Characterizing the fine structure of a neural sensory code through information distortion

Abstract

We present an application of the information distortion approach to neural coding. The approach allows the discovery of neural symbols and the corresponding stimulus space of a neuron or neural ensemble simultaneously and quantitatively, making few assumptions about the nature of either code or relevant features. The neural codebook is derived by quantitizing sensory stimuli and neural responses into small reproduction sets, and optimizing the quantization to minimize the information distortion function. The application of this approach to the analysis of coding in sensory interneurons involved a further restriction of the space of allowed quantitizers to a smaller family of parametric distributions. We show that, for some cells in this system, a significant amount of information is encoded in patterns of spikes that would not be discovered through analyses based on linear stimulus-response measures.