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Abstract:
Animal data on delay-reward conditioning experiments shows a
striking property -- the data for different time intervals
collapses into a single curve when the data is scaled by the time
interval. thi sis called the scalar property of interval timing.
Here a simple model of a neural clock is presented and shown to
give rise to the scalar property. The model is an accumulator
consisting of noisy, linear spiking neurons. It is analytically
tractable and contains only three parameters. When coupled with
reinforcement learning it simulates peak procedure experiments,
producing both the scalar property and the pattern of single
trial covariance.
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