Built to Last: Functional and structural mechanisms in the moth olfactory network mitigate effects of neural injury

TL;DR

This study investigates how structural and functional motifs like Hebbian learning, noise, and parallel connections enhance the robustness of the moth olfactory network against neural injuries, demonstrating their role as adaptive injury mitigation mechanisms.

Contribution

It reveals that specific network motifs in the moth olfactory system help mitigate injury effects, highlighting their potential as adaptive mechanisms for maintaining neural function.

Findings

Motifs reduce injury impact on neural responses.

Motifs restore responses to pre-injury levels.

Robustness is a key design principle in neural systems.

Abstract

Most organisms suffer neuronal damage throughout their lives, which can impair performance of core behaviors. Their neural circuits need to maintain function despite injury, which in particular requires preserving key system outputs. In this work, we explore whether and how certain structural and functional neuronal network motifs act as injury mitigation mechanisms. Specifically, we examine how (i) Hebbian learning, (ii) high levels of noise, and (iii) parallel inhibitory and excitatory connections contribute to the robustness of the olfactory system in the Manduca sexta moth. We simulate injuries on a detailed computational model of the moth olfactory network calibrated to in vivo data. The injuries are modeled on focal axonal swellings, a ubiquitous form of axonal pathology observed in traumatic brain injuries and other brain disorders. Axonal swellings effectively compromise spike train propagation along the axon, reducing the effective neural firing rate delivered to downstream neurons. All three of the network motifs examined significantly mitigate the effects of injury on readout neurons, either by reducing injury's impact on readout neuron responses or by restoring these responses to pre-injury levels. These motifs may thus be partially explained by their value as adaptive mechanisms to minimize the functional effects of neural injury. More generally, robustness to injury is a vital design principle to consider when analyzing neural systems.