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Found 8 papers, 2 papers with code
Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit
Coordinated ensemble spiking activity is widely observable in neural recordings and central in the study of population codes, with hypothesized roles including robust stimulus representation, interareal communication of neural information, and learning and memory formation.
Multitaper Analysis of Evolutionary Spectra from Multivariate Spiking Observations
Extracting the spectral representations of the neural processes that underlie spiking activity is key to understanding how the brain rhythms mediate cognitive functions.
Information Theory Systems and Control Systems and Control Information Theory Methodology
Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution
In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events.
Sampling Requirements for Stable Autoregressive Estimation
Assuming that the parameters are compressible, we analyze the performance of the $\ell_1$-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime.
Recursive Sparse Point Process Regression with Application to Spectrotemporal Receptive Field Plasticity Analysis
We consider the problem of estimating the sparse time-varying parameter vectors of a point process model in an online fashion, where the observations and inputs respectively consist of binary and continuous time series.
Robust Estimation of Self-Exciting Generalized Linear Models with Application to Neuronal Modeling
We consider the problem of estimating self-exciting generalized linear models from limited binary observations, where the history of the process serves as the covariate.
A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment
Humans are able to segregate auditory objects in a complex acoustic scene, through an interplay of bottom-up feature extraction and top-down selective attention in the brain.
Exact and Stable Recovery of Sequences of Signals with Sparse Increments via Differential _1-Minimization
We consider the problem of recovering a sequence of vectors, $(x_k)_{k=0}^K$, for which the increments $x_k-x_{k-1}$ are $S_k$-sparse (with $S_k$ typically smaller than $S_1$), based on linear measurements $(y_k = A_k x_k + e_k)_{k=1}^K$, where $A_k$ and $e_k$ denote the measurement matrix and noise, respectively.
