Oscillatory recurrent gated neural integrator circuits (ORGaNICs), a unifying theoretical framework for neural dynamics

David J. Heeger, Wayne E. Mackey

Research output: Contribution to journalArticle


Working memory is an example of a cognitive and neural process that is not static but evolves dynamically with changing sensory inputs; another example is motor preparation and execution. We introduce a theoretical framework for neural dynamics, based on oscillatory recurrent gated neural integrator circuits (ORGaNICs), and apply it to simulate key phenomena of working memory and motor control. The model circuits simulate neural activity with complex dynamics, including sequential activity and traveling waves of activity, that manipulate (as well as maintain) information during working memory. The same circuits convert spatial patterns of premotor activity to temporal profiles of motor control activity and manipulate (e.g., time warp) the dynamics. Derivative-like recurrent connectivity, in particular, serves to manipulate and update internal models, an essential feature of working memory and motor execution. In addition, these circuits incorporate recurrent normalization, to ensure stability over time and robustness with respect to perturbations of synaptic weights.

Original languageEnglish (US)
Pages (from-to)22783-22794
Number of pages12
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number45
StatePublished - Nov 5 2019



  • Computational neuroscience
  • Motor control
  • Normalization
  • Recurrent neural network
  • Working memory

ASJC Scopus subject areas

  • General

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