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

A Deep 2-Dimensional Dynamical Spiking Neuronal Network for Temporal Encoding trained with STDP

Matthew Evanusa, Cornelia Fermüller, Yiannis Aloimonos

The researchers show that a large, deep layered spiking neural network with dynamical, chaotic activity mimicking the mammalian cortex with biologically-inspired learning rules, such as STDP, is capable of encoding information from temporal data.

arXiv.org

Hybrid Backpropagation Parallel Reservoir Networks

Matthew Evanusa, Snehesh Shrestha, Michelle Girvan, Cornelia Fermüller, Yiannis Aloimonos

Demonstrates the use of a backpropagation hybrid mechanism for parallel reservoir computingwith a meta ring structure and its application on a real-world gesture recognition dataset. This mechanism can be used as an alternative to state of the art recurrent neural networks, LSTMs and GRUs.

arXiv.org

2020

Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise

Sina Miran, Behtash Babadi, Alessandro Presacco, Jonathan Simon, Michael Fu, Steven Marcus

This research develops efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting.

PLOS Computational Biology

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

A Deep 2-Dimensional Dynamical Spiking Neuronal Network for Temporal Encoding trained with STDP

Matthew Evanusa, Cornelia Fermüller, Yiannis Aloimonos

The researchers show that a large, deep layered spiking neural network with dynamical, chaotic activity mimicking the mammalian cortex with biologically-inspired learning rules, such as STDP, is capable of encoding information from temporal data.

arXiv.org

Hybrid Backpropagation Parallel Reservoir Networks

Matthew Evanusa, Snehesh Shrestha, Michelle Girvan, Cornelia Fermüller, Yiannis Aloimonos

Demonstrates the use of a backpropagation hybrid mechanism for parallel reservoir computingwith a meta ring structure and its application on a real-world gesture recognition dataset. This mechanism can be used as an alternative to state of the art recurrent neural networks, LSTMs and GRUs.

arXiv.org

2020

Neural speech restoration at the cocktail party: Auditory cortex recovers masked speech of both attended and ignored speakers

Christian Brodbeck, Alex Jiao, L. Elliot Hong, Jonathan Simon

Humans are remarkably skilled at listening to one speaker out of an acoustic mixture of several speech sources. Two speakers are easily segregated, even without binaural cues, but the neural mechanisms underlying this ability are not well understood. One possibility is that early cortical processing performs a spectrotemporal decomposition of the acoustic mixture, allowing the attended speech to be reconstructed via optimally weighted recombinations that discount spectrotemporal regions where sources heavily overlap. Using human magnetoencephalography (MEG) responses to a 2-talker mixture, the authors show evidence for an alternative possibility, in which early, active segregation occurs even for strongly spectrotemporally overlapping regions.

PLOS Biology

High Gamma Cortical Processing of Continuous Speech in Younger and Older Listeners

Peng Zan, Alessandro Presacco, Samira Anderson, Jonathan Simon

Aging is associated with an exaggerated representation of the speech envelope in auditory cortex. The relationship between this age-related exaggerated response and a listener’s ability to understand speech in noise remains an open question. Here, information-theory-based analysis methods are applied to magnetoencephalography recordings of human listeners, investigating their cortical responses to continuous speech, using the novel nonlinear measure of phase-locked mutual information between the speech stimuli and cortical responses. The cortex of older listeners shows an exaggerated level of mutual information, compared with younger listeners, for both attended and unattended speakers.

Journal of Neurophysiology

Exaggerated cortical representation of speech in older listeners: mutualinformation analysis

Peng Zan, Alessandro Presacco, Samira Anderson, Jonathan Simon

Information-theory-based analysis methods are applied to magnetoencephalography recordings of human listeners, investigating their cortical responses to continuous speech, using the novel nonlinear measure of phase-locked mutual information between the speech stimuli and cortical responses. This information-theory-based analysis provides new, and less coarse-grained, results regarding age-related change in auditory cortical speech processing, and its correlation with cognitive measures, com-pared with related linear measures.

Journal of Neurophysiology

Continuous Speech Processing

Christian Brodbeck, Jonathan Simon

Speech processing in the human brain is grounded in non-specific auditory processing in the general mammalian brain, but relies on human-specific adaptations for processing speech and language. For this reason, many recent neurophysiological investigations of speech processing have turned to the human brain, with an emphasis on continuous speech. This article considers the substantial progress that has been made using the phenomenon of 'neural speech tracking,' in which neurophysiological responses time-lock to the rhythm of auditory (and other) features in continuous speech.

Current Opinion in Physiology

Dynamic estimation of auditory temporal response functions via state-space models with Gaussian mixture process noise

Sina Miran, Behtash Babadi, Alessandro Presacco, Jonathan Simon, Michael Fu, Steven Marcus

This research develops efficient algorithms for inferring the parameters of a general class of Gaussian mixture process noise models from noisy and limited observations, and utilize them in extracting the neural dynamics that underlie auditory processing from magnetoencephalography (MEG) data in a cocktail party setting.

PLOS Computational Biology

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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