Evolution has yielded a diverse set of neurons with varying morphologies and physiological properties that impact their processing of temporal information. In addition, it is known empirically that spike timing is a significant factor in neural computations. However, despite these two observations, most neural network models deal with spatially structured inputs with synchronous time steps, while restricting variation to parameters like weights and biases. In this study, we investigate the relevance of adapting temporal parameters, like time constants and delays, in feedforward networks that map spatio-temporal spike patterns. In this context, we show that networks with richer potential dynamics are able to more easily and robustly learn tasks with temporal structure. Indeed, when adaptation was restricted to weights, networks were unable to solve most problems. We also show strong interactions between the various parameters and the advantages of adapting temporal parameters when dealing with noise in inputs and weights, which might prove useful in neuromorphic hardware design.

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