Estimating Spiking Irregularities Under Changing Environments

Authors:
Keiji Miura;Masato Okada;Shun-Ichi Amari
Affiliations:
Department of Physics, Kyoto University, Kyoto 606-8502, and Intelligent Cooperation and Control, PRESTO, JST, Chiba 277-8561, Japan miura@ton.scphys.kyoto-u.ac.jp;Department of Complexity Science and Engineering, University of Tokyo, Chiba 277-8561/ Intelligent Cooperation and Control, PRESTO, JST, Chiba 277-8561/ and Brain Science Institute, RIKEN, Saitama ...;Brain Science Institute, RIKEN, Saitama 351-0198, Japan amari@brain.riken.jp
Venue:
Neural Computation
Year:
2006

Citing 6
Cited 13

Information bounds and nonparametric maximum likelihood estimation

Information bounds and nonparametric maximum likelihood estimation
Natural gradient works efficiently in learning

Neural Computation
Temporally correlated inputs to leaky integrate-and-fire models can reproduce spiking statistics of cortical neurons

Neural Networks - Special issue on organisation of computation in brain-like systems
The time-rescaling theorem and its application to neural spike train data analysis

Neural Computation
Differences in spiking patterns among cortical neurons

Neural Computation
Information Geometry of Interspike Intervals in Spiking Neurons

Neural Computation

Parameters of spike trains observed in a short time window

Neural Computation
2008 Special Issue: FIND - A unified framework for neural data analysis

Neural Networks
Capacity of a single spiking neuron channel

Neural Computation
Estimating instantaneous irregularity of neuronal firing

Neural Computation
Information-geometric measures as robust estimators of connection strengths and external inputs

Neural Computation
Measure of correlation orthogonal to change in firing rate

Neural Computation
Information geometry of interspike intervals in spiking neurons with refractories

ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
A characterization of the time-rescaled gamma process as a model for spike trains

Journal of Computational Neuroscience
Comparison of local measures of spike time irregularity and relating variability to firing rate in motor cortical neurons

Journal of Computational Neuroscience
Firing variability is higher than deduced from the empirical coefficient of variation

Neural Computation
Optimizing time histograms for non-poissonian spike trains

Neural Computation
Dreaming of mathematical neuroscience for half a century

Neural Networks
Information-geometric measures for estimation of connection weight under correlated inputs

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We considered a gammadistribution of interspike intervals as a statistical model for neuronal spike generation. A gamma distribution is a natural extension of the Poisson process taking the effect of a refractory period into account. The model is specified by two parameters: a time-dependent firing rate and a shape parameter that characterizes spiking irregularities of individual neurons. Because the environment changes over time, observed data are generated from a model with a time-dependent firing rate, which is an unknown function. A statistical model with an unknown function is called a semiparametric model and is generally very difficult to solve. We used a novel method of estimating functions in information geometry to estimate the shape parameter without estimating the unknown function. We obtained an optimal estimating function analytically for the shape parameter independent of the functional form of the firing rate. This estimation is efficient without Fisher information loss and better than maximum likelihood estimation. We suggest a measure of spiking irregularity based on the estimating function, which may be useful for characterizing individual neurons in changing environments.