Nov 18, 2019

Flexible working memory through selective gating and attentional tagging

BioRxiv : the Preprint Server for Biology
Wouter KruijneChristian N.L. Olivers


Working memory is essential for intelligent behavior as it serves to guide behavior of humans and nonhuman primates when task-relevant stimuli are no longer present to the senses. Moreover, complex tasks often require that multiple working memory representations can be flexibly and independently maintained, prioritized, and updated according to changing task demands. Thus far, neural network models of working memory have been unable to offer an integrative account of how such control mechanisms are implemented in the brain and how they can be acquired in a biologically plausible manner. Here, we present WorkMATe, a neural network architecture that models cognitive control over working memory content and learns the appropriate control operations needed to solve complex working memory tasks. Key components of the model include a gated memory circuit that is controlled by internal actions, encoding sensory information through untrained connections, and a neural circuit that matches sensory inputs to memory content. The network is trained by means of a biologically plausible reinforcement learning rule that relies on attentional feedback and reward prediction errors to guide synaptic updates. We demonstrate that the model successfu...Continue Reading

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Mentioned in this Paper

Memory, Short-Term
Nonhuman primate
Neural Network Simulation
Pharmacologic Substance
Recognition (Psychology)
Gene Circuits

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