| Plastic spiking neural networks are synthesized for phototatic robots using evolutionary techniques. Synaptic plasticity asymmetrically depends on the precise relative timing between presynaptic and postsynapti spike at the millisecond range and on longer term activity dependent regulatory scaling. Comparative studies have been carried out for different kinds of plastic neural network with low and high levels neural noise. In all cases, the evolved contollers are highly robust againtst internal synaptic decay and other perturbations. The importance of the precise timing of spike in denomic changes at the mesoscale due to brusting, but during periods of high activity they are finely regulated at the microscale by synchronous or entrained firing. Spike train randomization result 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 trans, demonstrating the counterintuitive results that plasticity, which is dependent of precise spike timing, can work even in its absecnce, provided the behavioral strategies make use of robust longer term invariants of sensorimotor interactive. A comparison with a rate-pased model of synaptic plasticity show that under similiarly 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. |