Dynamic divisive normalization predicts time-varying value coding in decision-related circuits

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

Research output: Contribution to journalArticlepeer-review


Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding.

Original languageEnglish (US)
Pages (from-to)16046-16057
Number of pages12
JournalJournal of Neuroscience
Issue number48
StatePublished - Nov 26 2014


  • Computational modeling
  • Decision-making
  • Divisive normalization
  • Dynamical system
  • Reward

ASJC Scopus subject areas

  • General Neuroscience


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