Neural Computation
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Methods in Neuronal Modeling: From Ions to Networks
Methods in Neuronal Modeling: From Ions to Networks
Estimating a state-space model from point process observations
Neural Computation
Differences in spiking patterns among cortical neurons
Neural Computation
Information Geometry of Interspike Intervals in Spiking Neurons
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Estimating Spiking Irregularities Under Changing Environments
Neural Computation
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
A characterization of the time-rescaled gamma process as a model for spike trains
Journal of Computational Neuroscience
Kernel bandwidth optimization in spike rate estimation
Journal of Computational Neuroscience
Applying the multivariate time-rescaling theorem to neural population models
Neural Computation
Estimation of time-dependent input from neuronal membrane potential
Neural Computation
Optimizing time histograms for non-poissonian spike trains
Neural Computation
Mapping of visual receptive fields by tomographic reconstruction
Neural Computation
Information transmission using non-poisson regular firing
Neural Computation
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Cortical neurons in vivo had been regarded as Poisson spike generators that convey no information other than the rate of random firing. Recently, using a metric for analyzing local variation of interspike intervals, researchers have found that individual neurons express specific patterns in generating spikes, which may symbolically be termed regular, random, or bursty, rather invariantly in time. In order to study the dynamics of firing patterns in greater detail, we propose here a Bayesian method for estimating firing irregularity and the firing rate simultaneously for a given spike sequence, and we implement an algorithm that may render the empirical Bayesian estimation practicable for data comprising a large number of spikes. Application of this method to electrophysiological data revealed a subtle correlation between the degree of firing irregularity and the firing rate for individual neurons. Irregularity of firing did not deviate greatly around the low degree of dependence on the firing rate and remained practically unchanged for individual neurons in the cortical areas V1 and MT, whereas it fluctuated greatly in the lateral geniculate nucleus of the thalamus. This indicates the presence and absence of autocontrolling mechanisms for maintaining patterns of firing in the cortex and thalamus, respectively.