The Temporal Dynamics of Cortical Normalization Models of Decision-making

Thomas LoFaro, Kenway Louie, Ryan Webb, Paul W. Glimcher

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)209-220
Number of pages12
JournalLetters in Biomathematics
Issue number2
StatePublished - Jan 1 2014


  • cortical normalization
  • differential equations
  • neuroeconomics
  • neuroscience

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Applied Mathematics


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