Tracking Neural Modulation Depth by Dual Sequential Monte Carlo Estimation on Point Processes for Brain–Machine Interfaces

2016 ◽  
Vol 63 (8) ◽  
pp. 1728-1741 ◽  
Author(s):  
Yiwen Wang ◽  
Xiwei She ◽  
Yuxi Liao ◽  
Hongbao Li ◽  
Qiaosheng Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Point Processes ◽  
Sequential Monte Carlo ◽  
Modulation Depth ◽  
Brain Machine Interfaces ◽  
Neural Modulation ◽  
Monte Carlo Estimation

Sequential Monte Carlo Point-Process Estimation of Kinematics from Neural Spiking Activity for Brain-Machine Interfaces

2009 ◽  
Vol 21 (10) ◽  
pp. 2894-2930 ◽  
Author(s):  
Yiwen Wang ◽  
António R. C. Paiva ◽  
José C. Príncipe ◽  
Justin C. Sanchez
Keyword(s):  
Monte Carlo ◽  
Point Process ◽  
Adaptive Filtering ◽  
Point Processes ◽  
Sequential Monte Carlo ◽  
Hand Movement ◽  
Spike Trains ◽  
Training Data ◽  
Brain Machine Interfaces ◽  
Monte Carlo Estimation

Many decoding algorithms for brain machine interfaces' (BMIs) estimate hand movement from binned spike rates, which do not fully exploit the resolution contained in spike timing and may exclude rich neural dynamics from the modeling. More recently, an adaptive filtering method based on a Bayesian approach to reconstruct the neural state from the observed spike times has been proposed. However, it assumes and propagates a gaussian distributed state posterior density, which in general is too restrictive. We have also proposed a sequential Monte Carlo estimation methodology to reconstruct the kinematic states directly from the multichannel spike trains. This letter presents a systematic testing of this algorithm in a simulated neural spike train decoding experiment and then in BMI data. Compared to a point-process adaptive filtering algorithm with a linear observation model and a gaussian approximation (the counterpart for point processes of the Kalman filter), our sequential Monte Carlo estimation methodology exploits a detailed encoding model (tuning function) derived for each neuron from training data. However, this added complexity is translated into higher performance with real data. To deal with the intrinsic spike randomness in online modeling, several synthetic spike trains are generated from the intensity function estimated from the neurons and utilized as extra model inputs in an attempt to decrease the variance in the kinematic predictions. The performance of the sequential Monte Carlo estimation methodology augmented with this synthetic spike input provides improved reconstruction, which raises interesting questions and helps explain the overall modeling requirements better.

scholarly journals Neural Decoding Using a Parallel Sequential Monte Carlo Method on Point Processes with Ensemble Effect

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Xu ◽  
Yiwen Wang ◽  
Fang Wang ◽  
Yuxi Liao ◽  
Qiaosheng Zhang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Point Process ◽  
Goodness Of Fit ◽  
Point Processes ◽  
Sequential Monte Carlo ◽  
Neuronal Response ◽  
Position Estimation ◽  
Monte Carlo Algorithm ◽  
Sequential Monte Carlo Method ◽  
Proposed Model

Sequential Monte Carlo estimation on point processes has been successfully applied to predict the movement from neural activity. However, there exist some issues along with this method such as the simplified tuning model and the high computational complexity, which may degenerate the decoding performance of motor brain machine interfaces. In this paper, we adopt a general tuning model which takes recent ensemble activity into account. The goodness-of-fit analysis demonstrates that the proposed model can predict the neuronal response more accurately than the one only depending on kinematics. A new sequential Monte Carlo algorithm based on the proposed model is constructed. The algorithm can significantly reduce the root mean square error of decoding results, which decreases 23.6% in position estimation. In addition, we accelerate the decoding speed by implementing the proposed algorithm in a massive parallel manner on GPU. The results demonstrate that the spike trains can be decoded as point process in real time even with 8000 particles or 300 neurons, which is over 10 times faster than the serial implementation. The main contribution of our work is to enable the sequential Monte Carlo algorithm with point process observation to output the movement estimation much faster and more accurately.

Efficient sequential Monte Carlo estimation of range-dependent seabed properties

2016 ◽  
Vol 140 (4) ◽  
pp. 2949-2949
Author(s):  
Eric Mandolesi ◽  
Jan Dettmer ◽  
Stan E. Dosso ◽  
Charles W. Holland ◽  
Sheri Martinelli
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Monte Carlo Estimation

scholarly journals Earthquake forecasting based on data assimilation: sequential Monte Carlo methods for renewal point processes

2011 ◽  
Vol 18 (1) ◽  
pp. 49-70 ◽  
Author(s):  
M. J. Werner ◽  
K. Ide ◽  
D. Sornette
Keyword(s):  
Monte Carlo ◽  
Data Assimilation ◽  
Monte Carlo Methods ◽  
Measurement Errors ◽  
Process Model ◽  
Point Processes ◽  
Sequential Monte Carlo ◽  
Earthquake Forecasting ◽  
Seismic Gap ◽  
Sequential Monte Carlo Methods

Abstract. Data assimilation is routinely employed in meteorology, engineering and computer sciences to optimally combine noisy observations with prior model information for obtaining better estimates of a state, and thus better forecasts, than achieved by ignoring data uncertainties. Earthquake forecasting, too, suffers from measurement errors and partial model information and may thus gain significantly from data assimilation. We present perhaps the first fully implementable data assimilation method for earthquake forecasts generated by a point-process model of seismicity. We test the method on a synthetic and pedagogical example of a renewal process observed in noise, which is relevant for the seismic gap hypothesis, models of characteristic earthquakes and recurrence statistics of large quakes inferred from paleoseismic data records. To address the non-Gaussian statistics of earthquakes, we use sequential Monte Carlo methods, a set of flexible simulation-based methods for recursively estimating arbitrary posterior distributions. We perform extensive numerical simulations to demonstrate the feasibility and benefits of forecasting earthquakes based on data assimilation.

scholarly journals Pilot-Aided Sequential Monte Carlo Estimation of Phase Distortions and Transmitted Symbols in Multicarrier Systems

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
François Septier ◽  
Yves Delignon ◽  
Atika Menhaj-Rivenq ◽  
Christelle Garnier
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Estimation Algorithm ◽  
Time Correlation ◽  
Joint Estimation ◽  
Sequential Monte Carlo Methods ◽  
Multicarrier Systems ◽  
Ofdm Signal ◽  
Monte Carlo Estimation ◽  
Phase Distortions

We address the challenging problem of the joint estimation of transmitted symbols and phase distortions in standardized multicarrier systems, including pilot or virtual subcarriers. These subcarriers create time correlation on the useful transmitted OFDM signal that we propose to take into account by an autoregressive model. Because the phase distortions are nonlinear, we set the joint estimation algorithm on the framework of the Sequential Monte Carlo methods. Simulation results are provided in terms of bit error rate (BER) and mean square error (MSE); they highlight the efficiency and the robustness of the estimator.

scholarly journals Integration by Parts for Point Processes and Monte Carlo Estimation

2007 ◽  
Vol 44 (3) ◽  
pp. 806-823 ◽  
Author(s):  
Nicolas Privault ◽  
Xiao Wei
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Lebesgue Measure ◽  
Density Estimation ◽  
Stochastic Models ◽  
Point Processes ◽  
Kernel Estimator ◽  
Integration By Parts ◽  
Bandwidth Parameter ◽  
Monte Carlo Estimation

We develop an integration by parts technique for point processes, with application to the computation of sensitivities via Monte Carlo simulations in stochastic models with jumps. The method is applied to density estimation with respect to the Lebesgue measure via a modified kernel estimator which is less sensitive to variations of the bandwidth parameter than standard kernel estimators. This applies to random variables whose densities are not analytically known and requires the knowledge of the point process jump times.

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