Neural networks and perceptual learning.
Sensory perception is a learned trait. The brain strategies we use to perceive the world are constantly modified by experience. With practice, we subconsciously become better at identifying familiar objects or distinguishing fine details in our environment. Current theoretical models simulate some properties of perceptual learning, but neglect the underlying cortical circuits. Future neural network models must incorporate the top-down alteration of cortical function by expectation or perceptual tasks. These newly found dynamic processes are challenging earlier views of static and feedforward processing of sensory information.
Tsodyks
Misha
M
Department of Neurobiology, Weizmann Institute, Rehovot 76100, Israel. misha@weizmann.ac.il
Gilbert
Charles
C
D001921:Brain / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009473:Neuronal Plasticity / Q000502:physiology; D010465:Perception / Q000502:physiology*