vahnsin 's Library tagged Cortex → View Popular

04 Sep 07

The primary visual cortex (V1) is pre-wired to facilitate the extraction of behaviorally important visual features. Collinear edge detectors in V1, for instance, mutually enhance each other to improve the perception of lines against a noisy background. The same pre-wiring that facilitates line extraction, however, is detrimental when subjects have to discriminate the brightness of different line segments. How is it possible to improve in one task by unsupervised practicing, without getting worse in the other task?
We propose a general neuronal model showing that perceptual learning can modulate top-down input to V1 in a task-specific way while feedforward and lateral pathways remain intact.
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- In a contemporary neuroscientific version of the empiricist's position, one may argue that the perception of visual objects is always distorted by the nonlinearities in the visual pathway, and in particular by the intrinsic circuitry of primary visual cortex (V1)
- We show in a model that top-down modulation of V1 during unsupervised perceptual learning can suppress intrinsic nonlinearities in V1.
- The brightness of the test bar is overestimated because the underlying neuronal population representing the test bar within V1 is recurrently excited by the corresponding population representing the collinear flanking bar