Allee Synaptic Plasticity and Memory

TL;DR

This paper introduces an Allee-based nonlinear synaptic plasticity model that improves memory retention, noise robustness, and synaptic stability, with extensions for dynamic environments, advancing understanding in neural adaptation and AI.

Contribution

It presents a novel Allee-inspired plasticity model with biologically motivated stabilization, extending it with time-dependent dynamics for enhanced memory performance.

Findings

Enhanced noise robustness and stability over classical models

Increased memory capacity and retrieval reliability

Improved dynamic adaptation with time-dependent extensions

Abstract

Neural plasticity is fundamental to memory storage and retrieval in biological systems, yet existing models often fall short in addressing noise sensitivity and unbounded synaptic weight growth. This paper investigates the Allee-based nonlinear plasticity model, emphasizing its biologically inspired weight stabilization mechanisms, enhanced noise robustness, and critical thresholds for synaptic regulation. We analyze its performance in memory retention and pattern retrieval, demonstrating increased capacity and reliability compared to classical models like Hebbian and Oja's rules. To address temporal limitations, we extend the model by integrating time-dependent dynamics, including eligibility traces and oscillatory inputs, resulting in improved retrieval accuracy and resilience in dynamic environments. This work bridges theoretical insights with practical implications, offering a robust framework for modeling neural adaptation and informing advances in artificial intelligence and neuroscience.