Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models

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

13 Citations (Scopus)

Abstract

Recent work on the statistical modeling of neural responses has focused on modulated renewal processes in which the spike rate is a function of the stimulus and recent spiking history. Typically, these models incorporate spike-history dependencies via either: (A) a conditionally-Poisson process with rate dependent on a linear projection of the spike train history (e.g., generalized linear model); or (B) a modulated non-Poisson renewal process (e.g., inhomogeneous gamma process). Here we show that the two approaches can be combined, resulting in a conditional renewal (CR) model for neural spike trains. This model captures both real-time and rescaled-time history effects, and can be fit by maximum likelihood using a simple application of the time-rescaling theorem [1]. We show that for any modulated renewal process model, the log-likelihood is concave in the linear filter parameters only under certain restrictive conditions on the renewal density (ruling out many popular choices, e.g. gamma with shape κ ≠ 1), suggesting that real-time history effects are easier to estimate than non-Poisson renewal properties. Moreover, we show that goodness-of-fit tests based on the time-rescaling theorem [1] quantify relative-time effects, but do not reliably assess accuracy in spike prediction or stimulus-response modeling. We illustrate the CR model with applications to both real and simulated neural data.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference
Pages1473-1481
Number of pages9
StatePublished - Dec 1 2009
Externally publishedYes
Event23rd Annual Conference on Neural Information Processing Systems, NIPS 2009 - Vancouver, BC, Canada
Duration: Dec 7 2009Dec 10 2009

Other

Other23rd Annual Conference on Neural Information Processing Systems, NIPS 2009
CountryCanada
CityVancouver, BC
Period12/7/0912/10/09

Fingerprint

Rescaling
Spike
Renewal
Encoding
Renewal Process
Gamma Process
Linear Projection
Real-time
Linear Filter
Model
Statistical Modeling
Goodness of Fit Test
Generalized Linear Model
Poisson process
Theorem
Process Model
Maximum Likelihood
Likelihood
Quantify
History

All Science Journal Classification (ASJC) codes

  • Information Systems

Cite this

Pillow, J. W. (2009). Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference (pp. 1473-1481)
Pillow, Jonathan William. / Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 2009. pp. 1473-1481
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Pillow, JW 2009, Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. in Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. pp. 1473-1481, 23rd Annual Conference on Neural Information Processing Systems, NIPS 2009, Vancouver, BC, Canada, 12/7/09.

Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. / Pillow, Jonathan William.

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 2009. p. 1473-1481.

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

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Pillow JW. Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 2009. p. 1473-1481