Timing using temporal context

We present a memory model that explicitly constructs and stores the temporal information about when a stimulus was encountered in the past. The temporal information is constructed from a set of temporal context vectors adapted from the temporal context model (TCM). These vectors are leaky integrators that could be constructed from a population of persistently firing cells. An array of temporal context vectors with different decay rates calculates the Laplace transform of real time events. Simple bands of feedforward excitatory and inhibitory connections from these temporal context vectors enable another population of cells, timing cells. These timing cells approximately reconstruct the entire temporal history of past events. The temporal representation of events farther in the past is less accurate than for more recent events. This history-reconstruction procedure, which we refer to as timing from inverse Laplace transform (TILT), displays a scalar property with respect to the accuracy of reconstruction. When incorporated into a simple associative memory framework, we show that TILT predicts well-timed peak responses and the Weber law property, like that observed in interval timing tasks and classical conditioning experiments.

Research highlights
► How is the temporal information about the recent past represented in the brain?
► We propose that it is stored in the form of Laplace transform distributed across a spatially distributed population of neurons.
► Simple feed forward connections yield "time cells" that respond to specific stimuli at specific latencies.
► The activity of these time cells are scale invariant, and immediately leads to Weber's law at the behavioral level.