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Asymptotics for Linear Processes
A method of deriving asymptotics for linear processes is introduced which uses an explicit algebraic decomposition of the linear filter. The method leads to substantial simplifications in the…
Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering
This work uses 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 and suggests a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.
Motion and Ballistocardiogram Artifact Removal for Interleaved Recording of EEG and EPs during MRI
This paper introduces a method for motion artifact cancellation based on adaptive filtering and takes advantage of piezoelectric motion sensor information to estimate the motion artifact noise, which outperforms the simple band-pass filter for alpha detection and is also capable of reducing noise within the frequency band of interest.
Adaptive Signal Processing Algorithms: Stability and Performance
Part I: Algorithm Analysis: Deterministic Global Theory; Part II: Stochastic Averaging; Part III: Mixed Time Scale.
On $l_q$ Optimization and Matrix Completion
This paper bridges the gap between the two penalties of the rank penalty and proposes the 0, 0, 1 penalized least squares problem for matrix completion, a non-trivial convergence result.
Dimension Estimation in Noisy PCA With SURE and Random Matrix Theory
This paper proposes to use Stein's unbiased risk estimator (SURE) to estimate, with some assistance from RMT, the number of principal components in PCA, and is compared to BIC and the Laplace method.
Dynamic Analyses of Information Encoding in Neural Ensembles
A general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal is presented and an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted is suggested.
Intrinsic random functions and the paradox of l/f noise
- V. Solo
- 1 February 1992
By means of a generalized Fejer theorem, certain stationary increment random functions and random fields are shown to possess nonintegrable time invariant spectra. The periodogram computed from a…
An analysis of neural receptive field plasticity by point process adaptive filtering
- E. Brown, D. P. Nguyen, L. Frank, M. Wilson, V. Solo
- BiologyProceedings of the National Academy of Sciences…
- 9 October 2001
An adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity is presented and an instantaneous steepest descent algorithm is derived by using as the criterion function the instantaneous log likelihood of a point process spike train model.
On the stability of slowly time-varying linear systems
- V. Solo
- MathematicsMath. Control. Signals Syst.
- 1 December 1994
The eigenvalues of A(t) are allowed to “wander” into the right half-plane as long as “on average” they are strictly in the left half-planes.