A Biologically Plausible Learning Rule for Perceptual Systems of organisms that Maximize Mutual Information
This work addresses the challenge of creating realistic learning mechanisms for perceptual systems in organisms, though it appears incremental as it builds on the established Infomax principle.
The authors tackled the problem of implementing the Infomax principle for perceptual systems by developing a biologically plausible learning rule that is local, spike-based, and continuous-time, enabling accurate maximization of mutual information between neural coding and sensory signals.
It is widely believed that the perceptual system of an organism is optimized for the properties of the environment to which it is exposed. A specific instance of this principle known as the Infomax principle holds that the purpose of early perceptual processing is to maximize the mutual information between the neural coding and the incoming sensory signal. In this article, we present a method to implement this principle accurately with a local, spike-based, and continuous-time learning rule.