Maximum likelihood estimation of a stochastic integrate-and-fire neural model

Jonathan W. Pillow, Liam Paninski, Eero Simoncelli

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recent work has examined the estimation of models of stimulus-driven neural activity in which some linear filtering process is followed by a nonlinear, probabilistic spiking stage. We analyze the estimation of one such model for which this nonlinear step is implemented by a noisy, leaky, integrate-and-fire mechanism with a spike-dependent aftercurrent. This model is a biophysically plausible alternative to models with Poisson (memory-less) spiking, and has been shown to effectively reproduce various spiking statistics of neurons in vivo. However, the problem of estimating the model from extracellular spike train data has not been examined in depth. We formulate the problem in terms of maximum likelihood estimation, and show that the computational problem of maximizing the likelihood is tractable. Our main contribution is an algorithm and a proof that this algorithm is guaranteed to find the global optimum with reasonable speed. We demonstrate the effectiveness of our estimator with numerical simulations.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
PublisherNeural information processing systems foundation
ISBN (Print)0262201526, 9780262201520
StatePublished - 2004
Event17th Annual Conference on Neural Information Processing Systems, NIPS 2003 - Vancouver, BC, Canada
Duration: Dec 8 2003Dec 13 2003

Other

Other17th Annual Conference on Neural Information Processing Systems, NIPS 2003
CountryCanada
CityVancouver, BC
Period12/8/0312/13/03

Fingerprint

Maximum likelihood estimation
Fires
Neurons
Statistics
Data storage equipment
Computer simulation

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Pillow, J. W., Paninski, L., & Simoncelli, E. (2004). Maximum likelihood estimation of a stochastic integrate-and-fire neural model. In Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003 Neural information processing systems foundation.

Maximum likelihood estimation of a stochastic integrate-and-fire neural model. / Pillow, Jonathan W.; Paninski, Liam; Simoncelli, Eero.

Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003. Neural information processing systems foundation, 2004.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pillow, JW, Paninski, L & Simoncelli, E 2004, Maximum likelihood estimation of a stochastic integrate-and-fire neural model. in Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003. Neural information processing systems foundation, 17th Annual Conference on Neural Information Processing Systems, NIPS 2003, Vancouver, BC, Canada, 12/8/03.
Pillow JW, Paninski L, Simoncelli E. Maximum likelihood estimation of a stochastic integrate-and-fire neural model. In Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003. Neural information processing systems foundation. 2004
Pillow, Jonathan W. ; Paninski, Liam ; Simoncelli, Eero. / Maximum likelihood estimation of a stochastic integrate-and-fire neural model. Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003. Neural information processing systems foundation, 2004.
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