Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.
Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More rece...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
|
| Series: | PLoS Computational Biology |
| Online Access: | http://europepmc.org/articles/PMC3708849?pdf=render |
| _version_ | 1811310529705148416 |
|---|---|
| author | Felipe Gerhard Tilman Kispersky Gabrielle J Gutierrez Eve Marder Mark Kramer Uri Eden |
| author_facet | Felipe Gerhard Tilman Kispersky Gabrielle J Gutierrez Eve Marder Mark Kramer Uri Eden |
| author_sort | Felipe Gerhard |
| collection | DOAJ |
| description | Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities. |
| first_indexed | 2024-04-13T10:00:22Z |
| format | Article |
| id | doaj.art-209c8fc5b06d4d7981044024a02f85ab |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-13T10:00:22Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-209c8fc5b06d4d7981044024a02f85ab2022-12-22T02:51:15ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582013-01-0197e100313810.1371/journal.pcbi.1003138Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.Felipe GerhardTilman KisperskyGabrielle J GutierrezEve MarderMark KramerUri EdenIdentifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity. Usually, these algorithms have not been validated on a neurophysiological data set for which the actual circuitry is known. Recent work has shown that traditional network inference algorithms based on linear models typically fail to identify the correct coupling of a small central pattern generating circuit in the stomatogastric ganglion of the crab Cancer borealis. In this work, we show that point process models of observed spike trains can guide inference of relative connectivity estimates that match the known physiological connectivity of the central pattern generator up to a choice of threshold. We elucidate the necessary steps to derive faithful connectivity estimates from a model that incorporates the spike train nature of the data. We then apply the model to measure changes in the effective connectivity pattern in response to two pharmacological interventions, which affect both intrinsic neural dynamics and synaptic transmission. Our results provide the first successful application of a network inference algorithm to a circuit for which the actual physiological synapses between neurons are known. The point process methodology presented here generalizes well to larger networks and can describe the statistics of neural populations. In general we show that advanced statistical models allow for the characterization of effective network structure, deciphering underlying network dynamics and estimating information-processing capabilities.http://europepmc.org/articles/PMC3708849?pdf=render |
| spellingShingle | Felipe Gerhard Tilman Kispersky Gabrielle J Gutierrez Eve Marder Mark Kramer Uri Eden Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone. PLoS Computational Biology |
| title | Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone. |
| title_full | Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone. |
| title_fullStr | Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone. |
| title_full_unstemmed | Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone. |
| title_short | Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone. |
| title_sort | successful reconstruction of a physiological circuit with known connectivity from spiking activity alone |
| url | http://europepmc.org/articles/PMC3708849?pdf=render |
| work_keys_str_mv | AT felipegerhard successfulreconstructionofaphysiologicalcircuitwithknownconnectivityfromspikingactivityalone AT tilmankispersky successfulreconstructionofaphysiologicalcircuitwithknownconnectivityfromspikingactivityalone AT gabriellejgutierrez successfulreconstructionofaphysiologicalcircuitwithknownconnectivityfromspikingactivityalone AT evemarder successfulreconstructionofaphysiologicalcircuitwithknownconnectivityfromspikingactivityalone AT markkramer successfulreconstructionofaphysiologicalcircuitwithknownconnectivityfromspikingactivityalone AT urieden successfulreconstructionofaphysiologicalcircuitwithknownconnectivityfromspikingactivityalone |