Background-activity-dependent properties of a network model for working memory that incorporates cellular bistability

  • Authors:

  • Christopher P. Fall;Timothy J. Lewis;John Rinzel

  • Affiliations:

  • New York University Center for Neural Science, 10003, New York, NY, USA;New York University Center for Neural Science, 10003, New York, NY, USA and Courant Institute of Mathematical Science, New York University, 10012, New York, NY, USA;New York University Center for Neural Science, 10003, New York, NY, USA and Courant Institute of Mathematical Science, New York University, 10012, New York, NY, USA

  • Venue:
  • Biological Cybernetics
  • Year:
  • 2005

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Abstract

In models of working memory, transient stimuli are encoded by feature-selective persistent neural activity. Network models of working memory are also implicitly bistable. In the absence of a brief stimulus, only spontaneous, low-level, and presumably nonpatterned neural activity is seen. In many working-memory models, local recurrent excitation combined with long-range inhibition (Mexican hat coupling) can result in a network-induced, spatially localized persistent activity or “bump state” that coexists with a stable uniform state. There is now renewed interest in the concept that individual neurons might have some intrinsic ability to sustain persistent activity without recurrent network interactions. A recent visuospatial working-memory model (Camperi and Wang 1998) incorporates both intrinsic bistability of individual neurons within a firing rate network model and a single population of neurons on a ring with lateral inhibitory coupling. We have explored this model in more detail and have characterized the response properties with changes in background synaptic input Io and stimulus width. We find that only a small range of Io yields a working-memory-like coexistence of bump and uniform solutions that are both stable. There is a rather larger range where only the bump solution is stable that might correspond instead to a feature-selective long-term memory. Such a network therefore requires careful tuning to exhibit working-memory-like function. Interestingly, where bumps and uniform stable states coexist, we find a continuous family of stable bumps representing stimulus width. Thus, in the range of parameters corresponding to working memory, the model is capable of capturing a two-parameter family of stimulus features including both orientation and width.