Distributed delays stabilize neural feedback systems

TL;DR

This paper investigates how distributed delays in neural feedback loops, such as in the avian optic tectum, can enhance stability and influence convergence dynamics, with implications for understanding neural system robustness.

Contribution

It demonstrates that distributed delays improve stability and convergence in neural feedback systems, extending prior mathematical analysis to biological neural circuits.

Findings

Distributed delays increase system stability.

Delays lead to faster convergence to fixed points.

Broader delay distributions expand stable delay ranges.

Abstract

We consider the effect of distributed delays in neural feedback systems. The avian optic tectum is reciprocally connected with the nucleus isthmi. Extracellular stimulation combined with intracellular recordings reveal a range of signal delays from 4 to 9 ms between isthmotectal elements. This observation together with prior mathematical analysis concerning the influence of a delay distribution on system dynamics raises the question whether a broad delay distribution can impact the dynamics of neural feedback loops. For a system of reciprocally connected model neurons, we found that distributed delays enhance system stability in the following sense. With increased distribution of delays, the system converges faster to a fixed point and converges slower toward a limit cycle. Further, the introduction of distributed delays leads to an increased range of the average delay value for which the system's equilibrium point is stable. The enhancement of stability with increasing delay distribution is caused by the introduction of smaller delays rather than the distribution per se.