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 networks. We demonstrate the versatility of these networks to perform tasks from supervised, unsupervised and memory learning. By leveraging the interpretable nature of the PID framework, infomorphic networks represent a valuable tool to advance our understanding of the intricate structure of local learning.

翻译：尽管生物和人工网络表现出令人瞩目的性能，但对其局部学习动态如何促进网络级任务解决方案的直观理解至今仍是一个挑战。将学习过程推向更局部尺度的努力确实带来了宝贵的见解，然而，目前仍缺乏一种兼具可解释性与跨任务适应性的通用构建方法来描述局部学习目标。我们此前已针对具有区室结构的模型神经元，提出了一种高度适应且可解释的局部信息处理目标。基于局部信息分解（PID）的最新进展，我们在此推导出相应的参数化局部学习规则，从而得以引入“信息形态”神经网络。我们展示了这些网络在监督学习、无监督学习和记忆学习等任务中的多功能性。通过利用PID框架的可解释特性，信息形态网络为增进我们对局部学习复杂结构的理解提供了有价值的工具。