Stochastic facilitation in heteroclinic communication channels

Biological neural systems encode and transmit information as patterns of activity tracing complex trajectories in high-dimensional state-spaces, inspiring alternative paradigms of information processing. Heteroclinic networks, naturally emerging in artificial neural systems, are networks of saddles in state-space that provide a transparent approach to generate complex trajectories via controlled switches among interconnected saddles. External signals induce specific switching sequences, thus dynamically encoding inputs as trajectories. Recent works have focused either on computational aspects of heteroclinic networks, i.e. Heteroclinic Computing, or their stochastic properties under noise. Yet, how well such systems may transmit information remains an open question. Here we investigate the information transmission properties of heteroclinic networks, studying them as communication channels. Choosing a tractable but representative system exhibiting a heteroclinic network, we investigate the mutual information rate (MIR) between input signals and the resulting sequences of states as the level of noise varies. Intriguingly, MIR does not decrease monotonically with increasing noise. Intermediate noise levels indeed maximize the information transmission capacity by promoting an increased yet controlled exploration of the underlying network of states. Complementing standard stochastic resonance, these results highlight the constructive effect of stochastic facilitation (i.e. noise-enhanced information transfer) on heteroclinic communication channels and possibly on more general dynamical systems exhibiting complex trajectories in state-space.

READ FULL TEXT

page 1

page 7

page 13

page 14

research
03/07/2022

Path Weight Sampling: Exact Monte Carlo Computation of the Mutual Information between Stochastic Trajectories

Most natural and engineered information-processing systems transmit info...
research
01/29/2019

Capacity of Control for Stochastic Dynamical Systems Perturbed by Mixed Fractional Brownian Motion with Delay in Control

In this paper, we discuss the relationships between capacity of control ...
research
11/06/2019

Conditional Mutual Information Neural Estimator

Several recent works in communication systems have proposed to leverage ...
research
06/03/2016

Property-driven State-Space Coarsening for Continuous Time Markov Chains

Dynamical systems with large state-spaces are often expensive to thoroug...
research
01/07/2020

Numerical computations of geometric ergodicity for stochastic dynamics

A probabilistic approach of computing geometric rate of convergence of s...
research
08/12/2019

Classes of Full-Duplex Channels with Capacity Achieved Without Adaptation

Full-duplex communication allows a terminal to transmit and receive sign...
research
11/15/2022

Emergence of a stochastic resonance in machine learning

Can noise be beneficial to machine-learning prediction of chaotic system...

Please sign up or login with your details

Forgot password? Click here to reset