A neural network model of reliably optimized spike transmission

Toshikazu Samura, Yuji Ikegaya, Yasuomi D. Sato

Research output: Contribution to journalReview articlepeer-review

6 Citations (Scopus)


We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.

Original languageEnglish
Pages (from-to)265-277
Number of pages13
JournalCognitive Neurodynamics
Issue number3
Publication statusPublished - 2015 Jun 1
Externally publishedYes


  • Log-normally distributed synaptic weights
  • Power-law-distributed synchrony
  • Spike transmission
  • Synaptic background activity

ASJC Scopus subject areas

  • Cognitive Neuroscience


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