. Biological Cognition–Memory and Learning
. Biological Cognition–Memory and Learning
This chapter is devoted to consideration of adaptive processes in neural processing. This adaptation includes both weight changes (what is usually meant by ‘learning’), and dynamic stability of network activity states (what is usually taken to be a model of ‘memory’). A serial working memory model is described in detail, that exploits the syntactic representations of the SPA. In addition, a biologically detailed spike-based learning rule is presented and applied to learning arbitrary vector functions, reinforcement learning, and explaining STDP effects. Finally, this rule is applied to a network that explains the results of the Wason Card Selection task. Tutorial: Learning in Nengo
Keywords: learning, working memory, STDP, reinforcement learning, adaptation, Wason Card task
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