Reinforcement Learning and Time Perception -- a Model of Animal Experiments
Abstract
Animal data on delayed-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. This is 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 covariances.
Cite
Text
Shapiro and Wearden. "Reinforcement Learning and Time Perception -- a Model of Animal Experiments." Neural Information Processing Systems, 2001.Markdown
[Shapiro and Wearden. "Reinforcement Learning and Time Perception -- a Model of Animal Experiments." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/shapiro2001neurips-reinforcement/)BibTeX
@inproceedings{shapiro2001neurips-reinforcement,
title = {{Reinforcement Learning and Time Perception -- a Model of Animal Experiments}},
author = {Shapiro, Jonathan L. and Wearden, J.},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {115-122},
url = {https://mlanthology.org/neurips/2001/shapiro2001neurips-reinforcement/}
}