Spike-based models of neural computation

Neurons compute mainly with action potentials or “spikes”, which are stereotypical electrical impulses. Over the last century, the operating function of neurons has been mainly described in terms of firing rates, with the timing of spikes bearing little information. More recently, experimental evidence and theoretical studies have shown that the relative spike timing of inputs has an important effect both on computation and learning in neurons. This evidence has triggered considerable interest for spiking neuron models in computational neuroscience, but the theory of computation in those models is sparse. Spiking neuron models are hybrid dynamical systems, combining differential equations and discrete events. I have developed specific theoretical approaches to study this particular type of models. In particular, two specific properties seem to be relevant for computation: spiking models can encode time-varying inputs into trains of precisely timed spikes, and they are more likely fire to when input spike trains are tightly correlated. To simulate spiking models efficiently, we have developed specific techniques, which can now be used in an open source simulator (Brian). These theoretical and methodological investigations now allow us to address spike-based modeling at a more global and functional level. Since the mechanisms of synaptic plasticity tend to favor synchronous inputs, I propose to investigate computational mechanisms based on neural synchrony in sensory modalities.