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Synaptic plasticity in a cortical microcircuit model: different - - PowerPoint PPT Presentation

Oct 21, 2023 147 likes •369 views

Synaptic plasticity in a cortical microcircuit model: different scenarios Renan O. Shimoura, Antonio C. Roque Physics Department, FFCLRP, University of So Paulo, Ribeiro Preto, SP, Brazil Laboratrio de Sistema Neurais (SisNe)

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Synaptic plasticity in a cortical microcircuit model: different scenarios

Renan O. Shimoura, Antonio C. Roque Physics Department, FFCLRP, University of São Paulo, Ribeirão Preto, SP, Brazil

Laboratório de Sistema Neurais (SisNe) renanshimoura@usp.br

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Introduction

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Primary visual cortex

  • As the entire neocortex, V1 is anatomically

divided into six layers, where each layer has different types and numbers

  • f

neurons.

  • Synaptic plasticity is thought to be the

underlying mechanism behind learning and memory.

  • There are neurons in

V1 which its response is selective to angular

  • rientation.
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Goal

  • How does the synaptic plasticity affect the
  • rientation selectivity of the network?
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SLIDE 5

Methods

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The network

Excitatory neurons Excitatory synapses Inhibitory neurons Inhibitory synapses Potjans TC, Diesmann M (2014).

10,000 neurons ~ 5 million synapses

Poissonian noise (8 Hz)

Excitatory/inhibitory ratio = 4:1

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Stochastic neuron model (GL model)

Synaptic increment

i j

wij

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Izhikevich (2007). Galves, A., Löcherbach, E. (2013).

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Asymmetric spike-timing-dependent plasticity (STDP) rule

%∆w tpre-tpost

* Song S, Miller KD, Abbott LF (2000). Competitive Hebbian learning through spike- timing-dependent synaptic plasticity.

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  • Duration of simulation: 10000 ms;
  • 1st: Poissonian spike trains applied as background with 8 Hz;
  • 2nd: visual stimuli applied at L4 as angular oriented pulses;

𝐽𝑓𝑦𝑢,𝑗 = 𝐽 ∙ cos (𝜄𝐽 − 𝜄𝑗

∗)

OSI = 0 → The neuron fires for any stimuli. OSI = 1 → The neuron fires preferentially to one angle.

Simulations

𝑃𝑇𝐽𝑗 = 𝜄 𝑔

𝑗 𝜄 𝑑𝑝𝑡 2𝜄 2 + ( 𝜄 𝑔 𝑗 𝜄 𝑡𝑓𝑜 2𝜄 )²

𝜄 𝑔

𝑗(𝜄)

Orientation selectivity index (OSI):

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Preliminary results

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Control (no STDP) With STDP

L23e L23i L4e L4i L5e L5i L6e L6i L23e L23i L4e L4i L5e L5i L6e L6i

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Control STDP Orientation Selectivity Index (OSI) L23 L4 L23 L4

#n: 1.86 % #n: 15.20 % #n: 0.56 % #n: 13.51 %

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Control STDP Orientation Selectivity Index (OSI) L5 L6 L5 L6

#n: 0.00 % #n: 20.51 % #n: 4.84 % #n: 6.55 %

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Partial conclusion

  • In the first case, the network with STDP higher average

frequency;

  • STDP can improve the orientation selectivity in this

network.

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References

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References

  • Potjans TC, Diesmann M (2014). The cell-type specific cortical

microcircuit: relating structure and activity in a full-scale spiking network model. Cereb. Cortex, 24;785-806.

  • Izhikevich EM (2007). Dynamical Systems in Neuroscience: The

Geometry of Excitability and Bursting. MIT Press, Cambridge, MA.

  • Galves, A., Löcherbach, E. (2013). Infinite systems of interacting

chains with memory of variable length: a stochastic model for biological neural nets. J. Stat. Phys. 151:896-921.

  • Song S, Miller KD, Abbott LF (2000). Competitive Hebbian learning

through spike-timing-dependent synaptic plasticity. Nat Neurosci 3(9):919-926.

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Control (no STDP)

V ( mV)

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With STDP

V ( mV)

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Control STDP