language: en

Neuronal Goals: Efficient Coding and Coincidence Detection

Nathan Intrator
School of Mathematical Sciences Tel Aviv University
nin@cns.brown.edu

Abstract| Barlow's seminal work on minimal entropy codes and unsupervised learning is reiterated. In particular, the need to transmit the probability of events is put in a practical neuronal framework for detecting suspicious events. A variant of the BCM learning rule [15] is presented together with some mathematical results suggesting optimal minimal entropy coding.

Key words: Sparse coding, Non-Gaussian distributions, BCM Theory, Minimal Entropy

1 Introduction