دورية أكاديمية
أعرض في EDS

Neural field models for latent state inference: Application to large-scale neuronal recordings.

التفاصيل البيبلوغرافية
العنوان: Neural field models for latent state inference: Application to large-scale neuronal recordings.
المؤلفون: Rule ME; Department of Engineering, University of Cambridge, Cambridge, United Kingdom., Schnoerr D; Theoretical Systems Biology, Imperial College London, London, United Kingdom., Hennig MH; Department of Informatics, University of Edinburgh, Edinburgh, United Kingdom., Sanguinetti G; Department of Informatics, University of Edinburgh, Edinburgh, United Kingdom.
المصدر: PLoS computational biology [PLoS Comput Biol] 2019 Nov 04; Vol. 15 (11), pp. e1007442. Date of Electronic Publication: 2019 Nov 04 (Print Publication: 2019).
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science, [2005]-
مواضيع طبية MeSH: Computational Biology/*methods , Neuroimaging/*methods, Action Potentials/physiology ; Algorithms ; Animals ; Bayes Theorem ; Brain Mapping/methods ; Data Interpretation, Statistical ; Humans ; Models, Neurological ; Models, Theoretical ; Nerve Net/physiology ; Neurons/physiology
مستخلص: Large-scale neural recording methods now allow us to observe large populations of identified single neurons simultaneously, opening a window into neural population dynamics in living organisms. However, distilling such large-scale recordings to build theories of emergent collective dynamics remains a fundamental statistical challenge. The neural field models of Wilson, Cowan, and colleagues remain the mainstay of mathematical population modeling owing to their interpretable, mechanistic parameters and amenability to mathematical analysis. Inspired by recent advances in biochemical modeling, we develop a method based on moment closure to interpret neural field models as latent state-space point-process models, making them amenable to statistical inference. With this approach we can infer the intrinsic states of neurons, such as active and refractory, solely from spiking activity in large populations. After validating this approach with synthetic data, we apply it to high-density recordings of spiking activity in the developing mouse retina. This confirms the essential role of a long lasting refractory state in shaping spatiotemporal properties of neonatal retinal waves. This conceptual and methodological advance opens up new theoretical connections between mathematical theory and point-process state-space models in neural data analysis.
Competing Interests: The authors have declared that no competing interests exist.
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تواريخ الأحداث: Date Created: 20191105 Date Completed: 20200214 Latest Revision: 20200309
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC6855563
DOI: 10.1371/journal.pcbi.1007442
PMID: 31682604
قاعدة البيانات: MEDLINE
الوصف
تدمد:1553-7358
DOI:10.1371/journal.pcbi.1007442