The brain effortlessly extracts latent causes of stimuli, but how it does
this at the network level remains unknown. Most prior attempts at this problem
proposed neural networks that implement independent component analysis which
works under the limitation that latent causes are mutually independent. Here,
we relax this limitation and propose a biologically plausible neural network
that extracts correlated latent sources by exploiting information about their
domains. To derive this network, we choose maximum correlative information
transfer from inputs to outputs as the separation objective under the
constraint that the outputs are restricted to their presumed sets. The online
formulation of this optimization problem naturally leads to neural networks
with local learning rules. Our framework incorporates infinitely many source
domain choices and flexibly models complex latent structures. Choices of
simplex or polytopic source domains result in networks with piecewise-linear
activation functions. We provide numerical examples to demonstrate the superior
correlated source separation capability for both synthetic and natural sources.

提出一种基于神经网络的生物学可行模型，它通过利用源域信息提取相关的潜在因素。该模型采用最大相关信息转移作为分离目标，遵循输出限制约束，从而具有本地学习规则。使用不同的源域选择，可灵活地建模复杂的潜在结构，从而具有出色的相关源分离能力。

基于相关信息最大化的仿生神经网络进行相关源分离

Correlative Information Maximization Based Biologically Plausible Neural  Networks for Correlated Source Separation

We consider the classical two-encoder multiterminal source coding problem
where distortion is measured under logarithmic loss. We provide a single-letter
characterization of the achievable rate distortion region for arbitrarily
correlated sources with finite alphabets. In doing so, we also give the rate
distortion region for the $m$-encoder CEO problem (also under logarithmic
loss). Several applications and examples are given.

本文针对两个编码器多终端源编码问题进行研究，在对数损失测量下，提供了对于任意相关源有限字母表的可实现速率失真区域的单字母表达。在此过程中，还给出了 $m$ 个编码器首席执行官的速率失真区域（也在对数损失下）。并提供了多个应用和示例。