Spike-Timing Dependent Plasticity for Evolved Robots

Abstract

Plastic spiking neural networks are synthesized for phototactic robots using evolutionary techniques. Synaptic plasticity asymmetrically depends on the precise relative timing between presynaptic and postsynaptic spikes at the millisecond range and on longer-term activity-dependent regulatory scaling. Comparative studies have been carried out for different kinds of plastic neural networks with low and high levels of neural noise. In all cases, the evolved controllers are highly robust against internal synaptic decay and other perturbations. The importance of the precise timing of spikes is demonstrated by randomizing the spike trains. In the low neural noise scenario, weight values undergo rhythmic changes at the mesoscale due to bursting, but during periods of high activity they are finely regulated at the microscale by synchronous or entrained firing. Spike train randomization results in loss of performance in this case. In contrast, in the high neural noise scenario, robots are robust to loss of information in the timing of the spike trains, demonstrating the counterintuitive results that plasticity, which is dependent on precise spike timing, can work even in its absence, provided the behavioral strategies make use of robust longer-term invariants of sensorimotor interaction. A comparison with a rate-based model of synaptic plasticity shows that under similarly noisy conditions, asymmetric spike-timing dependent plasticity achieves better performance by means of efficient reduction in weight variance over time. Performance also presents negative sensitivity to reduced levels of noise, showing that random firing has a functional value.