Keywords
Color, learning, retina
Abstract
An unsupervised learning model for developing L/M specific wiring at the ganglion cell level would support the research indicating L/M specific wiring at the ganglion cell level (Reid and Shapley, 2002). Removing the contributions to the surround from cells of the same cone type improves the signal-to-noise ratio of the chromatic signals. The unsupervised learning model used is Hebbian associative learning, which strengthens the surround input connections according to the correlation of the output with the input. Since the surround units of the same cone type as the center are redundant with the center, their weights end up disappearing. This process can be thought of as a general mechanism for eliminating unnecessary cells in the nervous system.
Start Date
11-5-2016 2:00 PM
End Date
11-5-2016 2:25 PM
Included in
A Learning Model for L/M Specificity in Ganglion Cells
An unsupervised learning model for developing L/M specific wiring at the ganglion cell level would support the research indicating L/M specific wiring at the ganglion cell level (Reid and Shapley, 2002). Removing the contributions to the surround from cells of the same cone type improves the signal-to-noise ratio of the chromatic signals. The unsupervised learning model used is Hebbian associative learning, which strengthens the surround input connections according to the correlation of the output with the input. Since the surround units of the same cone type as the center are redundant with the center, their weights end up disappearing. This process can be thought of as a general mechanism for eliminating unnecessary cells in the nervous system.