Statistical models for neural encoding, decoding, and optimal stimulus design

Liam Paninski, Jonathan Pillow, Jeremy Lewi

Research output: Chapter in Book/Report/Conference proceedingChapter

138 Scopus citations

Abstract

There are two basic problems in the statistical analysis of neural data. The "encoding" problem concerns how information is encoded in neural spike trains: can we predict the spike trains of a neuron (or population of neurons), given an arbitrary stimulus or observed motor response? Conversely, the "decoding" problem concerns how much information is in a spike train, in particular, how well can we estimate the stimulus that gave rise to the spike train? This chapter describes statistical model-based techniques that in some cases provide a unified solution to these two coding problems. These models can capture stimulus dependencies as well as spike history and interneuronal interaction effects in population spike trains, and are intimately related to biophysically based models of integrate-and-fire type. We describe flexible, powerful likelihood-based methods for fitting these encoding models and then for using the models to perform optimal decoding. Each of these (apparently quite difficult) tasks turn out to be highly computationally tractable, due to a key concavity property of the model likelihood. Finally, we return to the encoding problem to describe how to use these models to adaptively optimize the stimuli presented to the cell on a trial-by-trial basis, in order that we may infer the optimal model parameters as efficiently as possible.

Original languageEnglish (US)
Title of host publicationComputational Neuroscience
Subtitle of host publicationTheoretical Insights into Brain Function
EditorsPaul Cisek, Trevor Drew, John Kalaska
Pages493-507
Number of pages15
DOIs
StatePublished - Oct 8 2007
Externally publishedYes

Publication series

NameProgress in Brain Research
Volume165
ISSN (Print)0079-6123

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

Keywords

  • decoding
  • neural coding
  • optimal experimental design

Fingerprint Dive into the research topics of 'Statistical models for neural encoding, decoding, and optimal stimulus design'. Together they form a unique fingerprint.

  • Cite this

    Paninski, L., Pillow, J., & Lewi, J. (2007). Statistical models for neural encoding, decoding, and optimal stimulus design. In P. Cisek, T. Drew, & J. Kalaska (Eds.), Computational Neuroscience: Theoretical Insights into Brain Function (pp. 493-507). (Progress in Brain Research; Vol. 165). https://doi.org/10.1016/S0079-6123(06)65031-0