Adaptive signal processing algorithms: stability and performance
Adaptive signal processing algorithms: stability and performance
Adaptive filter theory (3rd ed.)
Adaptive filter theory (3rd ed.)
Estimating a state-space model from point process observations
Neural Computation
A Self-Organizing Computing Network for Decision-Making in Data Sets with a Diversity of Data Types
IEEE Transactions on Knowledge and Data Engineering
The computational structure of spike trains
Neural Computation
A new look at state-space models for neural data
Journal of Computational Neuroscience
Journal of Computational Neuroscience
Comparison of brain---computer interface decoding algorithms in open-loop and closed-loop control
Journal of Computational Neuroscience
Applying the multivariate time-rescaling theorem to neural population models
Neural Computation
Computing confidence intervals for point process models
Neural Computation
Estimation of time-dependent input from neuronal membrane potential
Neural Computation
Stochastic optimal control as a theory of brain-machine interface operation
Neural Computation
Inferring evoked brain connectivity through adaptive perturbation
Journal of Computational Neuroscience
Dynamic analysis of naive adaptive brain-machine interfaces
Neural Computation
Dynamic analysis of naive adaptive brain-machine interfaces
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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Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt their representations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. We derive point process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new filters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the receptive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for point process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.