Inferring the temporal evolution of synaptic weights from dynamic functional connectivity
Abstract How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-12-01
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| Series: | Brain Informatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40708-022-00178-0 |
| _version_ | 1811188705677803520 |
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| author | Marco Celotto Stefan Lemke Stefano Panzeri |
| author_facet | Marco Celotto Stefan Lemke Stefano Panzeri |
| author_sort | Marco Celotto |
| collection | DOAJ |
| description | Abstract How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issue. We first simulated recurrent neural network models of spiking neurons with spike timing-dependent plasticity mechanisms that generate time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that infer fixed and time-varying properties of synaptic connectivity from directed functional connectivity measures, such as cross-covariance and transfer entropy. We found that, while both cross-covariance and transfer entropy provide robust estimates of which synapses are present in the network and their communication delays, dynamic functional connectivity measured via cross-covariance better captures the evolution of synaptic weights over time. We also established how measures of information transmission delays from static functional connectivity computed over long recording periods (i.e., several hours) can improve shorter time-scale estimates of the temporal evolution of synaptic weights from dynamic functional connectivity. These results provide useful information about how to accurately estimate the temporal variation of synaptic strength from spiking activity measures. |
| first_indexed | 2024-04-11T14:22:55Z |
| format | Article |
| id | doaj.art-0b6002883ee542ef8d297bf3d48f2339 |
| institution | Directory Open Access Journal |
| issn | 2198-4018 2198-4026 |
| language | English |
| last_indexed | 2024-04-11T14:22:55Z |
| publishDate | 2022-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj.art-0b6002883ee542ef8d297bf3d48f23392022-12-22T04:18:57ZengSpringerOpenBrain Informatics2198-40182198-40262022-12-019111210.1186/s40708-022-00178-0Inferring the temporal evolution of synaptic weights from dynamic functional connectivityMarco Celotto0Stefan Lemke1Stefano Panzeri2Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE)Neural Computation Laboratory, Istituto Italiano di TecnologiaDepartment of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE)Abstract How to capture the temporal evolution of synaptic weights from measures of dynamic functional connectivity between the activity of different simultaneously recorded neurons is an important and open problem in systems neuroscience. Here, we report methodological progress to address this issue. We first simulated recurrent neural network models of spiking neurons with spike timing-dependent plasticity mechanisms that generate time-varying synaptic and functional coupling. We then used these simulations to test analytical approaches that infer fixed and time-varying properties of synaptic connectivity from directed functional connectivity measures, such as cross-covariance and transfer entropy. We found that, while both cross-covariance and transfer entropy provide robust estimates of which synapses are present in the network and their communication delays, dynamic functional connectivity measured via cross-covariance better captures the evolution of synaptic weights over time. We also established how measures of information transmission delays from static functional connectivity computed over long recording periods (i.e., several hours) can improve shorter time-scale estimates of the temporal evolution of synaptic weights from dynamic functional connectivity. These results provide useful information about how to accurately estimate the temporal variation of synaptic strength from spiking activity measures.https://doi.org/10.1186/s40708-022-00178-0Dynamic functional connectivitySpiking neural networkCommunication delayTransfer entropyCross-covariance |
| spellingShingle | Marco Celotto Stefan Lemke Stefano Panzeri Inferring the temporal evolution of synaptic weights from dynamic functional connectivity Brain Informatics Dynamic functional connectivity Spiking neural network Communication delay Transfer entropy Cross-covariance |
| title | Inferring the temporal evolution of synaptic weights from dynamic functional connectivity |
| title_full | Inferring the temporal evolution of synaptic weights from dynamic functional connectivity |
| title_fullStr | Inferring the temporal evolution of synaptic weights from dynamic functional connectivity |
| title_full_unstemmed | Inferring the temporal evolution of synaptic weights from dynamic functional connectivity |
| title_short | Inferring the temporal evolution of synaptic weights from dynamic functional connectivity |
| title_sort | inferring the temporal evolution of synaptic weights from dynamic functional connectivity |
| topic | Dynamic functional connectivity Spiking neural network Communication delay Transfer entropy Cross-covariance |
| url | https://doi.org/10.1186/s40708-022-00178-0 |
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