Analysis and modelling of variability and covariability of population spike trains across multiple time scales

As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modelling such data must also be developed. We present a model of responses to repeated trials of a sensory stimulus based on thresholded Gaussian processes that...

Full description

Saved in:
Bibliographic Details
Published in:Network (Bristol) 2012-03, Vol.23 (1-2), p.76-103
Main Authors: Lyamzin, Dmitry R., Garcia-Lazaro, Jose A., Lesica, Nicholas A.
Format: Article
Language:English
Subjects:
Algorithms
Animals
Auditory Pathways - anatomy & histology
Computer Simulation
Data Interpretation, Statistical
Dichotomized Gaussian
Excitatory Postsynaptic Potentials - physiology
Fano factor
Humans
Image Processing, Computer-Assisted - methods
Models, Neurological
Models, Statistical
Neural Networks (Computer)
Neuroimaging - statistics & numerical data
Neurons - physiology
noise correlations
Normal Distribution
population coding
Signal-To-Noise Ratio
Time Factors
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modelling such data must also be developed. We present a model of responses to repeated trials of a sensory stimulus based on thresholded Gaussian processes that allows for analysis and modelling of variability and covariability of population spike trains across multiple time scales. The model framework can be used to specify the values of many different variability measures including spike timing precision across trials, coefficient of variation of the interspike interval distribution, and Fano factor of spike counts for individual neurons, as well as signal and noise correlations and correlations of spike counts across multiple neurons. Using both simulated data and data from different stages of the mammalian auditory pathway, we demonstrate the range of possible independent manipulations of different variability measures, and explore how this range depends on the sensory stimulus. The model provides a powerful framework for the study of experimental and surrogate data and for analyzing dependencies between different statistical properties of neuronal populations.
ISSN:0954-898X
1361-6536
DOI:10.3109/0954898X.2012.679334