no code implementations • NeurIPS 2021 • Shoutik Mukherjee, Behtash Babadi
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.
1 code implementation • 22 Jun 2019 • Anuththara Rupasinghe, Behtash Babadi
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
no code implementations • 20 Oct 2016 • Abbas Kazemipour, Ji Liu, Patrick Kanold, Min Wu, Behtash Babadi
In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events.
no code implementations • 4 May 2016 • Abbas Kazemipour, Sina Miran, Piya Pal, Behtash Babadi, Min Wu
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.
no code implementations • 16 Jul 2015 • Alireza Sheikhattar, Jonathan B. Fritz, Shihab A. Shamma, Behtash Babadi
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.
1 code implementation • 14 Jul 2015 • Abbas Kazemipour, Min Wu, Behtash Babadi
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.
no code implementations • NeurIPS 2014 • Sahar Akram, Jonathan Z. Simon, Shihab A. Shamma, Behtash Babadi
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.
no code implementations • NeurIPS 2012 • Demba Ba, Behtash Babadi, Patrick Purdon, Emery Brown
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.