Journal of Computational Neuroscience

, Volume 5, Issue 4, pp 383–405 | Cite as

A Model of Visuospatial Working Memory in Prefrontal Cortex: Recurrent Network and Cellular Bistability

  • Marcelo Camperi
  • Xiao-Jing Wang


We report a computer simulation of the visuospatial delayed-response experiments of Funahashi et al. (1989), using a firing-rate model that combines intrinsic cellular bistability with the recurrent local network architecture of the neocortex. In our model, the visuospatial working memory is stored in the form of a continuum of network activity profiles that coexist with a spontaneous activity state. These neuronal firing patterns provide a population code for the cue position in a graded manner. We show that neuronal persistent activity and tuning curves of delay-period activity (memory fields) can be generated by an excitatory feedback circuit and recurrent synaptic inhibition. However, if the memory fields are constructed solely by network mechanisms, noise may induce a random drift over time in the encoded cue position, so that the working memory storage becomes unreliable. Furthermore, a “distraction” stimulus presented during the delay period produces a systematic shift in the encoded cue position. We found that the working memory performance can be rendered robust against noise and distraction stimuli if single neurons are endowed with cellular bistability (presumably due to intrinsic ion channel mechanisms) that is conditional and realized only with sustained synaptic inputs from the recurrent network. We discuss how cellular bistability at the single cell level may be detected by analysis of spike trains recorded during delay-period activity and how local modulation of intrinsic cell properties and/or synaptic transmission can alter the memory fields of individual neurons in the prefrontal cortex.

working memory prefrontal cortex bistability network attractor dynamics 


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Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Marcelo Camperi
    • 1
    • 2
  • Xiao-Jing Wang
    • 3
  1. 1.Center for Complex SystemsBrandeis UniversityWaltham
  2. 2.Physics DepartmentUniversity of San FranciscoSan Francisco
  3. 3.Center for Complex Systems and Department of PhysicsBrandeis UniversityWaltham

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