A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions

arXiv:2306.02149v2 Announce Type: replace-cross
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 …

abstract artificial arxiv challenge cs.it cs.lg cs.ne dynamics framework functions general indeed information math.it network networks performance scale solutions type understanding