URLanisms are exposed to neural damage throughout their lives, which can reduce their ability to carry out core behavioral tasks . Their neural circuits need to maintain functionality despite injury, which often requires that key readout signals from the system be preserved . In this work, we explore whether and how certain structural and functional  network motifs act as injury mitigation mechanisms by protecting these readouts . Specifically, we examine how (i) Hebbian learning, (ii) high levels of noise, and (iii) presence of parallel inhibitory and excitatory connections contribute to  robustness of the olfactory system in the Manduca sexta moth. We simulate injuries on a detailed computational model of the moth olfactory network in which structures-under-test are parametrically varied. Neuronal impairments are modeled as focal axonal swellings, an axonal pathology observed across all severities of traumatic brain injuries and present in other leading brain disorders. Axonal swellings effectively compromise spike train propagation along the axon and consequently reduce 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 through reinforcement learning . These motifs may thus be partially explained by their value as adaptive mechanisms to minimize the functional effects of neural injury. More generally, these experiments suggest that robustness to injury is a vital design principle to consider when analyzing biological neural systems. Indeed, it is possible that some neural structures and mechanisms, including the ability to learn, are best understood as evolutionary solutions to the challenge of maintaining system function despite injury.
URLanisms 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. Indeed, it is possible that some neural structures and mechanisms, including the ability to learn, are best understood as evolutionary solutions to the challenge of maintaining system function despite injury.
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are exposed to neural
suffer neuronal
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reduce their ability to carry out core behavioral tasks
impair performance of core behaviors
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functionality
function
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often requires that key readout signals from the system be preserved
in particular requires preserving key system outputs
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neuronal
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by protecting these readouts
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presence of
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the
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in which structures-under-test are parametrically varied. Neuronal impairments are modeled as
calibrated to in vivo data. The injuries are modeled on
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an
a ubiquitous form of
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across all severities of
in
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present in other leading
other
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and consequently reduce
, reducing
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through reinforcement learning
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these experiments suggest that
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biological