Abstract

Normalization is a widespread neural computation in both early sensory coding and higher-order processes such as attention and multisensory integration. It has been shown that during decision-making, normalization implements a context-dependent value code in parietal cortex. In this paper we develop a simple differential equations model based on presumed neural circuitry that implements normalization at equilibrium and predicts specific time-varying properties of value coding. Moreover, we show that when parameters representing value are changed, the solution curves change in a manner consistent with normalization theory and experiment. We show that these dynamic normalization models naturally implement a time-discounted normalization over past activity, implying an intrinsic reference-dependence in value coding of a kind seen experimentally. These results suggest that a single network mechanism can explain transient and sustained decision activity, reference dependence through time discounting, and hence emphasizes the importance of a dynamic rather than static view of divisive normalization in neural coding.