Maximizing the efficiency of decoding by selecting heterogeneous inputs from a population of electrosensory neurons responding to communication signals

Abstract

Neural processing of sensory signals relies on spatially and temporally distributed patterns of activity across a population of neurons; understanding the structure of these patterns of activity is key to understanding neural codes and thus how information is extracted from the environment to guide specific behaviors. It has long been recognized that a population of neurons of the same type, in a given brain area and subserving a similar function, must be regarded as a synergistic whole [1]. Only in recent years has the heterogeneity of neural properties within such a population, and its critical importance to the function of the population, been fully appreciated [2,3]. In the electrosensory system of gymnotiform fish, neural heterogeneity in the primary sensory area of the hindbrain (i.e. the ELL) has been shown to enhance the accuracy of encoding of a specific kind of communication signal: big chirps. Big chirps are commonly produced by males during courtship interactions with females. As with any courtship signal, its quality could influence the female’s response, and thus, detailed information about the signal’s characteristics should be acquired by the nervous system. The pyramidal cells of the ELL respond to big chirp with a graded increase in firing rate, and their spiking patterns are very variable from cell to cell, much more than from one response of ac ell to another response of the same cell to the same stimulus. Even cells that belong to the same sub-class can differ significantly in their response patterns. In a