Neural Network Information Flow Topology Representation

Abstract

This paper investigates the representation of information flow within neural networks, proposing that the transmission of information is not a random process but rather governed by a specific topological structure. The core argument centers on the idea that the network's topology dictates the pathways through which information propagates and, consequently, influences its processing capabilities. We introduce a methodology for constructing a neural network information flow topology graph based on analyzing the strength, delay, and direction of connections between neurons. Utilizing topological analysis techniques, this framework aims to decipher the operational mechanisms of neural networks by interpreting the network's structure as a representation of information flow. This approach distinguishes itself from traditional neural network research, which frequently focuses on individual neuron activation without considering the overarching network topology. The research emphasizes a holistic view, recognizing the importance of the network's interconnectedness in shaping information processing. The methodology presented offers a novel perspective for understanding complex neural systems, potentially leading to improved network design and analysis.