Goodness-of-fit tests for neural population models: the multivariate time-rescaling theorem

  • Poster Presentation from Nineteenth Annual Computational Neuroscience Meeting: CNS*2010 San Antonio, TX, USA. 24-30 July 2010 Statistical models of neural activity are at the core of the field of modern computational neuroscience. The activity of single neurons has been modeled to successfully explain dependencies of neural dynamics to its own spiking history, to external stimuli or other covariates [1]. Recently, there has been a growing interest in modeling spiking activity of a population of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing (existing models include generalized linear models [2,3] or maximum-entropy approaches [4]). For point-process-based models of single neurons, the time-rescaling theorem has proven to be a useful toolbox to assess goodness-of-fit. In its univariate form, the time-rescaling theorem states that if the conditional intensity function of a point process is known, then its inter-spike intervals can be transformed or “rescaled” so that they are independent and exponentially distributed [5]. However, the theorem in its original form lacks sensitivity to detect even strong dependencies between neurons. Here, we present how the theorem can be extended to be applied to neural population models and we provide a step-by-step procedure to perform the statistical tests. We then apply both the univariate and multivariate tests to simplified toy models, but also to more complicated many-neuron models and to neuronal populations recorded in V1 of awake monkey during natural scenes stimulation. We demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. ...

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Metadaten
Author:Felipe Gerhard, Robert Haslinger, Gordon PipaORCiDGND
URN:urn:nbn:de:hebis:30-78317
DOI:https://doi.org/10.1186/1471-2202-11-S1-P46
Parent Title (English):BMC neuroscience 2010, 11(Suppl 1):P46
Document Type:Article
Language:English
Year of Completion:2010
Year of first Publication:2010
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2010/08/10
Volume:11(Suppl 1)
Issue:P46
Note:
© 2010 Gerhard et al; licensee BioMed Central Ltd.
Source:http://www.biomedcentral.com/1471-2202/11/S1/P46
HeBIS-PPN:226001601
Institutes:Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Sammlungen:Universitätspublikationen
Licence (German):License LogoDeutsches Urheberrecht