Chapter 2: Strategies for finding neural codes

Sheila Nirenberg
Department of Physiology and Biophysics, Cornell University

A critical problem in systems neuroscience is determining what the neural code is. Many codes have been proposed—coarse codes, fine codes, temporal correlation codes, and synchronous firing codes, among others. The number of candidates has grown as more and more studies have shown that different aspects of spike trains can carry information (reviewed in Averbeck and Lee, 2004; Oram et al., 2002; Victor, 1999; Borst and Theunissen, 1999; Johnson and Ray, 2004; Theunissen and Miller, 1995; Nirenberg and Latham, 2003; Shadlen and Newsome, 1994; MacLeod, Backer, and Laurent, 1998; Bialek et al., 1991; Nirenberg et al., 2001; Parker and Newsome, 1998; Romo and Salinas, 2001; Dhingra et al., 2003; Gawne, Richmond, and Optican, 1991). Here we present a strategy to reduce the space of possibilities. We describe a framework for determining which codes are viable and which are not, that is, which can and cannot account for behavior. Our approach is to obtain an upper bound on the performance of each code and compare it to the performance of the animal. The upper bound is obtained by measuring code performance using the same number and distribution of cells that the animal uses, the same amount of data the animal uses, and a decoding strategy that is as good as or better than the one that the animal uses. If the upper-bound performance falls short of the animal’s performance, the code can be eliminated. We demonstrate the application of this approach to a model system, the mouse retina.

Key words: neural coding, population coding, Bayesian analysis, ideal observer analysis, information theory