Population activity structure of excitatory and inhibitory neurons
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| Publicado en: | PLoS One vol. 12, no. 8 (Aug 2017), p. e0181773 |
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| Autor principal: | |
| Otros Autores: | , , , , , , |
| Publicado: |
Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 1930441969 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0181773 |2 doi | |
| 035 | |a 1930441969 | ||
| 045 | 2 | |b d20170801 |b d20170831 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Bittner, Sean R | |
| 245 | 1 | |a Population activity structure of excitatory and inhibitory neurons | |
| 260 | |b Public Library of Science |c Aug 2017 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure. | |
| 610 | 4 | |a University of Pittsburgh Carnegie Mellon University | |
| 651 | 4 | |a Pittsburgh Pennsylvania | |
| 651 | 4 | |a Pennsylvania | |
| 651 | 4 | |a New York | |
| 651 | 4 | |a United States--US | |
| 653 | |a Neurons | ||
| 653 | |a Population | ||
| 653 | |a Visual perception | ||
| 653 | |a Statistical tests | ||
| 653 | |a Factor analysis | ||
| 653 | |a Neurosciences | ||
| 653 | |a Biomedical engineering | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Cognition & reasoning | ||
| 653 | |a Population (statistical) | ||
| 653 | |a Excitation | ||
| 653 | |a Social | ||
| 653 | |a Clustering | ||
| 653 | |a Classification | ||
| 653 | |a Computer engineering | ||
| 653 | |a Firing pattern | ||
| 653 | |a Methods | ||
| 653 | |a Structure-function relationships | ||
| 653 | |a Population studies | ||
| 653 | |a Visual cortex | ||
| 700 | 1 | |a Williamson, Ryan C | |
| 700 | 1 | |a Snyder, Adam C | |
| 700 | 1 | |a Litwin-Kumar, Ashok | |
| 700 | 1 | |a Doiron, Brent | |
| 700 | 1 | |a Chase, Steven M | |
| 700 | 1 | |a Smith, Matthew A | |
| 700 | 1 | |a Yu, Byron M | |
| 773 | 0 | |t PLoS One |g vol. 12, no. 8 (Aug 2017), p. e0181773 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1930441969/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/1930441969/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1930441969/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |