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Neuron. 2016 Jul 20;91(2):221-59. doi: 10.1016/j.neuron.2016.05.039.

Analysis of Neuronal Spike Trains, Deconstructed.

Author information

1
Department of Physics, University of California, San Diego, San Diego, CA 92093, USA; Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA. Electronic address: aljadeff@uchicago.edu.
2
Department of Applied Mathematics, University of Washington, Seattle, WA 98195, USA.
3
Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA; WRF UW Institute for Neuroengineering, University of Washington, Seattle, WA 98195, USA.
4
Department of Physics, University of California, San Diego, San Diego, CA 92093, USA; Section of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA; Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA 92093, USA. Electronic address: dk@physics.ucsd.edu.

Abstract

As information flows through the brain, neuronal firing progresses from encoding the world as sensed by the animal to driving the motor output of subsequent behavior. One of the more tractable goals of quantitative neuroscience is to develop predictive models that relate the sensory or motor streams with neuronal firing. Here we review and contrast analytical tools used to accomplish this task. We focus on classes of models in which the external variable is compared with one or more feature vectors to extract a low-dimensional representation, the history of spiking and other variables are potentially incorporated, and these factors are nonlinearly transformed to predict the occurrences of spikes. We illustrate these techniques in application to datasets of different degrees of complexity. In particular, we address the fitting of models in the presence of strong correlations in the external variable, as occurs in natural sensory stimuli and in movement. Spectral correlation between predicted and measured spike trains is introduced to contrast the relative success of different methods.

KEYWORDS:

dimensional reduction; feature vector; neuroinformatics; predictive modeling; reverse correlation; systems identification; time series

PMID:
27477016
PMCID:
PMC4970242
DOI:
10.1016/j.neuron.2016.05.039
[Indexed for MEDLINE]
Free PMC Article

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