Academic Journal
Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.
| العنوان: | Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone. |
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| المؤلفون: | Felipe Gerhard, Tilman Kispersky, Gabrielle J Gutierrez, Eve Marder, Mark Kramer, Uri Eden |
| المصدر: | PLoS Computational Biology, Vol 9, Iss 7, p e1003138 (2013) |
| بيانات النشر: | Public Library of Science (PLoS), 2013. |
| سنة النشر: | 2013 |
| المجموعة: | LCC:Biology (General) |
| مصطلحات موضوعية: | Biology (General), QH301-705.5 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1553-734X 1553-7358 |
| Relation: | http://europepmc.org/articles/PMC3708849?pdf=render; https://doaj.org/toc/1553-734X; https://doaj.org/toc/1553-7358 |
| DOI: | 10.1371/journal.pcbi.1003138 |
| URL الوصول: | https://doaj.org/article/209c8fc5b06d4d7981044024a02f85ab |
| رقم الانضمام: | edsdoj.209c8fc5b06d4d7981044024a02f85ab |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 1553734X 15537358 |
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| DOI: | 10.1371/journal.pcbi.1003138 |