The Temporal Dynamics of Cortical Normalization Models of Decision-making

  • Thomas LoFaro Gustavus Adolphus College
  • Kenway Louie New York University
  • Ryan Webb University of Toronto
  • Paul W. Glimcher New York University
Keywords: neuroscience, neuroeconomics, cortical normalization, differential equations


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.

How to Cite
LoFaro, Thomas, Kenway Louie, Ryan Webb, and Paul W. Glimcher. 2014. “The Temporal Dynamics of Cortical Normalization Models of Decision-Making”. Letters in Biomathematics 1 (2), 209-20.