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  Low-dimensional models of neural population activity in sensory cortical circuits

Archer, E., Koster, U., Pillow, J., & Macke, J. (2014). Low-dimensional models of neural population activity in sensory cortical circuits. In Ghahramani, Z., M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27 (pp. 343-351).

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 Urheber:
Archer, EW1, 2, Autor
Koster, U, Autor
Pillow, JW, Autor
Macke, JH1, 2, Autor           
Affiliations:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Zusammenfassung: Neural responses in visual cortex are influenced by visual stimuli and by ongoing spiking activity in local circuits. An important challenge in computational neuroscience is to develop models that can account for both of these features in large multi-neuron recordings and to reveal how stimulus representations interact with and depend on cortical dynamics. Here we introduce a statistical model of neural population activity that integrates a nonlinear receptive field model with a latent dynamical model of ongoing cortical activity. This model captures the temporal dynamics, effective network connectivity in large population recordings, and correlations due to shared stimulus drive as well as common noise. Moreover, because the nonlinear stimulus inputs are mixed by the ongoing dynamics, the model can account for a relatively large number of idiosyncratic receptive field shapes with a small number of nonlinear inputs to a low-dimensional latent dynamical model. We introduce a fast estimation method using online expectation maximization with Laplace approximations. Inference scales linearly in both population size and recording duration. We apply this model to multi-channel recordings from primary visual cortex and show that it accounts for a large number of individual neural receptive fields using a small number of nonlinear inputs and a low-dimensional dynamical model.

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 Datum: 2014-12
 Publikationsstatus: Erschienen
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 Identifikatoren: BibTex Citekey: ArcherKPM2014
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Titel: Twenty-Eighth Annual Conference on Neural Information Processing Systems (NIPS 2014)
Veranstaltungsort: Montréal, Quebec, Canada
Start-/Enddatum: -

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Titel: Advances in Neural Information Processing Systems 27
Genre der Quelle: Konferenzband
 Urheber:
Ghahramani, Herausgeber
Z., Herausgeber
Welling, M., Herausgeber
Cortes, C., Herausgeber
Lawrence, N. D., Herausgeber
Weinberger, K. Q., Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 343 - 351 Identifikator: -