标题： 通过循环神经网络中的互信息优化出现功能分化结构 显示英文标题

标题： Emergence of Functionally Differentiated Structures via Mutual Information Optimization in Recurrent Neural Networks

摘要： 大脑中的功能分化随着不同区域的专业化而出现，这对于理解大脑作为一个复杂系统的行为至关重要。 先前的研究使用具有特定约束的人工神经网络对这一过程进行了建模。 在此，我们提出了一种新方法，通过互信息神经估计最小化神经子群之间的互信息，从而在循环神经网络中诱导功能分化。 我们将该方法应用于一个2位工作记忆任务和一个涉及Lorenz和Rössler时间序列的混沌信号分离任务。 对网络性能、相关模式和权重矩阵的分析表明，互信息最小化在实现高任务性能的同时，也表现出清晰的功能模块性和适度的结构模块性。 重要的是，我们的结果表明，通过相关结构测量的功能分化比由突触权重定义的结构模块性更早出现。 这表明功能专业化先于并可能驱动发育中的神经网络内的结构重组。 我们的发现为信息论原则如何在人工和生物大脑发育过程中支配专门功能和模块化结构的出现提供了新的见解。 显示英文摘要

摘要： Functional differentiation in the brain emerges as distinct regions specialize and is key to understanding brain function as a complex system. Previous research has modeled this process using artificial neural networks with specific constraints. Here, we propose a novel approach that induces functional differentiation in recurrent neural networks by minimizing mutual information between neural subgroups via mutual information neural estimation. We apply our method to a 2-bit working memory task and a chaotic signal separation task involving Lorenz and R\"ossler time series. Analysis of network performance, correlation patterns, and weight matrices reveals that mutual information minimization yields high task performance alongside clear functional modularity and moderate structural modularity. Importantly, our results show that functional differentiation, which is measured through correlation structures, emerges earlier than structural modularity defined by synaptic weights. This suggests that functional specialization precedes and probably drives structural reorganization within developing neural networks. Our findings provide new insights into how information-theoretic principles may govern the emergence of specialized functions and modular structures during artificial and biological brain development.