Damage explains function in spiking neural networks representing central pattern generator

Complex biological systems evolved to control dynamics in the presence of noisy and often unpredictable inputs. The staple example is locomotor control, which is vital for survival. Control of locomotion results 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. The neural dynamic computations are expressed in the rhythmogenic spinal network termed the central pattern generator (CPG). While a system of ordinary differential equations or a rate model is typically “good enough” to describe the CPG function, the computations performed by thousands of neurons in vertebrates are poorly understood. To study the distributed computations of a well-defined neural dynamic system, we developed a CPG model for gait expressed with the spiking neural networks (SNN). The SNN-CPG model faithfully recreated the input-output relationship of the rate model, describing the modulation of gait phase characteristics. The degradation of distributed computation within elements of the SNN-CPG model was further studied with “lesion” experiments. We found that lesioning flexor or extensor elements, with otherwise identical structural organization of reciprocal networks, affected differently the overall CPG computation. This result mimics experimental observations. Moreover, the increasing general excitability within the network can compensate for the loss of function after progressive lesions. This observation may explain the response to spinal stimulation and propose a novel theoretical framework for degraded computations and their applications within restorative technologies.