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.

本文提出了一种新型的模型神经元，通过优化抽象本地信息处理目标模拟生物神经元的功能特征，并且提出了一种相应的参数本地学习规则作为神经网络的基础，称为 “infomorphic 网络”，通过利用 PID 框架的解释能力和可解释性，这些 infomorphic 网络代表了一种有价值的工具来推进我们对皮层功能的理解。

信息形态网络：基于部分信息分解导出的本地学习神经网络

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

Predictive coding offers a potentially unifying account of cortical function
-- postulating that the core function of the brain is to minimize prediction
errors with respect to a generative model of the world. The theory is closely
related to the Bayesian brain framework and, over the last two decades, has
gained substantial influence in the fields of theoretical and cognitive
neuroscience. A large body of research has arisen based on both empirically
testing improved and extended theoretical and mathematical models of predictive
coding, as well as in evaluating their potential biological plausibility for
implementation in the brain and the concrete neurophysiological and
psychological predictions made by the theory. Despite this enduring popularity,
however, no comprehensive review of predictive coding theory, and especially of
recent developments in this field, exists. Here, we provide a comprehensive
review both of the core mathematical structure and logic of predictive coding,
thus complementing recent tutorials in the literature. We also review a wide
range of classic and recent work within the framework, ranging from the
neurobiologically realistic microcircuits that could implement predictive
coding, to the close relationship between predictive coding and the widely-used
backpropagation of error algorithm, as well as surveying the close
relationships between predictive coding and modern machine learning techniques.

该论文提供了对预测编码理论的全面评估，包括其核心数学结构和逻辑以及在神经科学领域的实现和应用。该理论适用于皮层功能的统一解释，主张大脑的核心功能是通过一个世界的生成模型来减少与预测误差。

预测编码：理论和实验综述

Predictive Coding: a Theoretical and Experimental Review

Empirical evidence demonstrates that every region of the neocortex represents
information using sparse activity patterns. This paper examines Sparse
Distributed Representations (SDRs), the primary information representation
strategy in Hierarchical Temporal Memory (HTM) systems and the neocortex. We
derive a number of properties that are core to scaling, robustness, and
generalization. We use the theory to provide practical guidelines and
illustrate the power of SDRs as the basis of HTM. Our goal is to help create a
unified mathematical and practical framework for SDRs as it relates to cortical
function.

研究了神经皮层表征信息的稀疏分布式表示方法（Sparse Distributed Representations，SDRs）的核心属性对于缩放、鲁棒性和泛化的作用，并提供了实用指南，说明将 SDRs 作为 HTM 的基础的潜在和理论意义。