Attractor Metadynamics in Adapting Neural Networks

TL;DR

This paper introduces the concept of attractor metadynamics, describing how slow parameter changes in neural networks reshape their attractor landscape, providing insights into neural dynamics development.

Contribution

It presents a novel framework for analyzing how slow adaptation processes influence the evolving attractor landscape in neural networks.

Findings

Identification of first- and second-order attractor shifts

Demonstration of attractor landscape evolution as a developmental tool

Application to continuous-time autonomous neural models

Abstract

Slow adaption processes, like synaptic and intrinsic plasticity, abound in the brain and shape the landscape for the neural dynamics occurring on substantially faster timescales. At any given time the network is characterized by a set of internal parameters, which are adapting continuously, albeit slowly. This set of parameters defines the number and the location of the respective adiabatic attractors. The slow evolution of network parameters hence induces an evolving attractor landscape, a process which we term attractor metadynamics. We study the nature of the metadynamics of the attractor landscape for several continuous-time autonomous model networks. We find both first- and second-order changes in the location of adiabatic attractors and argue that the study of the continuously evolving attractor landscape constitutes a powerful tool for understanding the overall development of the neural dynamics.