Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain
Dopamine-dependent plasticity of corticostriatal synapses
Neural Networks - Computational models of neuromodulation
Type i membranes, phase resetting curves, and synchrony
Neural Computation
Simple model of spiking neurons
IEEE Transactions on Neural Networks
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An essential component of skill acquisition is learning the environmental conditions in which that skill is relevant. This article proposes and tests a neurobiologically detailed theory of how such learning is mediated. The theory assumes that a key component of this learning is provided by the cholinergic interneurons in the striatum known as tonically active neurons (TANs). The TANs are assumed to exert a tonic inhibitory influence over cortical inputs to the striatum that prevents the execution of any striatal-dependent actions. The TANs learn to pause in rewarding environments, and this pause releases the striatal output neurons from this inhibitory effect, thereby facilitating the learning and expression of striatal-dependent behaviors. When rewards are no longer available, the TANs cease to pause, which protects striatal learning from decay. A computational version of this theory accounts for a variety of single-cell recording data and some classic behavioral phenomena, including fast reacquisition after extinction.