Damage explains function in spiking neural networks representing central pattern generator

Objective.Complex biological systems have evolved to control movement dynamics despite noisy and unpredictable inputs and processing delays that necessitate forward predictions. The staple example in vertebrates is the locomotor control emerging from interactions between multiple systems-from passive dynamics of inverted pendulum governing body motion to coupled neural oscillators that integrate predictive forward and sensory feedback signals. These neural dynamic computations are expressed in the rhythmogenic spinal network known as the central pattern generator (CPG). While a system of ordinary differential equations constituting a rate model can accurately reproduce flexor-extensor modulation patterns aligned with experimental data from cats, the equivalent computations performed by thousands of neurons in vertebrates or even in silicon are poorly understood.Approach.We developed a locomotor CPG model expressed as a spiking neural network (SNN) to test how damage affects the distributed computations of a well-defined neural circuit with known dynamics. The SNN-CPG model accurately recreated the input-output relationship of the rate model, describing the modulation of gait phase characteristics.Main Results.The degradation of distributed computation within elements of the SNN-CPG model was further analyzed with progressive simulated lesions. Circuits trained to express flexor or extensor function, with otherwise identical structural organization, were differently affected by lesions mimicking results in experimental observations. The increasing external drive was shown to overcome structural damage and restore function after progressive lesions.Significance.These model results provide theoretical insights into the network dynamics of locomotor control and introduce the concept of degraded computations with applications for restorative technologies.