A general framework for interpretable neural learning based on local information-theoretic goal functions.

ABSTRACT: Despite the impressive performance of biological and artificial networks, an intuitive understanding of how their local learning dynamics contribute to network-level task solutions remains a challenge to this date. Efforts to bring learning to a more local scale indeed lead to valuable insights, however, a general constructive approach to describe local learning goals that is both interpretable and adaptable across diverse tasks is still missing. We have previously formulated a local information processing goal that is highly adaptable and interpretable for a model neuron with compartmental structure. Building on recent advances in Partial Information Decomposition (PID), we here derive a corresponding parametric local learning rule, which allows us to introduce "infomorphic" neural networ

SUBMITTER: Makkeh A 

PROVIDER: S-EPMC11912414 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature