A Model of Predictive Coding based on Spike Timing

Several decades of research have made many advances towards the goal of interpreting the neural spike train but a comprehensive understanding remains elusive. This paper pursues this goal in the context of a new class of models termed predictive models. Predictive models characterize the cortex as a memory whose parameters can be used to predict its input. This allows the input to be economically coded as a residual difference between itself and the prediction. Such models have recently had considerable success in modeling features of visual cortex. This paper shows that the predictive coding model can be extended to a lower level of detail that includes individual spikes as primitives. This is a significant improvement in perspicuity compared to the firing rate variables used by most current models. The specific model we describe exploits the use of coincidence of spike arrival times and the fact that neural representations can be distributed over large numbers of cells.