Neuroscience

Auditory cortex signal processing; brain development and plasticity; sensorimotor integration; neuromechanical systems; speech recognition; neuromorphic sensors, control and VLSI; computational neuroscience; cell-based sensors

ISR is a longtime leader in advancing understanding of neural processing in the brain's auditory system, including speech processing and sound localization. Our faculty and students have made neuroscience-based advances in signal processing principles and solutions, and have developed novel neuromorphic architectures for intelligent systems. We are active in NIH BRAIN Initiative research, using neural modeling to establish causal links between neural activity and behavior. We also developed multi-pitch tracking for adverse environments, a communication technology that pulls speech out of noise and can radically improve sound quality over cell phones and in hearing aids. ISR researchers have been key in the establishment of the university’s Brain and Behavior Initiative.

Recent publications

2020

Multitaper Analysis of Semi-Stationary Spectra from Multivariate Neuronal Spiking Observations

Anuththara Rupasinghe, Behtash Babadi

Extracting the spectral representations of neural processes that underlie spiking activity is key to understanding how brain rhythms mediate cognitive functions. This work develops a multitaper spectral estimation methodology that can be directly applied to multivariate spiking observations to extract the semi-stationary spectral density of the latent non-stationary processes that govern spiking activity.

IEEE Transactions on Signal Processing

Robust inference of neuronal correlations from blurred and noisy spiking observations

Anuththara Rupasinghe, Behtash Babadi

Proposes an algorithm to directly estimate neuronal correlations from ensemble two-photon imaging data, by integrating techniques from point process modeling and variational Bayesian inference, with no recourse to intermediate spike deconvolution.

2020 54th Annual Conference on Information Sciences and Systems

Neuro-current response functions: A unified approach to MEG source analysis under the continuous stimuli paradigm

Proloy Das, Christian Brodbeck, Jonathan Z. Simon, Behtash Babadi

A principled modeling and estimation paradigm for MEG source analysis tailored to extracting the cortical origin of electrophysiological responses to continuous stimuli.

NeuroImage

Granger Causal Inference from Indirect Low-Dimensional Measurements with Application to MEG Functional Connectivity Analysis

Behrad Soleimani, Proloy Das, Joshua Kulasingham, Jonathan Z. Simon, Behtash Babadi

The authors consider the problem of determining Granger causal influences among sources that are indirectly observed through low-dimensional and noisy linear projections. They model the source dynamics as sparse vector autoregressive processes and estimate the model parameters directly from the observations, with no recourse to intermediate source localization.

54th Conference on Information Sciences and Systems

2020

A latent variable approach to decoding neural population activity

Matthew Whiteway, Bruno Averbeck, Daniel Butts

The authors propose a new decoding framework that exploits the low-dimensional structure of neural population variability by removing correlated variability unrelated to the decoded variable, then decoding the resulting denoised activity.

biorXiv.org

2019

The quest for interpretable models of neural population activity

Matthew Whiteway, Daniel Butts

Research explores latent variable models for neural recording coordinated activity of large neuron populations in brain function.

Current Opinion in Neurobiology

2020

Neuro-current response functions: A unified approach to MEG source analysis under the continuous stimuli paradigm

Proloy Das, Christian Brodbeck, Jonathan Z. Simon, Behtash Babadi

A principled modeling and estimation paradigm for MEG source analysis tailored to extracting the cortical origin of electrophysiological responses to continuous stimuli.

NeuroImage

Granger Causal Inference from Indirect Low-Dimensional Measurements with Application to MEG Functional Connectivity Analysis

Behrad Soleimani, Proloy Das, Joshua Kulasingham, Jonathan Z. Simon, Behtash Babadi

The authors consider the problem of determining Granger causal influences among sources that are indirectly observed through low-dimensional and noisy linear projections. They model the source dynamics as sparse vector autoregressive processes and estimate the model parameters directly from the observations, with no recourse to intermediate source localization.

54th Conference on Information Sciences and Systems

 

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