Cannabinoid-mediated disinhibition and working memory: Dynamical interplay of multiple feedback mechanisms in a continuous attractor model of prefrontal cortex

Eugene Carter, Xiao Jing Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Recurrent excitation is believed to underlie persistent neural activity observed in the prefrontal cortex and elsewhere during working memory. However, other positive and negative feedback mechanisms, operating on disparate timescales, may also play significant roles in determining the behavior of a working memory circuit. In this study, we examined dynamical interactions of multiple feedback mechanisms in a biophysically based neural model of spatial working memory. In such continuous attractor networks, a self-sustained activity pattern tends to drift randomly, resulting in a decreased accuracy of memory over time. Moreover, attractor states become unstable when spike-frequency adaptation reduces the excitability of persistently firing pyramidal neurons. Here, we show that a slow activity-dependent local disinhibition, namely cannabinoid-dependent depolarization-induced suppression of inhibition (DSI), can counteract these destabilizing effects, rendering working memory function more robust. In addition, the slow DSI effect gives rise to trial-to-trial correlations of memoryguided behavioral responses. On the other hand, computer simulations revealed that a global cannabinoid agonist (mimicking the effect of drug intake) yields the opposite effect. Thus, this work suggests a circuit scenario according to which endogenous DSI is beneficial for, whereas an exogenous drug such as marijuana is detrimental to, working memory and possibly other prefrontal functions.

Original languageEnglish (US)
Pages (from-to)i16-i26
JournalCerebral Cortex
Volume17
Issue numberSUPPL. 1
DOIs
StatePublished - Sep 2007

Keywords

  • Continuous attractor network
  • Endocannabinoid
  • Oculomotor delayed-response task
  • Prefrontal cortex
  • Working memory

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Cellular and Molecular Neuroscience

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