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A neurocomputational model of prospective and retrospective timing

Joost de Jong
University of Groningen ~ Department of Psychology
Dr. Aaron Voelker
Terry Stewart
National Research Council of Canada
Chris Eliasmith
Centre for Theoretical Neuroscience, University of Waterloo, Canada
Hedderik van Rijn
University of Groningen, The Netherlands

Keeping track of time is essential for everyday behavior. Theoretical models have proposed a wide variety of neural processes that could tell time, but it is unclear which ones the brain actually uses. Low-level neural models are specific, but rarely explicate how cognitive processes, such as attention and memory, modulate prospective and retrospective timing. Here we develop a neurocomputational model of prospective and retrospective timing, using a spiking recurrent neural network. The model captures behavior of individual spiking neurons and population dynamics when producing and perceiving time intervals, thus bridging low- and high-level phenomena. When interrupting events are introduced, the model delays responding in a similar way to pigeons and rats. Crucially, the model also explains why attending incoming stimuli decreases prospective estimates and increases retrospective estimates of time. In sum, our model offers a neurocomputational account of prospective and retrospective timing, from low-level neural dynamics to high-level cognition.



time perception
recurrent neural network

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Cite this as:

de Jong, J., Voelker, A., Stewart, T., Eliasmith, C., & van Rijn, H. (2021, July). A neurocomputational model of prospective and retrospective timing. Paper presented at Virtual MathPsych/ICCM 2021. Via