Challenged with the discrepancy between theory and data, we take a fresh view of the subject with the proposal that the randomness associated with neuronal outputs is certain to have a purpose. In particular, we model neurons as probabilistic devices that not only compute probabilities but also fire probabilistically to signal their computations. According to our model, signaling of probabilities is done by having cells with similar receptive fields' fire synchronously to achieve fast communication; this is consistent with observations of neurons coding as ensembles and topographic map organization. Our proposal of probabilistic, distributed synchronous volleys as a neural signaling strategy not only accounts for variable neural responses, but also provides the advantage of robust and fast computation. Furthermore, the involvement of probabilistic firing and distributed coding explicate how synchronous firing can appear to be a rate code, accounting for the vast amount of data supporting a rate code assumption.
Any neural signaling model must support cortical computation in a biologically realistic fashion. Going beyond simply addressing the role of spikes in cortical cells' communication, we show that our distributed synchrony model can be implemented in a predictive coding framework and can be used to learn structures in the natural environment. Trained with patches from natural images, our model V1 cells develop localized and oriented receptive fields, consistent with V1 simple cell properties. Unlike most cortical computation models, our predictive coding model makes use of single spikes, instead of abstracting spikes away with analog quantities. This close resemblance to biology makes our model well suited for guiding experimental research.