GAMIT-Net: retrospective and prospective interval timing in a single neural network

The neural network version of the Gaussian Activation Model of Interval Timing (GAMIT-Net) is a simple recurrent network that unifies retrospective and prospective timing in a single framework. It has two parts. Firstly, a time-dependent signal is generated by a spreading Gaussian activation. Next, a simple recurrent network (SRN) combines information from the Gaussian and its own internal state during a timing task to generate time estimates. This model captures the scalar property of interval timing (Gibbon, 1977). Furthermore, under high cognitive load the Gaussian fades faster while the internal state is updated less often. These factors interact to account for the surprising finding that retrospective estimates increase under cognitive load while prospective estimates decrease (Block, Hancock & Zakay, 2010)