(a)nest-simulator.org To unsubscribe send an email to users-leave(a)nest-simulator.org

Thanh Binh To

12:03 p.m.

Tom Tetzlaff

1:01 p.m.

Sure. Simply search for "Resonator" in https://link.springer.com/article/10.1007/s10827-018-0693-9/tables/4 for AMAT, and in https://link.springer.com/article/10.1007/s10827-018-0693-9/tables/3 for Izhikevich. All parameters not mentioned in the tables are given in the table captions. Cheers Tom On 29.11.24 12:03, Thanh Binh To wrote:

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Dear Tom, thanks for your recommendation. Could you please show me more details, which parameters must be set to build a R&F neuron from AMAT or Izhikevich neuron? Best thanks and BR Binh *Gesendet:* Freitag, 29. November 2024 um 10:53 Uhr *Von:* "Tom Tetzlaff" <t.tetzlaff(a)fz-juelich.de> *An:* users(a)nest-simulator.org *Betreff:* [NEST Users] Re: About Resonate and Fire Neuron Model? Dear Thanh, in NEST, you could use the augmented multi-timescale adaptive threshold neuron (AMAT; https://nest-simulator.readthedocs.io/en/stable/models/amat2_psc_exp.html <https://nest-simulator.readthedocs.io/en/stable/models/amat2_psc_exp.html>) or the Izhikevich neuron (https://nest-simulator.readthedocs.io/en/stable/models/izhikevich.html <https://nest-simulator.readthedocs.io/en/stable/models/izhikevich.html>). Both can be parameterized such that they exhibit resonate-and-fire behavior. In https://doi.org/10.1007/s10827-018-0693-9 <https://doi.org/10.1007/s10827-018-0693-9>, you'll find tables with parameter settings for the various types of neurons, the including resonating type. The advantage of the AMAT model is its linear subthreshold dynamics. It can hence be solved exactly (as it is in NEST). With the Izhikevich model, you'd need a numerical solver. Best Tom On 28.11.24 16:32, Thanh Binh To wrote:

-- --- Dr. Tom Tetzlaff Institute for Advanced Simulation (IAS-6) Computational and Systems Neuroscience Jülich Research Centre Jülich, Germany https://www.fz-juelich.de/profile/tetzlaff_t <https://www.fz-juelich.de/profile/tetzlaff_t> ----------------------------------------------------------------------- ----------------------------------------------------------------------- Forschungszentrum Jülich GmbH 52425 Jülich Sitz der Gesellschaft: Jülich Eingetragen im Handelsregister des Amtsgerichts Düren Nr. HR B 3498 Vorsitzender des Aufsichtsrats: MinDir Stefan Müller Geschäftsführung: Prof. Dr. Astrid Lambrecht (Vorsitzende), Karsten Beneke (stellv. Vorsitzender), Dr. Ir. Pieter Jansens ----------------------------------------------------------------------- ----------------------------------------------------------------------- _______________________________________________ NEST Users mailing list -- users(a)nest-simulator.org To unsubscribe send an email to users-leave(a)nest-simulator.org _______________________________________________ NEST Users mailing list -- users(a)nest-simulator.org To unsubscribe send an email to users-leave(a)nest-simulator.org

Thanh Binh To

5:02 p.m.

Tom Tetzlaff

2 Dec 2 Dec

9:02 a.m.

Dear Binh, yes, these parameters should work. I can't tell by heart how the resonance frequency depends on the model parameters. I'd guess the time constants (tau_m, tau_1, tau_V; see table captions) play a major role here. Maybe the original papers (see references on the NEST documentation pages below) will answer your question. Best Tom On 29.11.24 17:02, Thanh Binh To wrote:

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Dear Tom, yes I found for Resonator for AMAT model alpha_1 = 10, alpha_2 = 0, and beta = 0.5 for Izhikevich a = 0.1, b=0.26, c=-60, d=-1, psi = 2.4 and Iext = 0 If I understood you well, I have to set those parameters of the corresponding neuron model, so that should work.???? But how can I know the resonate frequency of this neuron in relating to those parameter set? Thanks With kind regards Mit freundlichen Grüßen Thanh Binh To *Gesendet:* Freitag, 29. November 2024 um 13:01 Uhr *Von:* "Tom Tetzlaff" <t.tetzlaff(a)fz-juelich.de> *An:* users(a)nest-simulator.org *Betreff:* [NEST Users] Re: About Resonate and Fire Neuron Model? Sure. Simply search for "Resonator" in https://link.springer.com/article/10.1007/s10827-018-0693-9/tables/4 <https://link.springer.com/article/10.1007/s10827-018-0693-9/tables/4> for AMAT, and in https://link.springer.com/article/10.1007/s10827-018-0693-9/tables/3 <https://link.springer.com/article/10.1007/s10827-018-0693-9/tables/3> for Izhikevich. All parameters not mentioned in the tables are given in the table captions. Cheers Tom On 29.11.24 12:03, Thanh Binh To wrote:

https://nest-simulator.readthedocs.io/en/stable/models/amat2_psc_exp.html <https://nest-simulator.readthedocs.io/en/stable/models/amat2_psc_exp.html> <https://nest-simulator.readthedocs.io/en/stable/models/amat2_psc_exp.html <https://nest-simulator.readthedocs.io/en/stable/models/amat2_psc_exp.html>>)

or the Izhikevich neuron

(https://nest-simulator.readthedocs.io/en/stable/models/izhikevich.html <https://nest-simulator.readthedocs.io/en/stable/models/izhikevich.html>

<https://nest-simulator.readthedocs.io/en/stable/models/izhikevich.html <https://nest-simulator.readthedocs.io/en/stable/models/izhikevich.html>>).

Both can be parameterized such that they exhibit resonate-and-fire behavior. In https://doi.org/10.1007/s10827-018-0693-9

<https://doi.org/10.1007/s10827-018-0693-9>

<https://doi.org/10.1007/s10827-018-0693-9

<https://doi.org/10.1007/s10827-018-0693-9>>, you'll find

tables with parameter settings for the various types of neurons, the including resonating type. The advantage of the AMAT model is its linear subthreshold dynamics. It can hence be solved exactly (as it is in NEST). With the Izhikevich model, you'd need a numerical solver. Best Tom On 28.11.24 16:32, Thanh Binh To wrote:

-- --- Dr. Tom Tetzlaff Institute for Advanced Simulation (IAS-6) Computational and Systems Neuroscience Jülich Research Centre Jülich, Germany https://www.fz-juelich.de/profile/tetzlaff_t

<https://www.fz-juelich.de/profile/tetzlaff_t>

<https://www.fz-juelich.de/profile/tetzlaff_t

<https://www.fz-juelich.de/profile/tetzlaff_t>>

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Thanh Binh To
Tom Tetzlaff