A neural network model for top-down regulation of sensory plasticity

Abstract

Auditory cortex displays remarkable plasticity, but how such plasticity relates to human perceptual learning remains unclear. Here, we present a neural network model that attempts to link the two. The model consists of an encoding layer, a decoding/decision layer, and an interneuron that receives both top-down regulation and bottom-up stimulation. The interneuron, once activated, implements a neural learning mechanism such as reorganization of the tonotopic map or lateral inhibition. During active training, top-down regulation sensitizes the interneuron and initiates the learning process. Depending on the relative influence of top-down regulation and bottom-up stimulation, learning ranges from partially to completely stimulus specific, consistent with empirical observations. For stimulus-specific learning, we have recently shown that passive exposure to an untrained frequency after, but not before active training induces transfer to that frequency. The model displays similar behaviors regardless of the specific neural learning mechanism (remapping or inhibition) implemented. Overall, the simulation results indicate that auditory cortical plasticity is subject to top-down regulation and that the plasticity, once unlocked, can afford perceptual learning with bottom-up stimulation only.