Learning reconstruction and prediction of natural stimuli by a population of spiking neurons

We propose a model for learning representations of time dependent data with a population of spiking neurons. Encoding is based on a standard spiking neuron model, and the spike timings of the neurons represent the stimulus. Learning is based on the sole principle of maximization of representation accuracy: the stimulus can be decoded from the spike timings with minimum error. Since the encoding is causal, we propose two different representation strategies: The spike timings represent the stimulus either in a predictive manner or by reconstructing past input. We apply the model to speech data and discuss differences between
the emergent representations.