The Experts below are selected from a list of 13944 Experts worldwide ranked by ideXlab platform
Jason R. Potas - One of the best experts on this subject based on the ideXlab platform.
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dorsal column nuclei Neural signal features permit robust machine learning of natural tactile and proprioception dominated stimuli
Frontiers in Systems Neuroscience, 2020Co-Authors: Alastair J Loutit, Jason R. PotasAbstract:Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, the restoration of somatosensory feedback is required. We used feature-learnability, a machine-learning approach, to assess signal features for their capacity to enhance Decoding performance of Neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei (DCN) in urethane-anesthetized rats. The highest performing individual feature, spike amplitude, classified somatosensory DCN signals with 70% accuracy. The highest accuracy achieved was 87% using 13 features that were extracted from both high and low-frequency (LF) bands of DCN signals. In general, high-frequency (HF) features contained the most information about peripheral somatosensory events, but when features were acquired from short time-windows, classification accuracy was significantly improved by adding LF features to the feature set. We found that proprioception-dominated stimuli generalize across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to Neural Decoding changes over the time-course of dynamic somatosensory events. These findings may inform the biomimetic design of artificial stimuli that can activate the DCN to substitute somatosensory feedback. Although, we investigated somatosensory structures, the feature set we investigated may also prove useful for Decoding other (e.g., motor) Neural signals.
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novel Neural signal features permit robust machine learning of natural tactile and proprioception dominated dorsal column nuclei signals
bioRxiv, 2019Co-Authors: Alastair J Loutit, Jason R. PotasAbstract:Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, restoration of somatosensory feedback and advances in Neural Decoding of motor control-related brain signals are required. We used a machine-learning approach to assess signal features for their capacity to enhance Decoding performance of Neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei in urethane-anaesthetised rats. We determined signal features that are highly informative for Decoding somatosensory stimuli, yet these appear underutilised in neuroprosthetic applications. We found that proprioception-dominated stimuli generalise across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to Neural Decoding changes over a time-course of dynamic somatosensory events. These findings may improve Neural Decoding for various applications including novel neuroprosthetic design.
Alastair J Loutit - One of the best experts on this subject based on the ideXlab platform.
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dorsal column nuclei Neural signal features permit robust machine learning of natural tactile and proprioception dominated stimuli
Frontiers in Systems Neuroscience, 2020Co-Authors: Alastair J Loutit, Jason R. PotasAbstract:Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, the restoration of somatosensory feedback is required. We used feature-learnability, a machine-learning approach, to assess signal features for their capacity to enhance Decoding performance of Neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei (DCN) in urethane-anesthetized rats. The highest performing individual feature, spike amplitude, classified somatosensory DCN signals with 70% accuracy. The highest accuracy achieved was 87% using 13 features that were extracted from both high and low-frequency (LF) bands of DCN signals. In general, high-frequency (HF) features contained the most information about peripheral somatosensory events, but when features were acquired from short time-windows, classification accuracy was significantly improved by adding LF features to the feature set. We found that proprioception-dominated stimuli generalize across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to Neural Decoding changes over the time-course of dynamic somatosensory events. These findings may inform the biomimetic design of artificial stimuli that can activate the DCN to substitute somatosensory feedback. Although, we investigated somatosensory structures, the feature set we investigated may also prove useful for Decoding other (e.g., motor) Neural signals.
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novel Neural signal features permit robust machine learning of natural tactile and proprioception dominated dorsal column nuclei signals
bioRxiv, 2019Co-Authors: Alastair J Loutit, Jason R. PotasAbstract:Neural prostheses enable users to effect movement through a variety of actuators by translating brain signals into movement control signals. However, to achieve more natural limb movements from these devices, restoration of somatosensory feedback and advances in Neural Decoding of motor control-related brain signals are required. We used a machine-learning approach to assess signal features for their capacity to enhance Decoding performance of Neural signals evoked by natural tactile and proprioceptive somatosensory stimuli, recorded from the surface of the dorsal column nuclei in urethane-anaesthetised rats. We determined signal features that are highly informative for Decoding somatosensory stimuli, yet these appear underutilised in neuroprosthetic applications. We found that proprioception-dominated stimuli generalise across animals better than tactile-dominated stimuli, and we demonstrate how information that signal features contribute to Neural Decoding changes over a time-course of dynamic somatosensory events. These findings may improve Neural Decoding for various applications including novel neuroprosthetic design.
Ali Yousefi - One of the best experts on this subject based on the ideXlab platform.
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closed loop enhancement and Neural Decoding of cognitive control in humans
Nature Biomedical Engineering, 2021Co-Authors: Ishita Basu, Ali Yousefi, Britni Crocker, Rina Zelmann, Angelique C Paulk, Noam Peled, Kristen K Ellard, Daniel S Weisholtz, Rees G CosgroveAbstract:Deficits in cognitive control-that is, in the ability to withhold a default pre-potent response in favour of a more adaptive choice-are common in depression, anxiety, addiction and other mental disorders. Here we report proof-of-concept evidence that, in participants undergoing intracranial epilepsy monitoring, closed-loop direct stimulation of the internal capsule or striatum, especially the dorsal sites, enhances the participants' cognitive control during a conflict task. We also show that closed-loop stimulation upon the detection of lapses in cognitive control produced larger behavioural changes than open-loop stimulation, and that task performance for single trials can be directly decoded from the activity of a small number of electrodes via Neural features that are compatible with existing closed-loop brain implants. Closed-loop enhancement of cognitive control might remediate underlying cognitive deficits and aid the treatment of severe mental disorders.
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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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deep recurrent Neural network and point process filter approaches in multidimensional Neural Decoding problems
bioRxiv, 2020Co-Authors: Mohammad Reza Rezaei, Behzad Nazari, Saeid Sadri, Ali YousefiAbstract:Recent technological and experimental advances in recording from Neural systems have led to a significant increase in the type and volume of data being collected in neuroscience experiments. This brings an increasing demand for development of appropriate analytical tools to analyze large scale neuroscience data. Simultaneously, advancement in deep Neural networks (DNNs) and statistical modeling frameworks have provided new techniques for analysis of diverse forms of neuroscience data. DNNs like Long short-term memory (LSTM) or statistical modeling approaches like state-space point-process (SSPP) are widely used in the analysis of Neural data including Neural coding and inference analysis. Despite wide utilization of these techniques, there is a lack of comprehensive studies which systematically assess attributes of LSTM and SSPP approaches on a common neuroscience data analysis problem. As a result, this occasionally leads to inconsistent and divergent conclusions on the strength or weakness of either of the methodologies and also statistical significance of the analytical outcomes. In this research, we focus on providing a more systematic and multifaceted assessment of LSTM and SSPP techniques in a Neural Decoding problem.We examine different settings and modeling specifications to attain the optimal modeling solutions. We propose new LSTM network topologies and approximate filter solution to estimate a rat movement trajectory in a 2-D spaces using an ensemble of place cells' spiking activity. For each technique; we then study performance, computational efficiency, and generalizability of each technique in this Decoding problem. By utilizing these results, we provided a succinct picture of the strength and weakness of each modeling approach and suggest who each of these techniques can be properly utilized in Neural Decoding problems.
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closed loop enhancement and Neural Decoding of human cognitive control
bioRxiv, 2020Co-Authors: Ishita Basu, Ali Yousefi, Britni Crocker, Rina Zelmann, Angelique C Paulk, Noam Peled, Kristen K Ellard, Daniel S Weisholtz, Rees G CosgroveAbstract:Cognitive control is the ability to withhold a default, prepotent response in favor of a more adaptive choice. Control deficits are common across mental disorders, including depression, anxiety, and addiction. Thus, a method for improving cognitive control could be broadly useful in disorders with few effective treatments. Here, we demonstrate closed-loop enhancement of cognitive control by direct brain stimulation in humans. We stimulated internal capsule/striatum in participants undergoing intracranial epilepsy monitoring as they performed a cognitive control task. Stimulation enhanced performance, with the strongest effects from dorsal capsule/striatum stimulation. We then developed a framework to detect control lapses and stimulate in response. This closed-loop approach was more effective than open-loop stimulation. Participants who self-reported difficulties with self-control reported that stimulation made them more able to shift attention away from internal distress. Finally, we decoded cognitive control directly from activity on a small number of electrodes, using features compatible with existing closed-loop brain implants. Our findings suggest a new approach to treating severe mental disorders, by directly remediating underlying cognitive deficits.
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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.
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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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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a point process framework for relating Neural spiking activity to spiking history Neural ensemble and extrinsic covariate effects
Journal of Neurophysiology, 2005Co-Authors: Wilson Truccolo, Uri T Eden, Matthew R Fellows, John P Donoghue, Emery N BrownAbstract:Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and Neural Decoding. The framework thus allows for the formulation and analysis of point process models of Neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.
Fabian Kloosterman - One of the best experts on this subject based on the ideXlab platform.
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Falcon: a highly flexible open-source software for closed-loop neuroscience.
Journal of Neural Engineering, 2017Co-Authors: Davide Ciliberti, Fabian KloostermanAbstract:Objective. Closed-loop experiments provide unique insights into brain dynamics and function. To facilitate a wide range of closed-loop experiments, we created an open-source software platform that enables high-performance real-time processing of streaming experimental data. Approach. We wrote Falcon, a C++ multi-threaded software in which the user can load and execute an arbitrary processing graph. Each node of a Falcon graph is mapped to a single thread and nodes communicate with each other through thread-safe buffers. The framework allows for easy implementation of new processing nodes and data types. Falcon was tested both on a 32-core and a 4-core workstation. Streaming data was read from either a commercial acquisition system (Neuralynx) or the open-source Open Ephys hardware, while closed-loop TTL pulses were generated with a USB module for digital output. We characterized the round-trip latency of our Falcon-based closed-loop system, as well as the specific latency contribution of the software architecture, by testing processing graphs with up to 32 parallel pipelines and eight serial stages. We finally deployed Falcon in a task of real-time detection of population bursts recorded live from the hippocampus of a freely moving rat. Main results. On Neuralynx hardware, round-trip latency was well below 1 ms and stable for at least 1 h, while on Open Ephys hardware latencies were below 15 ms. The latency contribution of the software was below 0.5 ms. Round-trip and software latencies were similar on both 32- and 4-core workstations. Falcon was used successfully to detect population bursts online with ~40 ms average latency. Significance. Falcon is a novel open-source software for closed-loop neuroscience. It has sub-millisecond intrinsic latency and gives the experimenter direct control of CPU resources. We envisage Falcon to be a useful tool to the neuroscientific community for implementing a wide variety of closed-loop experiments, including those requiring use of complex data structures and real-time execution of computationally intensive algorithms, such as population Neural Decoding/encoding from large cell assemblies.
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kernel density compression for real time bayesian encoding Decoding of unsorted hippocampal spikes
Knowledge Based Systems, 2016Co-Authors: Danaipat Sodkomkham, Davide Ciliberti, Fabian Kloosterman, Matthew A Wilson, Kenichi Fukui, Koichi Moriyama, Masayuki NumaoAbstract:Abstract To gain a better understanding of how Neural ensembles communicate and process information, Neural Decoding algorithms are used to extract information encoded in their spiking activity. Bayesian Decoding is one of the most used Neural population Decoding approaches to extract information from the ensemble spiking activity of rat hippocampal neurons. Recently it has been shown how Bayesian Decoding can be implemented without the intermediate step of sorting spike waveforms into groups of single units. Here we extend the approach in order to make it suitable for online encoding/Decoding scenarios that require real-time Decoding such as brain-machine interfaces. We propose an online algorithm for the Bayesian Decoding that reduces the time required for Decoding Neural populations, resulting in a real-time capable Decoding framework. More specifically, we improve the speed of the probability density estimation step, which is the most essential and the most expensive computation of the spike-sorting-less Decoding process, by developing a kernel density compression algorithm. In contrary to existing online kernel compression techniques, rather than optimizing for the minimum estimation error caused by kernels compression, the proposed method compresses kernels on the basis of the distance between the merging component and its most similar neighbor. Thus, without costly optimization, the proposed method has very low compression latency with a small and manageable estimation error. In addition, the proposed bandwidth matching method for Gaussian kernels merging has an interesting mathematical property whereby optimization in the estimation of the probability density function can be performed efficiently, resulting in a faster Decoding speed. We successfully applied the proposed kernel compression algorithm to the Bayesian Decoding framework to reconstruct positions of a freely moving rat from hippocampal unsorted spikes, with significant improvements in the Decoding speed and acceptable Decoding error.
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transductive Neural Decoding for unsorted neuronal spikes of rat hippocampus
PMC, 2012Co-Authors: Zhe Chen, Fabian Kloosterman, Stuart P Layton, Matthew A WilsonAbstract:Neural Decoding is an important approach for extracting information from population codes. We previously proposed a novel transductive Neural Decoding paradigm and applied it to reconstruct the rat's position during navigation based on unsorted rat hippocampal ensemble spiking activity. Here, we investigate several important technical issues of this new paradigm using one data set of one animal. Several extensions of our Decoding method are discussed.