Bayesian forecasting and dynamic models (2nd ed.)
Bayesian forecasting and dynamic models (2nd ed.)
A unifying review of linear Gaussian models
Neural Computation
Estimation, information and neural signals
Estimation, information and neural signals
Latent variable models for neural data analysis
Latent variable models for neural data analysis
Rate Limitations of Unitary Event Analysis
Neural Computation
Dynamic analysis of neural encoding by point process adaptive filtering
Neural Computation
Dynamic Analyses of Information Encoding in Neural Ensembles
Neural Computation
Neural Computation
Estimating instantaneous irregularity of neuronal firing
Neural Computation
State-space algorithms for estimating spike rate functions
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Firing rate estimation using an approximate Bayesian method
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
A new look at state-space models for neural data
Journal of Computational Neuroscience
Kernel bandwidth optimization in spike rate estimation
Journal of Computational Neuroscience
Journal of Computational Neuroscience
Encoding of brain state changes in local field potentials modulated by motor behaviors
Journal of Computational Neuroscience
Detection of hidden structures in nonstationary spike trains
Neural Computation
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
Information transmission using non-poisson regular firing
Neural Computation
An overview of bayesian methods for neural spike train analysis
Computational Intelligence and Neuroscience - Special issue on Modeling and Analysis of Neural Spike Trains
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A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.