Deterministic excitable media under Poisson drive: power law responses, spiral waves and dynamic range

TL;DR

This study investigates how excitable media respond to Poisson stimuli, confirming power law behavior in low-stimulus regimes for certain models and showing that self-sustained activity affects dynamic range, with implications for neural coding.

Contribution

It verifies the power law response prediction in deterministic excitable lattices and explores how self-sustained activity influences dynamic range and neural coding.

Findings

Power law response confirmed for d=1,2,3 under certain conditions.

Dynamic range increases with network dimensionality.

Self-sustained activity limits power law behavior and maximizes dynamic range at critical coupling.

Abstract

When each site of a spatially extended excitable medium is independently driven by a Poisson stimulus with rate h, the interplay between creation and annihilation of excitable waves leads to an average activity F. It has recently been suggested that in the low-stimulus regime (h ~ 0) the response function F(h) of hypercubic deterministic systems behaves as a power law, F ~ h^m. Moreover the response exponent m has been predicted to depend only on the dimensionality d of the lattice, m = 1/(1+d) [T. Ohta and T. Yoshimura, Physica D 205, 189 (2005)]. In order to test this prediction, we study the response function of excitable lattices modeled by either coupled Morris-Lecar equations or Greenberg-Hastings cellular automata. We show that the prediction is verified in our model systems for d = 1, 2, and 3, provided that a minimum set of conditions is satisfied. Under these conditions, the dynamic range - which measures the range of stimulus intensities that can be coded by the network activity - increases with the dimensionality d of the network. The power law scenario breaks down, however, if the system can exhibit self-sustained activity (spiral waves). In this case, we recover a scenario that is common to probabilistic excitable media: as a function of the conductance coupling G among the excitable elements, the dynamic range is maximized precisely at the critical value G_c above which self-sustained activity becomes stable. We discuss the implications of these results in the context of neural coding.