On the efficiency of neuronal information processing

Abstract:

The research in computational neuroscience has a tradition of more than 100 years, marked, e.g., by the now-classical Lapicque, McCulloch-Pitts or Hodgkin-Huxley neuronal models. During the last three decades the field has experienced a dramatic increase, attracting a number of scientists from different disciplines. New topics have emerged alongside the traditional neuronal modeling approaches and the long-standing problem of neuronal coding is recently receiving substantial attention. The approach to the problem relies on the applications of information theory, signal detection and estimation theory and theory of stochastic processes to different aspects of neuronal information processing, including coding and decoding in individual neurons and populations, or analysis of beneficial role of the noise in the system. Understanding the principles of information processing in neurons may help to introduce, e.g., new algorithms or new generation of hardware which could enhance artificial sensors.