Spontaneous activity modeling in spiking neural systems with log-normal synaptic weight distribution

Abstract

Cortical neural networks are known to exhibit the spontaneous spiking activity with low firing rate (≈ 1 [Hz]) and high randomness, but the high coherent spike transmission between specific neurons. Recently, the genesis and function of this spontaneous activity in the brain has been increasingly gaining attention. Teramae et al. proposed that these features can be caused by the synaptic weights following log-normal distribution by using leaky integrate-and-fire (LIF) neuron model. However, LIF neuron model removes several factors of the neuro dynamics and it is necessary to study the effects of them to the spontaneous spiking activity. In our study, introducing new evaluation measures and indices, we first reanalyze the characteristics of spike transmission in LIF neural system with the log-normal synaptic weight distribution. Second, we focus on Izhikevich neuron model which can reproduce several major spiking activities in the cerebral cortex and examine the characteristics of spike transmission in Izhikevich neural system, comparing with the results of LIF neural system.