English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

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

Archer, E., Koster, U., Pillow, J., & Macke, J. (2015). Low-dimensional models of neural population activity in sensory cortical circuits. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, & K. Weinberger (Eds.), Advances in Neural Information Processing Systems 27 (pp. 343-351). Red Hook, NY, USA: Curran.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002A-47B7-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0000-FAA8-9
Genre: Conference Paper

Files

show Files

Creators

show
hide
 Creators:
Archer, EW1, Author              
Koster, U, Author
Pillow, JW, Author
Macke, JH2, Author              
Affiliations:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s):
 Dates: 2015
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: ArcherKPM2014
 Degree: -

Event

show
hide
Title: Twenty-Eighth Annual Conference on Neural Information Processing Systems (NIPS 2014)
Place of Event: Montréal, Quebec, Canada
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: Advances in Neural Information Processing Systems 27
Source Genre: Proceedings
 Creator(s):
Ghahramani, Z., Editor
Welling, M., Editor
Cortes, C., Editor
Lawrence, N.D., Editor
Weinberger, K.Q., Editor
Affiliations:
-
Publ. Info: Red Hook, NY, USA : Curran
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 343 - 351 Identifier: ISBN: 978-1-5108-0041-0