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

Despite its great success, backpropagation has certain limitations that
necessitate the investigation of new learning methods. In this study, we
present a biologically plausible local learning rule that improves upon Hebb's
well-known proposal and discovers unsupervised features by local competitions
among neurons. This simple learning rule enables the creation of a forward
learning paradigm called activation learning, in which the output activation
(sum of the squared output) of the neural network estimates the likelihood of
the input patterns, or "learn more, activate more" in simpler terms. For
classification on a few small classical datasets, activation learning performs
comparably to backpropagation using a fully connected network, and outperforms
backpropagation when there are fewer training samples or unpredictable
disturbances. Additionally, the same trained network can be used for a variety
of tasks, including image generation and completion. Activation learning also
achieves state-of-the-art performance on several real-world datasets for
anomaly detection. This new learning paradigm, which has the potential to unify
supervised, unsupervised, and semi-supervised learning and is reasonably more
resistant to adversarial attacks, deserves in-depth investigation.

本文提出了一种生物可行的局部学习规则，利用神经元之间的局部竞争完成无监督特征发现，使用称为激活学习的正向学习范例对样本进行分类，在一些小数据集上能够与反向传播相媲美；同时在实现异常检测时表现出色。

通过本地竞争来实现激活学习

Activation Learning by Local Competitions

The neural plausibility of backpropagation has long been disputed, primarily
for its use of non-local weight transport $-$ the biologically dubious
requirement that one neuron instantaneously measure the synaptic weights of
another. Until recently, attempts to create local learning rules that avoid
weight transport have typically failed in the large-scale learning scenarios
where backpropagation shines, e.g. ImageNet categorization with deep
convolutional networks. Here, we investigate a recently proposed local learning
rule that yields competitive performance with backpropagation and find that it
is highly sensitive to metaparameter choices, requiring laborious tuning that
does not transfer across network architecture. Our analysis indicates the
underlying mathematical reason for this instability, allowing us to identify a
more robust local learning rule that better transfers without metaparameter
tuning. Nonetheless, we find a performance and stability gap between this local
rule and backpropagation that widens with increasing model depth. We then
investigate several non-local learning rules that relax the need for
instantaneous weight transport into a more biologically-plausible "weight
estimation" process, showing that these rules match state-of-the-art
performance on deep networks and operate effectively in the presence of noisy
updates. Taken together, our results suggest two routes towards the discovery
of neural implementations for credit assignment without weight symmetry:
further improvement of local rules so that they perform consistently across
architectures and the identification of biological implementations for
non-local learning mechanisms.

本文讨论了反向传播算法的神经可行性，研究了一种竞争性的局部学习规则和几种使权重传输过程更生物学可行的非局部学习规则，并提出了两条无需权重对称的神经实现学分分配的路径。