Relaxation, closing probabilities and transition from oscillatory to chaotic attractors in asymmetric neural networks

TL;DR

This paper investigates the transition from oscillatory to chaotic attractors in asymmetric neural networks, identifying a critical symmetry point and analyzing how relaxation dynamics and cycle lengths depend on system asymmetry.

Contribution

It provides an analytic and numerical study of the phase transition at symmetry eta=0.33, revealing how cycle lengths and basin weights change with asymmetry in neural networks.

Findings

A sharp transition at eta=0.33 from fixed points to long cycles.

Cycle length scales exponentially with system size in the chaotic regime.

Relaxation dynamics are faster with increased asymmetry, acting like an effective temperature.

Abstract

Attractors in asymmetric neural networks with deterministic parallel dynamics were shown to present a "chaotic" regime at symmetry eta < 0.5, where the average length of the cycles increases exponentially with system size, and an oscillatory regime at high symmetry, where the typical length of the cycles is 2. We show, both with analytic arguments and numerically, that there is a sharp transition, at a critical symmetry $\e_c=0.33$, between a phase where the typical cycles have length 2 and basins of attraction of vanishing weight and a phase where the typical cycles are exponentially long with system size, and the weights of their attraction basins are distributed as in a Random Map with reversal symmetry. The time-scale after which cycles are reached grows exponentially with system size $N$, and the exponent vanishes in the symmetric limit, where $T\propto N^{2/3}$. The transition can be related to the dynamics of the infinite system (where cycles are never reached), using the closing probabilities as a tool. We also study the relaxation of the function $E(t)=-1/N\sum_i |h_i(t)|$, where $h_i$ is the local field experienced by the neuron $i$. In the symmetric system, it plays the role of a Ljapunov function which drives the system towards its minima through steepest descent. This interpretation survives, even if only on the average, also for small asymmetry. This acts like an effective temperature: the larger is the asymmetry, the faster is the relaxation of $E$, and the higher is the asymptotic value reached. $E$ reachs very deep minima in the fixed points of the dynamics, which are reached with vanishing probability, and attains a larger value on the typical attractors, which are cycles of length 2.