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Neural Network Modeling

開催日 2014/9/11
時間 11:00 - 12:00
会場 Poster / Exhibition(Event Hall B)

Theoretical study on spike-timing probability in a pair of pre-post synaptic neurons

  • P1-369
  • Safura Rashid-Shomali:1 Majid Nili Ahmadabadi:1,2 Hideaki Shimazaki:3 S Nader Rasuli:4 
  • 1:School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran 2:School of ECE, College of Engineering, University of Tehran, Tehran, Iran 3:RIKEN Brain Science Institute, Wako, Saitama, Japan  4:Department of Physics, University of Guilan, Rasht, Iran 

Neurons in the cortex are embedded in intricate networks and produce spikes in response to bombardment of stochastic, balanced synaptic inputs from excitatory and inhibitory pre-synaptic neurons when processing information. While the noisy synaptic inputs may obscure the effect of individual pre-synaptic neurons on post-synaptic spike generation, the output spikes are certainly not random, but can be controlled by features of selected input spike patterns buried in the noisy inputs. Although fundamentals of neuronal information transmission are described by such interactions between the input statistics and spiking nonlinearity, analytical studies on the effect of pre-synaptic inputs on generation of post-synaptic spikes under the balanced conditions are scarce; even in a simple leaky integrate-and-fire (LIF) neuron model. In this study, we theoretically investigate the effect of a single pre-synaptic neuron on spike timing of a post-synaptic neuron using the LIF model receiving noisy balanced inputs. Through diffusion approximation of the model and linear analysis on the corresponding Fokker-Planck equation, we provide the probability distribution of post-synaptic spike-timing conditioned on a pre-synaptic neuron's spike timing. In particular, we analytically demonstrate the change in the post-synaptic neuron's spiking probability caused by individual or synchronous pre-synaptic inputs. The result of this study is expected to be useful to analyze capacity of a LIF model neuron receiving balanced inputs, and further investigate synaptic weights' dynamics; that are continuously strengthened or weakened by causal plasticity rules.

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