Abstract
We present a computational model by which ensembles of regularly spiking neurons can encode different time intervals through synchronous firing. We show that a neuron responding to a large population of convergent inputs has the potential to learn to produce an appropriately-timed output via spike-time dependent plasticity. We explain why temporal variability of this population synchrony increases with increasing time intervals. We also show that the scalar property of timing and its violation at short intervals can be explained by the spike-wise accumulation of jitter in the inter-spike intervals of timing neurons. We explore how the challenge of encoding longer time intervals can be overcome and conclude that this may involve a switch to a different population of neurons with lower firing rate, with the added effect of producing an earlier bias in response. Experimental data on human timing performance show features in agreement with the model's output.
Highlights
Timing is an essential part of producing voluntary coordinated movements
We find that the neuronal noise limits the length of time intervals that can be encoded
Our model of interval timing consists of a large bank of independently and regularly spiking pacemaker neurons that converge onto a coincidence detector (Figure 1A)
Summary
Timing is an essential part of producing voluntary coordinated movements. Timed sequences of muscular contractions are required to generate a range of behaviors from speech production to locomotion. These voluntary movements require neuronal timing systems that are both precise and flexible. Two major (and mutually non-exclusive) theories are the “pacemaker-accumulator model” and the “beat-frequency” model (Buhusi and Meck, 2005). Both require some form of oscillatory signal, which is found in abundance in the brain (Buzsáki and Draguhn, 2004)
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