Maximum likelihood estimations in a nonlinear self-exciting point process model
Biological Cybernetics
Disambiguating different covariation types
Neural Computation
Correlations without synchrony
Neural Computation
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Dynamic Analyses of Information Encoding in Neural Ensembles
Neural Computation
Distinguishing Causal Interactions in Neural Populations
Neural Computation
Spike train decoding without spike sorting
Neural Computation
Journal of Cognitive Neuroscience
Multivariate autoregressive modeling and granger causality analysis of multiple spike trains
Computational Intelligence and Neuroscience - Special issue on signal processing for neural spike trains
Causal pattern recovery from neural spike train data using the Snap Shot Score
Journal of Computational Neuroscience
Journal of Computational Neuroscience
CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains
Journal of Computational Neuroscience
Journal of Computational Neuroscience
Applying the multivariate time-rescaling theorem to neural population models
Neural Computation
A systematic method for configuring vlsi networks of spiking neurons
Neural Computation
Dynamic state and parameter estimation applied to neuromorphic systems
Neural Computation
Predicting single-neuron activity in locally connected networks
Neural Computation
Point-process principal components analysis via geometric optimization
Neural Computation
Encoding through patterns: Regression tree-based neuronal population models
Neural Computation
Journal of Computational Neuroscience
Likelihood methods for point processes with refractoriness
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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Analyzing the dependencies between spike trains is an important step in understanding how neurons work in concert to represent biological signals. Usually this is done for pairs of neurons at a time using correlation-based techniques. Chornoboy, Schramm, and Karr (1988) proposed maximum likelihood methods for the simultaneous analysis of multiple pair-wise interactions among an ensemble of neurons. One of these methods is an iterative, continuous-time estimation algorithm for a network likelihood model formulated in terms of multiplicative conditional intensity functions. We devised a discrete-time version of this algorithm that includes a new, efficient computational strategy, a principled method to compute starting values, and a principled stopping criterion. In an analysis of simulated neural spike trains from ensembles of interacting neurons, the algorithm recovered the correct connectivity matrices and interaction parameters. In the analysis of spike trains from an ensemble of rat hippocampal place cells, the algorithm identified a connectivity matrix and interaction parameters consistent with the pattern of conjoined firing predicted by the overlap of the neurons' spatial receptive fields. These results suggest that the network likelihood model can be an efficient tool for the analysis of ensemble spiking activity.