Quadratic leaky integrate-and-fire neural network tuned with an evolution-strategy for a simulated 3D biped walking controller

This paper presents the results of experiments in applying a spiking neural network to control the locomotion of a simulated biped robot. The neural model used in simulations was developed to allow for an analytic solution to a neuron's fire time, while maintaining a non-instant post-synaptic potential rise time. The synaptic weights and delays were tuned using an evolution-strategy. Simulation experiments demonstrate that already within about two thousand generations the biped is able to acquire a dynamic walk which allows it to walk upright for several metres.