Proceedings of 2009 IEEE International Symposium on Information Theory (ISIT2009), pp. 1589–1593

著者:

  • Shiro Ikeda
  • Jonathan H. Manton

キーワード:

  • channel capacity
  • channel capacity achieving distribution
  • neuroscience
  • neuron

URL:

Abstract:

The information transfer through a single neuron is a fundamental information processing in the brain. This paper studies the information-theoretic capacity of a single neuron by treating the neuron as a communication channel. Two different models are considered. The temporal coding model of a neuron as a communication channel assumes the output is $\tau$ where $\tau$ is a gamma-distributed random variable corresponding to the inter-spike interval, that is, the time it takes for the neuron to fire once. The rate coding model is similar; the output is the actual rate of firing over a fixed period of time. We prove that for both models, the capacity achieving distribution has only a finite number of probability mass points. This allows us to compute numerically the capacity of a neuron. Our capacity results are in a plausible range based on biological evidence to date.