Learning to make external sensory stimulus predictions using internal correlations in populations of neurons.

ABSTRACT: To compensate for sensory processing delays, the visual system must make predictions to ensure timely and appropriate behaviors. Recent work has found predictive information about the stimulus in neural populations early in vision processing, starting in the retina. However, to utilize this information, cells downstream must be able to read out the predictive information from the spiking activity of retinal ganglion cells. Here we investigate whether a downstream cell could learn efficient encoding of predictive information in its inputs from the correlations in the inputs themselves, in the absence of other instructive signals. We simulate learning driven by spiking activity recorded in salamander retina. We model a downstream cell as a binary neuron receiving a small group of weighted in

SUBMITTER: Sederberg AJ 

PROVIDER: S-EPMC5798322 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature