Encoding Through Patterns: Regression Tree-Based Neuronal Population Models
DC Element | Wert | Sprache |
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dc.contributor.author | Haslinger, Robert | |
dc.contributor.author | Pipa, Gordon | |
dc.contributor.author | Lewis, Laura D. | |
dc.contributor.author | Nikolic, Danko | |
dc.contributor.author | Williams, Ziv | |
dc.contributor.author | Brown, Emery | |
dc.date.accessioned | 2021-12-23T16:22:10Z | - |
dc.date.available | 2021-12-23T16:22:10Z | - |
dc.date.issued | 2013 | |
dc.identifier.issn | 08997667 | |
dc.identifier.uri | https://osnascholar.ub.uni-osnabrueck.de/handle/unios/14189 | - |
dc.description.abstract | Although the existence of correlated spiking between neurons in a population is well known, the role such correlations play in encoding stimuli is not. We address this question by constructing pattern-based encoding models that describe how time-varying stimulus drive modulates the expression probabilities of population-wide spike patterns. The challenge is that large populations may express an astronomical number of unique patterns, and so fitting a unique encoding model for each individual pattern is not feasible. We avoid this combinatorial problem using a dimensionality-reduction approach based on regression trees. Using the insight that some patterns may, from the perspective of encoding, be statistically indistinguishable, the tree divisively clusters the observed patterns into groups whose member patterns possess similar encoding properties. These groups, corresponding to the leaves of the tree, are much smaller in number than the original patterns, and the tree itself constitutes a tractable encoding model for each pattern. Our formalism can detect an extremely weak stimulus-driven pattern structure and is based on maximizing the data likelihood, not making a priori assumptions as to how patterns should be grouped. Most important, by comparing pattern encodings with independent neuron encodings, one can determine if neurons in the population are driven independently or collectively. We demonstrate this method using multiple unit recordings from area 17 of anesthetized cat in response to a sinusoidal grating and show that pattern-based encodings are superior to those of independent neuron models. The agnostic nature of our clustering approach allows us to investigate encoding by the collective statistics that are actually present rather than those (such as pairwise) that might be presumed. | |
dc.description.sponsorship | NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [K25 NS052422-02, DP1 OD003646-0, MH59733-07]; Hertie Foundation; Deutsche ForschungsgemeinschaftGerman Research Foundation (DFG) [NI 708/2-1]; Hertie StiftungAustrian Science Fund (FWF); Max-Planck SocietyMax Planck SocietyFoundation CELLEX; Frankfurt Institute for Advanced Studies; NATIONAL INSTITUTE OF MENTAL HEALTHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01MH059733] Funding Source: NIH RePORTER; NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKEUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Neurological Disorders & Stroke (NINDS) [K25NS052422] Funding Source: NIH RePORTER; OFFICE OF THE DIRECTOR, NATIONAL INSTITUTES OF HEALTHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [DP1OD003646] Funding Source: NIH RePORTER; We thank Demba Ba, Zhe Chen, and Cosma Shalizi for helpful conversations regarding the research presented in this article. This work was supported by NIH grants K25 NS052422-02 (R.H.), DP1 OD003646-0, MH59733-07 (E.N.), and the Hertie Foundation (G.P.). Experiments with cat recordings (D.N.) were supported by a Deutsche Forschungsgemeinschaft Grant NI 708/2-1, Hertie Stiftung, Max-Planck Society, and the Frankfurt Institute for Advanced Studies. | |
dc.language.iso | en | |
dc.publisher | MIT PRESS | |
dc.relation.ispartof | NEURAL COMPUTATION | |
dc.subject | Computer Science | |
dc.subject | Computer Science, Artificial Intelligence | |
dc.subject | CORTICAL-NEURONS | |
dc.subject | ENSEMBLE | |
dc.subject | INFORMATION | |
dc.subject | Neurosciences | |
dc.subject | Neurosciences & Neurology | |
dc.subject | NOISE | |
dc.subject | RELIABILITY | |
dc.subject | RESPONSE VARIABILITY | |
dc.title | Encoding Through Patterns: Regression Tree-Based Neuronal Population Models | |
dc.type | journal article | |
dc.identifier.doi | 10.1162/NECO_a_00464 | |
dc.identifier.isi | ISI:000320592700001 | |
dc.description.volume | 25 | |
dc.description.issue | 8 | |
dc.description.startpage | 1953 | |
dc.description.endpage | 1993 | |
dc.contributor.orcid | 0000-0002-3416-2652 | |
dc.contributor.orcid | 0000-0002-9317-8494 | |
dc.contributor.orcid | 0000-0002-4003-0277 | |
dc.contributor.researcherid | M-1813-2014 | |
dc.identifier.eissn | 1530888X | |
dc.publisher.place | ONE ROGERS ST, CAMBRIDGE, MA 02142-1209 USA | |
dcterms.isPartOf.abbreviation | Neural Comput. | |
dcterms.oaStatus | Green Published | |
crisitem.author.dept | Institut für Kognitionswissenschaft | - |
crisitem.author.deptid | institute28 | - |
crisitem.author.orcid | 0000-0002-3416-2652 | - |
crisitem.author.parentorg | FB 08 - Humanwissenschaften | - |
crisitem.author.grandparentorg | Universität Osnabrück | - |
crisitem.author.netid | PiGo340 | - |
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geprüft am 07.05.2024