Infomorphic networks: Locally learning neural networks derived from
  partial information decomposition
Marcel Graetz Abdullah Makkeh Andreas C. Schneider David A. Ehrlich Viola Priesemann Michael Wibral
Understanding the intricate cooperation among individual neurons in
performing complex tasks remains a challenge to this date. In this paper, we
propose a novel type of model neuron that emulates the functional
characteristics of biological neurons by optimizing an abstract local
information processing goal. We have previously formulated such a goal function
based on principles from partial information decomposition (PID). Here, we
present a corresponding parametric local learning rule which serves as the
foundation of "infomorphic networks" as a novel concrete model of neural
networks. We demonstrate the versatility of these networks to perform tasks
from supervised, unsupervised and memory learning. By leveraging the
explanatory power and interpretable nature of the PID framework, these
infomorphic networks represent a valuable tool to advance our understanding of
cortical function.