Part of Advances in Neural Information Processing Systems 10 (NIPS 1997)
Brian Blais, Nathan Intrator, Harel Shouval, Leon Cooper
We study several statistically and biologically motivated learning rules using the same visual environment, one made up of natural scenes, and the same single cell neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. Included in these rules are kurtosis and skewness maximization, the quadratic form of the BCM learning rule, and single cell ICA. Using a structure removal method, we demonstrate that receptive fields developed using these rules de(cid:173) pend on a small portion of the distribution. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when the distribution contains kurtotic directions, although the BCM modification equations are compu(cid:173) tationally simpler.
B. S. Blais, N. Intrator, H. Shouval and L N. Cooper