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Emery N Brown - One of the best experts on this subject based on the ideXlab platform.
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a Point Process characterization of electrodermal activity
International Conference of the IEEE Engineering in Medicine and Biology Society, 2018Co-Authors: Sandya Subramanian, Riccardo Barbieri, Emery N BrownAbstract:Electrodermal activity (EDA) is a measure of sympathetic activity using skin conductance that has applications in research and in clinical medicine. However, current EDA analysis does not have physiologically-based statistical models that use stochastic structure to provide nuanced insight into autonomic dynamics. Therefore, in this study, we analyzed the data of two healthy volunteers under controlled propofol sedation. We identified a novel statistical model for EDA and used a Point Process framework to track instantaneous dynamics. Our results demonstrate for the first time that Point Process models rooted in physiology and built upon inherent statistical structure of EDA pulses have the potential to accurately track instantaneous dynamics in sympathetic tone.
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neural spike train denoising by Point Process re weighted iterative smoothing
Asilomar Conference on Signals Systems and Computers, 2014Co-Authors: Behtash Babadi, Patrick L Purdon, Emery N BrownAbstract:An important problem in computational neuro-science is to design algorithms that can capture robustly abrupt changes in the conditional intensity function (CIF) of a stochastic Point Process model of neural spiking data. Towards this end, we advocate the use of a Point Process analogue of the total variation denoising algorithm, which trades off the Point Process likelihood with a total variation prior on the parameters of a log-linear model of the CIF. We propose an iteratively re-weighted least squares (IRLS) algorithm, termed Point Process Re-weighted Iterative SMoothing (PRISM), to solve the Point Process total variation denoising (PPTVD) problem. PRISM can be implemented using well-established Point Process smoothing algorithms, which are Point Process analogues of the Kalman smoother. We use a connection between the Expectation-Maximization (EM) algorithm and IRLS to prove that the sequence generated by PRISM converges and that its limit Point coincides with the unique stationary Point of the PPTVD problem. We apply PRISM to 123 and 41 neuronal units acquired from two separate epileptic patients during general anesthesia induced using the drug propofol. The PRISM algorithm is able to capture robustly the onset of loss of consciousness at the millisecond time scale.
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a granger causality measure for Point Process models of ensemble neural spiking activity
PLOS Computational Biology, 2011Co-Authors: Sanggyun Kim, Emery N Brown, David Putrino, S GhoshAbstract:The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a Point Process framework that enables Granger causality to be applied to Point Process data such as neural spike trains. The proposed framework uses the Point Process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the Point Process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to Point Process data, and has the potential to provide unique physiological insights when applied to neural spike trains.
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characterizing nonlinear heartbeat dynamics within a Point Process framework
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Zhe Chen, Emery N Brown, Riccardo BarbieriAbstract:Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a Point Process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-Gaussian time series. The proposed Point Process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the Point Process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings.
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application of dynamic Point Process models to cardiovascular control
BioSystems, 2008Co-Authors: Riccardo Barbieri, Emery N BrownAbstract:The development of statistical models that accurately describe the stochastic structure of biological signals is a fast growing area in quantitative research. In developing a novel statistical paradigm based on Bayes' theorem applied to Point Processes, we are focusing our recent research on characterizing the physiological mechanisms involved in cardiovascular control. Results from a tilt table study Point at our statistical framework as a valid model for the heart beat, as generated from complex mechanisms underlying cardiovascular control. The Point Process analysis provides new quantitative indices that could have important implications for research studies of cardiovascular and autonomic regulation and for monitoring of heart rate and heart rate variability measures in clinical settings.
Riccardo Barbieri - One of the best experts on this subject based on the ideXlab platform.
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a Point Process characterization of electrodermal activity
International Conference of the IEEE Engineering in Medicine and Biology Society, 2018Co-Authors: Sandya Subramanian, Riccardo Barbieri, Emery N BrownAbstract:Electrodermal activity (EDA) is a measure of sympathetic activity using skin conductance that has applications in research and in clinical medicine. However, current EDA analysis does not have physiologically-based statistical models that use stochastic structure to provide nuanced insight into autonomic dynamics. Therefore, in this study, we analyzed the data of two healthy volunteers under controlled propofol sedation. We identified a novel statistical model for EDA and used a Point Process framework to track instantaneous dynamics. Our results demonstrate for the first time that Point Process models rooted in physiology and built upon inherent statistical structure of EDA pulses have the potential to accurately track instantaneous dynamics in sympathetic tone.
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Point Process Respiratory Sinus Arrhythmia analysis during deep tissue pain stimulation
2011 Computing in Cardiology, 2011Co-Authors: Sandun Kodituwakku, Vitaly Napadow, Marco L Loggia, Riccardo BarbieriAbstract:We present an analysis of autonomic nervous system responses to deep tissue pain by using an instantaneous Point Process assessment of Heart Rate Variability (HRV) and Respiratory Sinus Arrhythmia (RSA). Ten subjects received pressure stimuli at 8 individually calibrated intensities (7 painful) over three separate runs. An inverse Gaussian Point Process framework modeled the R-R interval (RR) by defining a bivariate regression incorporating both past RRs and respiration values observed at the beats. Instantaneous indices of sympatho-vagal balance and RSA were estimated combining a maximum-likelihood algorithm with time-frequency analysis. The model was validated by Kolmogorov-Smirnov goodness-of-fit and independence tests. Results show that, in comparison to the resting period, all three pain runs elicited a significant decrease in RSA by over 21% (p=0.0547, 0.0234, 0.0547) indicating a reduced parasympathetic tone during pain, with RSA estimates negatively correlated with the calibrated stimulus intensity levels (slope = -0.4123, p=0.0633).
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characterizing nonlinear heartbeat dynamics within a Point Process framework
IEEE Transactions on Biomedical Engineering, 2010Co-Authors: Zhe Chen, Emery N Brown, Riccardo BarbieriAbstract:Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R-R interval dynamics based on a nonlinear Volterra-Wiener expansion within a Point Process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-Gaussian time series. The proposed Point Process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the Point Process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings.
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application of dynamic Point Process models to cardiovascular control
BioSystems, 2008Co-Authors: Riccardo Barbieri, Emery N BrownAbstract:The development of statistical models that accurately describe the stochastic structure of biological signals is a fast growing area in quantitative research. In developing a novel statistical paradigm based on Bayes' theorem applied to Point Processes, we are focusing our recent research on characterizing the physiological mechanisms involved in cardiovascular control. Results from a tilt table study Point at our statistical framework as a valid model for the heart beat, as generated from complex mechanisms underlying cardiovascular control. The Point Process analysis provides new quantitative indices that could have important implications for research studies of cardiovascular and autonomic regulation and for monitoring of heart rate and heart rate variability measures in clinical settings.
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characterizing nonlinear heartbeat dynamics within a Point Process framework
International Conference of the IEEE Engineering in Medicine and Biology Society, 2008Co-Authors: Zhe Chen, Emery N Brown, Riccardo BarbieriAbstract:Heartbeat intervals are known to have nonlinear and non-stationary dynamics. In this paper, we propose a nonlinear Volterra-Wiener expansion modeling of human heartbeat dynamics within a Point Process framework. Inclusion of second-order nonlinearity allows us to estimate dynamic bispectrum. The proposed probabilistic model was examined with two recorded heartbeat interval data sets. Preliminary results show that our model is beneficial to characterize the inherent nonlinearity of the heartbeat dynamics.
Uri T. Eden - One of the best experts on this subject based on the ideXlab platform.
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real time Point Process filter for multidimensional decoding problems using mixture models
Journal of Neuroscience Methods, 2021Co-Authors: Mohammad Reza Rezaei, Uri T. Eden, Kensuke Arai, Loren M Frank, Ali YousefiAbstract:There is an increasing demand for a computationally efficient and accurate Point Process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the Point Process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general Point-Process filter problem when the conditional intensity of a cell's spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of Point Process observation called marked Point-Process, which encompasses both clustered - mainly, called sorted - and clusterless - generally called unsorted or raw- spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional Point-Process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat's position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% highest probability coverage area (HPD) performance metrics. Though the marked-Point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.
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continuous prediction of cognitive state using a marked Point Process modeling framework
International Conference of the IEEE Engineering in Medicine and Biology Society, 2019Co-Authors: Yalda Amidi, Uri T. Eden, Angelique C. Paulk, Darin D. Dougherty, Alik S. Widge, Sydney S Cash, Ali YousefiAbstract:Behavioral outcomes in many cognitive tasks are often recorded in a trial structure at discrete times. To adapt to this structure, neural encoder and decoder models have been built to take into account the trial organization to characterize the connection between brain dynamics and behavior, e.g. through latent dynamical models. The challenge of these models is that they are limited to discrete trial times while neural data is continuous. Here, we propose a marked-Point Process framework to characterize multivariate behavioral outcomes recorded during a trial-structured cognitive task, to build an estimation of cognitive state at a fine time resolution. We propose a state-space marked-Point Process modeling framework to characterize the relationship between observed behavior and underlying dynamical cognitive Processes. We define the framework for a class of behavioral readouts by a response time and a discrete mark signifying an observed binary decision, and develop the state estimation and system identification steps. We define the filter and smoother for the marked-Point Process observation and develop an EM algorithm to estimate the model’s free parameters. We demonstrate this modeling approach in a behavioral readout captured while participants perform an emotional conflict resolution task (ECR) and show that we can estimate underlying cognitive Processes at a fine temporal resolution beyond the trial by trial approach.
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real time Point Process filter for multidimensional decoding problems using mixture models
bioRxiv, 2018Co-Authors: Ali Yousefi, Mohammad Reza Rezaei, Kensuke Arai, Loren M Frank, Uri T. EdenAbstract:Abstract There is an increasing demand for a computationally efficient and accurate Point Process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the Point Process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general Point-Process filter problem when the conditional intensity of a cell’s spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of Point Process observation called marked Point-Process, which encompasses both clustered – mainly, called sorted – and clusterless – generally called unsorted or raw– spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional Point-Process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat’s position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% HPD coverage performance metrics. Though the marked-Point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.
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decoding position from multiunit activity using a marked Point Process filter
BMC Neuroscience, 2015Co-Authors: Xinyi Deng, Loren M Frank, Uri T. EdenAbstract:Traditionally, experiments designed to study the role of specific spike patterns in learning and memory tasks take one of two forms, 1) observational studies that characterize statistical properties of neural activity during such tasks or 2) interventional studies that broadly alter neural activities over an entire neural population or brain region. This work is part of a larger project to allow investigators to manipulate neural populations in a content-specific way, altering spiking activity related to certain learning and memory patterns while leaving activity related to other patterns intact. One fundamental challenge of this work is to decode the information content of specific spike sequences in real-time. Previously, we have used Point Process theory to develop efficient decoding algorithms based on spike train observations. However these algorithms assume the spike trains have been accurately sorted ahead of time, which is not possible for real-time decoding. Here we present a new Point Process decoding algorithm that does not require multiunit signals to be sorted. We use the theory of marked Point Processes to characterize the relationship between the coding properties of multiunit activity and features of the spike waveforms [1-3]. Using Bayes’ rule, we compute the posterior distribution of a signal to decode given multiunit activity from a neural population. We first characterize the spiking activity of a neural population using the conditional intensity function for marked Point Processes. We then construct Point Process filters to iteratively calculate the full posterior density of a signal. We illustrate our approach with a simulation study as well as with experimental data recorded in the hippocampus of a rat performing a spatial memory task. Our decoding framework is used to reconstruct the animal’s position from unsorted multiunit spiking activity. We then compare the quality of fit of our decoding framework to that of a traditional spike-sorting and decoding framework. Our analyses show that the proposed decoding algorithm performs as well as or better than algorithms based on sorted single-unit activity. These results provide a mechanism for content-specific manipulations of population activity in hippocampus.
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construction of Point Process adaptive filter algorithms for neural systems using sequential monte carlo methods
IEEE Transactions on Biomedical Engineering, 2007Co-Authors: A Ergun, Riccardo Barbieri, Uri T. Eden, Matthew A Wilson, Emery N BrownAbstract:The stochastic state Point Process filter (SSPPF) and steepest descent Point Process filter (SDPPF) are adaptive filter algorithms for state estimation from Point Process observations that have been used to track neural receptive field plasticity and to decode the representations of biological signals in ensemble neural spiking activity. The SSPPF and SDPPF are constructed using, respectively, Gaussian and steepest descent approximations to the standard Bayes and Chapman-Kolmogorov (BCK) system of filter equations. To extend these approaches for constructing Point Process adaptive filters, we develop sequential Monte Carlo (SMC) approximations to the BCK equations in which the SSPPF and SDPPF serve as the proposal densities. We term the two new SMC Point Process filters SMC-PPFS and SMC-PPFD , respectively. We illustrate the new filter algorithms by decoding the wind stimulus magnitude from simulated neural spiking activity in the cricket cercal system. The SMC-PPFS and SMC-PPFD provide more accurate state estimates at low number of particles than a conventional bootstrap SMC filter algorithm in which the state transition probability density is the proposal density. We also use the SMC-PPFS algorithm to track the temporal evolution of a spatial receptive field of a rat hippocampal neuron recorded while the animal foraged in an open environment. Our results suggest an approach for constructing Point Process adaptive filters using SMC methods
Loren M Frank - One of the best experts on this subject based on the ideXlab platform.
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real time Point Process filter for multidimensional decoding problems using mixture models
Journal of Neuroscience Methods, 2021Co-Authors: Mohammad Reza Rezaei, Uri T. Eden, Kensuke Arai, Loren M Frank, Ali YousefiAbstract:There is an increasing demand for a computationally efficient and accurate Point Process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the Point Process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general Point-Process filter problem when the conditional intensity of a cell's spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of Point Process observation called marked Point-Process, which encompasses both clustered - mainly, called sorted - and clusterless - generally called unsorted or raw- spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional Point-Process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat's position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% highest probability coverage area (HPD) performance metrics. Though the marked-Point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.
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real time Point Process filter for multidimensional decoding problems using mixture models
bioRxiv, 2018Co-Authors: Ali Yousefi, Mohammad Reza Rezaei, Kensuke Arai, Loren M Frank, Uri T. EdenAbstract:Abstract There is an increasing demand for a computationally efficient and accurate Point Process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the Point Process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general Point-Process filter problem when the conditional intensity of a cell’s spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of Point Process observation called marked Point-Process, which encompasses both clustered – mainly, called sorted – and clusterless – generally called unsorted or raw– spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional Point-Process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat’s position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% HPD coverage performance metrics. Though the marked-Point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.
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decoding position from multiunit activity using a marked Point Process filter
BMC Neuroscience, 2015Co-Authors: Xinyi Deng, Loren M Frank, Uri T. EdenAbstract:Traditionally, experiments designed to study the role of specific spike patterns in learning and memory tasks take one of two forms, 1) observational studies that characterize statistical properties of neural activity during such tasks or 2) interventional studies that broadly alter neural activities over an entire neural population or brain region. This work is part of a larger project to allow investigators to manipulate neural populations in a content-specific way, altering spiking activity related to certain learning and memory patterns while leaving activity related to other patterns intact. One fundamental challenge of this work is to decode the information content of specific spike sequences in real-time. Previously, we have used Point Process theory to develop efficient decoding algorithms based on spike train observations. However these algorithms assume the spike trains have been accurately sorted ahead of time, which is not possible for real-time decoding. Here we present a new Point Process decoding algorithm that does not require multiunit signals to be sorted. We use the theory of marked Point Processes to characterize the relationship between the coding properties of multiunit activity and features of the spike waveforms [1-3]. Using Bayes’ rule, we compute the posterior distribution of a signal to decode given multiunit activity from a neural population. We first characterize the spiking activity of a neural population using the conditional intensity function for marked Point Processes. We then construct Point Process filters to iteratively calculate the full posterior density of a signal. We illustrate our approach with a simulation study as well as with experimental data recorded in the hippocampus of a rat performing a spatial memory task. Our decoding framework is used to reconstruct the animal’s position from unsorted multiunit spiking activity. We then compare the quality of fit of our decoding framework to that of a traditional spike-sorting and decoding framework. Our analyses show that the proposed decoding algorithm performs as well as or better than algorithms based on sorted single-unit activity. These results provide a mechanism for content-specific manipulations of population activity in hippocampus.
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dynamic analysis of neural encoding by Point Process adaptive filtering
Neural Computation, 2004Co-Authors: Uri T. Eden, Loren M Frank, Riccardo Barbieri, Victor Solo, Emery N BrownAbstract:Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt their representations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal Processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use 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. We derive Point Process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new filters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the receptive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for Point Process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.
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an analysis of neural receptive field plasticity by Point Process adaptive filtering
Proceedings of the National Academy of Sciences of the United States of America, 2001Co-Authors: Emery N Brown, Loren M Frank, David P Nguyen, Matthew A Wilson, Victor SoloAbstract:Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal Processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on Point Process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a Point Process spike train model. We apply the Point Process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the Appendix. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point Process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields.
Ali Yousefi - One of the best experts on this subject based on the ideXlab platform.
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real time Point Process filter for multidimensional decoding problems using mixture models
Journal of Neuroscience Methods, 2021Co-Authors: Mohammad Reza Rezaei, Uri T. Eden, Kensuke Arai, Loren M Frank, Ali YousefiAbstract:There is an increasing demand for a computationally efficient and accurate Point Process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the Point Process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general Point-Process filter problem when the conditional intensity of a cell's spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of Point Process observation called marked Point-Process, which encompasses both clustered - mainly, called sorted - and clusterless - generally called unsorted or raw- spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional Point-Process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat's position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% highest probability coverage area (HPD) performance metrics. Though the marked-Point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.
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continuous prediction of cognitive state using a marked Point Process modeling framework
International Conference of the IEEE Engineering in Medicine and Biology Society, 2019Co-Authors: Yalda Amidi, Uri T. Eden, Angelique C. Paulk, Darin D. Dougherty, Alik S. Widge, Sydney S Cash, Ali YousefiAbstract:Behavioral outcomes in many cognitive tasks are often recorded in a trial structure at discrete times. To adapt to this structure, neural encoder and decoder models have been built to take into account the trial organization to characterize the connection between brain dynamics and behavior, e.g. through latent dynamical models. The challenge of these models is that they are limited to discrete trial times while neural data is continuous. Here, we propose a marked-Point Process framework to characterize multivariate behavioral outcomes recorded during a trial-structured cognitive task, to build an estimation of cognitive state at a fine time resolution. We propose a state-space marked-Point Process modeling framework to characterize the relationship between observed behavior and underlying dynamical cognitive Processes. We define the framework for a class of behavioral readouts by a response time and a discrete mark signifying an observed binary decision, and develop the state estimation and system identification steps. We define the filter and smoother for the marked-Point Process observation and develop an EM algorithm to estimate the model’s free parameters. We demonstrate this modeling approach in a behavioral readout captured while participants perform an emotional conflict resolution task (ECR) and show that we can estimate underlying cognitive Processes at a fine temporal resolution beyond the trial by trial approach.
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real time Point Process filter for multidimensional decoding problems using mixture models
bioRxiv, 2018Co-Authors: Ali Yousefi, Mohammad Reza Rezaei, Kensuke Arai, Loren M Frank, Uri T. EdenAbstract:Abstract There is an increasing demand for a computationally efficient and accurate Point Process filter solution for real-time decoding of population spiking activity in multidimensional spaces. Real-time tools for neural data analysis, specifically real-time neural decoding solutions open doors for developing experiments in a closed-loop setting and more versatile brain-machine interfaces. Over the past decade, the Point Process filter has been successfully applied in the decoding of behavioral and biological signals using spiking activity of an ensemble of cells; however, the filter solution is computationally expensive in multi-dimensional filtering problems. Here, we propose an approximate filter solution for a general Point-Process filter problem when the conditional intensity of a cell’s spiking activity is characterized using a Mixture of Gaussians. We propose the filter solution for a broader class of Point Process observation called marked Point-Process, which encompasses both clustered – mainly, called sorted – and clusterless – generally called unsorted or raw– spiking activity. We assume that the posterior distribution on each filtering time-step can be approximated using a Gaussian Mixture Model and propose a computationally efficient algorithm to estimate the optimal number of mixture components and their corresponding weights, mean, and covariance estimates. This algorithm provides a real-time solution for multi-dimensional Point-Process filter problem and attains accuracy comparable to the exact solution. Our solution takes advantage of mixture dropping and merging algorithms, which collectively control the growth of mixture components on each filtering time-step. We apply this methodology in decoding a rat’s position in both 1-D and 2-D spaces using clusterless spiking data of an ensemble of rat hippocampus place cells. The approximate solution in 1-D and 2-D decoding is more than 20 and 4,000 times faster than the exact solution, while their accuracy in decoding a rat position only drops by less than 9% and 4% in RMSE and 95% HPD coverage performance metrics. Though the marked-Point filter solution is better suited for real-time decoding problems, we discuss how the filter solution can be applied to sorted spike data to better reflect the proposed methodology versatility.
