Neuromimetic Dynamic Networks with Hebbian Learning

TL;DR

This paper introduces a biologically inspired neural network model with dynamic connections governed by Hebbian learning, demonstrating stability and controllability through formal analysis and simulations.

Contribution

It presents a novel neuromimetic network model with Hebbian learning rules and provides formal proofs of its stability and controllability properties based on graph theory.

Findings

Model exhibits boundedness and stability.

Structural controllability is achieved with generalized sym-cactus topology.

Simulations validate neural dynamics capture.

Abstract

Leveraging recent advances in neuroscience and control theory, this paper presents a neuromimetic network model with dynamic symmetric connections governed by Hebbian learning rules. Formal analysis grounded in graph theory and classical control establishes that this biologically plausible model exhibits boundedness, stability, and structural controllability given a generalized sym-cactus structure with multiple control nodes. We prove the necessity of this topology when there are distributed control inputs. Simulations using a 14-node generalized sym-cactus network with two input types validate the model's effectiveness in capturing key neural dynamics.