Temporal Complexity and Self-Organization in an Exponential Dense Associative Memory Model

TL;DR

This paper investigates the temporal self-organizing behavior of an exponential Dense Associative Memory model using Temporal Complexity, revealing regimes of complex intermittency and scale-free dynamics linked to learning and memory load.

Contribution

It introduces a TC-based analysis of a stochastic exponential DAM model, highlighting the spontaneous emergence of self-organizing dynamics across parameter ranges.

Findings

Regimes of complex intermittency with scale-free behavior identified.

Self-organizing dynamics emerge in specific noise ranges.

Memory load influences the criticality and self-organization capacity.

Abstract

Dense Associative Memory (DAM) models generalize the classical Hopfield model by incorporating n-body or exponential interactions that greatly enhance storage capacity. While the criticality of DAM models has been largely investigated, mainly within a statistical equilibrium picture, little attention has been devoted to the temporal self-organizing behavior induced by learning. In this work, we investigate the behavior of a stochastic exponential DAM (SEDAM) model through the lens of Temporal Complexity (TC), a framework that characterizes complex systems by intermittent transition events between order and disorder and by scale-free temporal statistics. Transition events associated with birth-death of neural avalanche structures are exploited for the TC analyses and compared with analogous transition events based on coincidence structures. We systematically explore how TC indicators depend on control parameters, i.e., noise intensity and memory load. Our results reveal that the SEDAM model exhibits regimes of complex intermittency characterized by nontrivial temporal correlations and scale-free behavior, indicating the spontaneous emergence of self-organizing dynamics. These regimes emerge in small intervals of noise intensity values, which, in agreement with the extended criticality concept, never shrink to a single critical point. Further, the noise intensity range needed to reach the critical region, where self-organizing behavior emerges, slightly decreases as the memory load increases. This study highlights the relevance of TC as a complementary framework for understanding learning and information processing in artificial and biological neural systems, revealing the link between the memory load and the self-organizing capacity of the network.