Data Driven Models of Short-Term Synaptic Plasticity
Simple models of short term synaptic plasticity that incorporate facilitation and/or depression have been created in abundance for different synapse types and circumstances. The analysis of these models has included computing mutual information between a stochastic input spike train and some sort of...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2018-05-01
|
| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fncom.2018.00032/full |
| _version_ | 1819074252477825024 |
|---|---|
| author | Elham Bayat Mokhtari J. Josh Lawrence Emily F. Stone |
| author_facet | Elham Bayat Mokhtari J. Josh Lawrence Emily F. Stone |
| author_sort | Elham Bayat Mokhtari |
| collection | DOAJ |
| description | Simple models of short term synaptic plasticity that incorporate facilitation and/or depression have been created in abundance for different synapse types and circumstances. The analysis of these models has included computing mutual information between a stochastic input spike train and some sort of representation of the postsynaptic response. While this approach has proven useful in many contexts, for the purpose of determining the type of process underlying a stochastic output train, it ignores the ordering of the responses, leaving an important characterizing feature on the table. In this paper we use a broader class of information measures on output only, and specifically construct hidden Markov models (HMMs) (known as epsilon machines or causal state models) to differentiate between synapse type, and classify the complexity of the process. We find that the machines allow us to differentiate between processes in a way not possible by considering distributions alone. We are also able to understand these differences in terms of the dynamics of the model used to create the output response, bringing the analysis full circle. Hence this technique provides a complimentary description of the synaptic filtering process, and potentially expands the interpretation of future experimental results. |
| first_indexed | 2024-12-21T18:06:34Z |
| format | Article |
| id | doaj.art-70a1ecfff5304995a995ba9935d726ff |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-21T18:06:34Z |
| publishDate | 2018-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-70a1ecfff5304995a995ba9935d726ff2022-12-21T18:54:55ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882018-05-011210.3389/fncom.2018.00032355973Data Driven Models of Short-Term Synaptic PlasticityElham Bayat Mokhtari0J. Josh Lawrence1Emily F. Stone2Department of Mathematical Sciences, The University of Montana, Missoula, MT, United StatesPharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, United StatesDepartment of Mathematical Sciences, The University of Montana, Missoula, MT, United StatesSimple models of short term synaptic plasticity that incorporate facilitation and/or depression have been created in abundance for different synapse types and circumstances. The analysis of these models has included computing mutual information between a stochastic input spike train and some sort of representation of the postsynaptic response. While this approach has proven useful in many contexts, for the purpose of determining the type of process underlying a stochastic output train, it ignores the ordering of the responses, leaving an important characterizing feature on the table. In this paper we use a broader class of information measures on output only, and specifically construct hidden Markov models (HMMs) (known as epsilon machines or causal state models) to differentiate between synapse type, and classify the complexity of the process. We find that the machines allow us to differentiate between processes in a way not possible by considering distributions alone. We are also able to understand these differences in terms of the dynamics of the model used to create the output response, bringing the analysis full circle. Hence this technique provides a complimentary description of the synaptic filtering process, and potentially expands the interpretation of future experimental results.https://www.frontiersin.org/article/10.3389/fncom.2018.00032/fullshort term plasticityepsilon machinessynaptic filteringmutual informationinterneuron-pyramidal cell synapsescausal state splitting reconstruction |
| spellingShingle | Elham Bayat Mokhtari J. Josh Lawrence Emily F. Stone Data Driven Models of Short-Term Synaptic Plasticity Frontiers in Computational Neuroscience short term plasticity epsilon machines synaptic filtering mutual information interneuron-pyramidal cell synapses causal state splitting reconstruction |
| title | Data Driven Models of Short-Term Synaptic Plasticity |
| title_full | Data Driven Models of Short-Term Synaptic Plasticity |
| title_fullStr | Data Driven Models of Short-Term Synaptic Plasticity |
| title_full_unstemmed | Data Driven Models of Short-Term Synaptic Plasticity |
| title_short | Data Driven Models of Short-Term Synaptic Plasticity |
| title_sort | data driven models of short term synaptic plasticity |
| topic | short term plasticity epsilon machines synaptic filtering mutual information interneuron-pyramidal cell synapses causal state splitting reconstruction |
| url | https://www.frontiersin.org/article/10.3389/fncom.2018.00032/full |
| work_keys_str_mv | AT elhambayatmokhtari datadrivenmodelsofshorttermsynapticplasticity AT jjoshlawrence datadrivenmodelsofshorttermsynapticplasticity AT emilyfstone datadrivenmodelsofshorttermsynapticplasticity |