Neural Computation
A reinforcement learning framework for spiking networks with dynamic synapses
Computational Intelligence and Neuroscience
Symbol representations in evolving droplet computers
UCNC'12 Proceedings of the 11th international conference on Unconventional Computation and Natural Computation
A new class of metrics for spike trains
Neural Computation
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A variety of (dis)similarity measures for one-dimensional point processes (e.g., spike trains) are investigated, including the Victor-Purpura distance metric, the van Rossum distance metric, the Schreiber et al. similarity measure, the Hunter-Milton similarity measure, the event synchronization proposed by Quiroga, and the stochastic event synchrony measures (SES) recently proposed by Dauwels et al. By analyzing surrogate data, it is demonstrated that most measures are not able to distinguish timing precision and event reliability, i.e., they depend on both aspects of synchrony. There are two exceptions: with appropriate choice of parameters, event synchronization quantifies event reliability, independently of timing precision; the two SES parameters quantify both timing precision and event reliability separately. Before one can apply the (dis)similarity measures (with the exception of SES), one needs to determine potential lags between the point processes. On the other hand, SES deals with lags in a natural and direct way, and therefore, the SES similarity measures are robust to lags. As an illustration, neuronal spike data generated by the Morris-Lecar neuron model is considered.