Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits
Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct char...
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Frontiers
2013
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| Online Access: | https://eprints.nottingham.ac.uk/49637/ |
| _version_ | 1848798043273428992 |
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| author | Costa, Rui P. Sjöström, P. Jesper van Rossum, Mark C.W. |
| author_facet | Costa, Rui P. Sjöström, P. Jesper van Rossum, Mark C.W. |
| author_sort | Costa, Rui P. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data. |
| first_indexed | 2025-11-14T20:13:30Z |
| format | Article |
| id | nottingham-49637 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T20:13:30Z |
| publishDate | 2013 |
| publisher | Frontiers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-496372020-05-04T16:37:31Z https://eprints.nottingham.ac.uk/49637/ Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits Costa, Rui P. Sjöström, P. Jesper van Rossum, Mark C.W. Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data. Frontiers 2013-06-06 Article PeerReviewed Costa, Rui P., Sjöström, P. Jesper and van Rossum, Mark C.W. (2013) Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits. Frontiers in Computational Neuroscience, 7 . p. 75. ISSN 1662-5188 short-term synaptic plasticity probabilistic inference neocortical circuits experimental design parameter estimation https://www.frontiersin.org/articles/10.3389/fncom.2013.00075/full doi:10.3389/fncom.2013.00075 doi:10.3389/fncom.2013.00075 |
| spellingShingle | short-term synaptic plasticity probabilistic inference neocortical circuits experimental design parameter estimation Costa, Rui P. Sjöström, P. Jesper van Rossum, Mark C.W. Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits |
| title | Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits |
| title_full | Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits |
| title_fullStr | Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits |
| title_full_unstemmed | Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits |
| title_short | Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits |
| title_sort | probabilistic inference of short-term synaptic plasticity in neocortical microcircuits |
| topic | short-term synaptic plasticity probabilistic inference neocortical circuits experimental design parameter estimation |
| url | https://eprints.nottingham.ac.uk/49637/ https://eprints.nottingham.ac.uk/49637/ https://eprints.nottingham.ac.uk/49637/ |