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Neural Comput. 2015 Aug;27(8):1609-23. doi: 10.1162/NECO_a_00754. Epub 2015 Jun 16.

An Empirical Model for Reliable Spiking Activity.

Author information

1
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. wanjiew@upenn.edu.
2
Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. stripathy@chibi.ubc.ca.
3
Department of Biology, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. krishnan@salk.edu.
4
Center for the Neural Basis of Cognition and Department of Biology, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. nurban@cmu.edu.
5
Department of Statistics, Machine Learning Department, and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, PA 15213, U.S.A. kass@stat.cmu.edu.

Abstract

Understanding a neuron's transfer function, which relates a neuron's inputs to its outputs, is essential for understanding the computational role of single neurons. Recently, statistical models, based on point processes and using generalized linear model (GLM) technology, have been widely applied to predict dynamic neuronal transfer functions. However, the standard version of these models fails to capture important features of neural activity, such as responses to stimuli that elicit highly reliable trial-to-trial spiking. Here, we consider a generalization of the usual GLM that incorporates nonlinearity by modeling reliable and nonreliable spikes as being generated by distinct stimulus features. We develop and apply these models to spike trains from olfactory bulb mitral cells recorded in vitro. We find that spike generation in these neurons is better modeled when reliable and unreliable spikes are considered separately and that this effect is most pronounced for neurons with a large number of both reliable and unreliable spikes.

PMID:
26079749
PMCID:
PMC5450813
DOI:
10.1162/NECO_a_00754
[Indexed for MEDLINE]
Free PMC Article

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