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Understanding the intricate cooperation among individual neurons inperforming complex tasks remains a challenge to this date. In this paper, wepropose a novel type of model neuron that emulates the functionalcharacteristics of biological neurons by optimizing an abstract localinformation processing goal. We have previously formulated such a goal functionbased on principles from partial information decomposition (PID). Here, wepresent a corresponding parametric local learning rule which serves as thefoundation of "infomorphic networks" as a novel concrete model of neuralnetworks. We demonstrate the versatility of these networks to perform tasksfrom supervised, unsupervised and memory learning. By leveraging theexplanatory power and interpretable nature of the PID framework, theseinfomorphic networks represent a valuable tool to advance our understanding ofcortical function.

Infomorphic networks: Locally learning neural networks derived from partial information decomposition