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.

翻译：理解单个神经元在完成复杂任务时的精细协作至今仍是一个挑战。本文提出一种新型模型神经元，通过优化抽象的局部信息处理目标来模拟生物神经元的功能特性。我们先前已基于部分信息分解（PID）原理构建了此类目标函数，在此我们提出对应的参数化局部学习规则，该规则构成"信息形态网络"这一新型神经网络具体模型的基础。我们展示了这些网络在监督学习、无监督学习和记忆学习任务中的多功能性。通过利用PID框架的解释力和可解释特性，这些信息形态网络成为推动我们对皮层功能理解的宝贵工具。