Differences in spiking patterns among cortical neurons

Authors:
Shigeru Shinomoto;Keisetsu Shima;Jun Tanji
Affiliations:
Department of Physics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan;Department of Physiology, Tohoku University School of Medicine, Sendai 980-8575, Japan;Department of Physiology, Tohoku University School of Medicine, Sendai 980-8575, Japan
Venue:
Neural Computation
Year:
2003

Citing 4
Cited 20

The Ornstein-Uhlenbeck process does not reproduce spiking statistics of neurons in prefrontal cortex

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
New classification scheme of cortical sites with the neuronal spiking characteristics

Neural Networks
Attractor reliability reveals deterministic structure in neuronal spike trains

Neural Computation

Information Geometry of Interspike Intervals in Spiking Neurons

Neural Computation
Estimating Spiking Irregularities Under Changing Environments

Neural Computation
A Method for Selecting the Bin Size of a Time Histogram

Neural Computation
Chaotic Motif Sampler for Motif Discovery Using Statistical Values of Spike Time-Series

Neural Information Processing
Capacity of a single spiking neuron channel

Neural Computation
Estimating instantaneous irregularity of neuronal firing

Neural Computation
Spiking neuron channel

ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 3
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
2010 Special Issue: Fitting a stochastic spiking model to neuronal current injection data

Neural Networks
A mathematical theory of energy efficient neural computation and communication

IEEE Transactions on Information Theory - Special issue on information theory in molecular biology and neuroscience
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
Kernel bandwidth optimization in spike rate estimation

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

Neural Computation
An information geometrical analysis of neural spike sequences

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Optimizing time histograms for non-poissonian spike trains

Neural Computation
The relationship between the newborn rats' hypoxic-ischemic brain damage and heart beat interval information

ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Statistical properties of superimposed stationary spike trains

Journal of Computational Neuroscience
Measures of statistical dispersion based on Shannon and Fisher information concepts

Information Sciences: an International Journal
Information transmission using non-poisson regular firing

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

Spike sequences recorded from four cortical areas of an awake behaving monkey were examined to explore characteristics that vary among neurons. We found that a measure of the local variation of interspike intervals, Lv, is nearly the same for every spike sequence for any given neuron, while it varies significantly among neurons. The distributions of Lv values for neuron ensembles in three of the four areas were found to be distinctly bimodal. Two groups of neurons classified according to the spiking irregularity exhibit different responses to the same stimulus. This suggests that neurons in each area can be classified into different groups possessing unique spiking statistics and corresponding functional properties.