What does scalar timing tell us about neural dynamics?
The “Scalar Timing Law,” which is a temporal domain generalization of the well known Weber Law, states that the errors estimating temporal intervals scale linearly with the durations of the intervals. Linear scaling has been studied extensively in human and animal models and holds over several order...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Frontiers Research Foundation
2014
|
| Online Access: | http://hdl.handle.net/1721.1/89196 https://orcid.org/0000-0001-8420-8973 |
| _version_ | 1811089693355278336 |
|---|---|
| author | Shouval, Harel Z. Hussain Shuler, Marshall G. Agarwal, Animesh Gavornik, Jeffrey |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Shouval, Harel Z. Hussain Shuler, Marshall G. Agarwal, Animesh Gavornik, Jeffrey |
| author_sort | Shouval, Harel Z. |
| collection | MIT |
| description | The “Scalar Timing Law,” which is a temporal domain generalization of the well known Weber Law, states that the errors estimating temporal intervals scale linearly with the durations of the intervals. Linear scaling has been studied extensively in human and animal models and holds over several orders of magnitude, though to date there is no agreed upon explanation for its physiological basis. Starting from the assumption that behavioral variability stems from neural variability, this work shows how to derive firing rate functions that are consistent with scalar timing. We show that firing rate functions with a log-power form, and a set of parameters that depend on spike count statistics, can account for scalar timing. Our derivation depends on a linear approximation, but we use simulations to validate the theory and show that log-power firing rate functions result in scalar timing over a large range of times and parameters. Simulation results match the predictions of our model, though our initial formulation results in a slight bias toward overestimation that can be corrected using a simple iterative approach to learn a decision threshold. |
| first_indexed | 2024-09-23T14:22:54Z |
| format | Article |
| id | mit-1721.1/89196 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:22:54Z |
| publishDate | 2014 |
| publisher | Frontiers Research Foundation |
| record_format | dspace |
| spelling | mit-1721.1/891962022-09-29T09:06:04Z What does scalar timing tell us about neural dynamics? Shouval, Harel Z. Hussain Shuler, Marshall G. Agarwal, Animesh Gavornik, Jeffrey Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Picower Institute for Learning and Memory Gavornik, Jeffrey The “Scalar Timing Law,” which is a temporal domain generalization of the well known Weber Law, states that the errors estimating temporal intervals scale linearly with the durations of the intervals. Linear scaling has been studied extensively in human and animal models and holds over several orders of magnitude, though to date there is no agreed upon explanation for its physiological basis. Starting from the assumption that behavioral variability stems from neural variability, this work shows how to derive firing rate functions that are consistent with scalar timing. We show that firing rate functions with a log-power form, and a set of parameters that depend on spike count statistics, can account for scalar timing. Our derivation depends on a linear approximation, but we use simulations to validate the theory and show that log-power firing rate functions result in scalar timing over a large range of times and parameters. Simulation results match the predictions of our model, though our initial formulation results in a slight bias toward overestimation that can be corrected using a simple iterative approach to learn a decision threshold. R01MH093665 K99MH099654 2014-09-05T13:47:52Z 2014-09-05T13:47:52Z 2014-06 2014-03 Article http://purl.org/eprint/type/JournalArticle 1662-5161 http://hdl.handle.net/1721.1/89196 Shouval, Harel Z., Marshall G. Hussain Shuler, Animesh Agarwal, and Jeffrey P. Gavornik. “What Does Scalar Timing Tell Us About Neural Dynamics?” Frontiers in Human Neuroscience 8 (June 19, 2014). https://orcid.org/0000-0001-8420-8973 en_US http://dx.doi.org/10.3389/fnhum.2014.00438 Frontiers in Human Neuroscience Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Frontiers Research Foundation Frontiers Research Foundation |
| spellingShingle | Shouval, Harel Z. Hussain Shuler, Marshall G. Agarwal, Animesh Gavornik, Jeffrey What does scalar timing tell us about neural dynamics? |
| title | What does scalar timing tell us about neural dynamics? |
| title_full | What does scalar timing tell us about neural dynamics? |
| title_fullStr | What does scalar timing tell us about neural dynamics? |
| title_full_unstemmed | What does scalar timing tell us about neural dynamics? |
| title_short | What does scalar timing tell us about neural dynamics? |
| title_sort | what does scalar timing tell us about neural dynamics |
| url | http://hdl.handle.net/1721.1/89196 https://orcid.org/0000-0001-8420-8973 |
| work_keys_str_mv | AT shouvalharelz whatdoesscalartimingtellusaboutneuraldynamics AT hussainshulermarshallg whatdoesscalartimingtellusaboutneuraldynamics AT agarwalanimesh whatdoesscalartimingtellusaboutneuraldynamics AT gavornikjeffrey whatdoesscalartimingtellusaboutneuraldynamics |