scholarly journals Hyperacuity Bayesian methods to enhance temporal resolution of two-photon recording of the complex spikes in the cerebellar Purkinje cells

2017 ◽  
Author(s):  
Huu Hoang ◽  
Masa-aki Sato ◽  
Mitsuo Kawato ◽  
Keisuke Toyama
Keyword(s):  
Estimation Error ◽  
Sampling Rate ◽  
Ground Truth ◽  
Neuronal Dynamics ◽  
Two Photon ◽  
Cerebellar Purkinje Cells ◽  
Unsupervised Algorithms ◽  
Temporal Profiles ◽  
Severe Limit ◽  
Spike Signals

AbstractTwo-photon imaging is a major recording technique in neuroscience but its low sampling rate imposes a severe limit of elucidating high temporal profiles of neuronal dynamics. Here we developed two hyperacuity Bayesian algorithms to improve spike detection and spike time precision, minimizing the estimation error supervised by the ground-truth given as the electrical spike signals. The benchmark showed that our algorithms outperformed other unsupervised algorithms maximizing the likelihood of the estimates for both experimental and simulation data. We argue that the supervised algorithms are useful tools to improve spike estimation of two-photon recording in case ground truth signals are available.

scholarly journals Simulating a Ground Truth for Transit Time Analysis of Indicator Dilution Curves

2020 ◽  
Vol 6 (3) ◽  
pp. 268-271
Author(s):  
Michael Reiß ◽  
Ady Naber ◽  
Werner Nahm
Keyword(s):  
Transit Time ◽  
Sampling Rate ◽  
Ground Truth ◽  
Indicator Dilution ◽  
Cross Sectional ◽  
In Vivo Measurements ◽  
Multiple Indicator Dilution ◽  
Transit Times ◽  
Dilution Curves

AbstractTransit times of a bolus through an organ can provide valuable information for researchers, technicians and clinicians. Therefore, an indicator is injected and the temporal propagation is monitored at two distinct locations. The transit time extracted from two indicator dilution curves can be used to calculate for example blood flow and thus provide the surgeon with important diagnostic information. However, the performance of methods to determine the transit time Δt cannot be assessed quantitatively due to the lack of a sufficient and trustworthy ground truth derived from in vivo measurements. Therefore, we propose a method to obtain an in silico generated dataset of differently subsampled indicator dilution curves with a ground truth of the transit time. This method allows variations on shape, sampling rate and noise while being accurate and easily configurable. COMSOL Multiphysics is used to simulate a laminar flow through a pipe containing blood analogue. The indicator is modelled as a rectangular function of concentration in a segment of the pipe. Afterwards, a flow is applied and the rectangular function will be diluted. Shape varying dilution curves are obtained by discrete-time measurement of the average dye concentration over different cross-sectional areas of the pipe. One dataset is obtained by duplicating one curve followed by subsampling, delaying and applying noise. Multiple indicator dilution curves were simulated, which are qualitatively matching in vivo measurements. The curves temporal resolution, delay and noise level can be chosen according to the requirements of the field of research. Various datasets, each containing two corresponding dilution curves with an existing ground truth transit time, are now available. With additional knowledge or assumptions regarding the detection-specific transfer function, realistic signal characteristics can be simulated. The accuracy of methods for the assessment of Δt can now be quantitatively compared and their sensitivity to noise evaluated.

Precise hyperacuity estimation of spike timing from calcium imaging

2019 ◽  
Author(s):  
Huu Hoang ◽  
Masa-aki Sato ◽  
Shigeru Shinomoto ◽  
Shinichiro Tsutsumi ◽  
Miki Hashizume ◽  
...  
Keyword(s):  
Sampling Rate ◽  
Time Estimation ◽  
Spike Timing ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Spike Time ◽  
Experimental Conditions ◽  
Two Photon ◽  
Photon Imaging ◽  
Two Photon Imaging

SummaryTwo-photon imaging is a major recording technique in neuroscience, but it suffers from several limitations, including a low sampling rate, the nonlinearity of calcium responses, the slow dynamics of calcium dyes and a low signal-to-noise ratio, all of which impose a severe limitation on the application of two-photon imaging in elucidating neuronal dynamics with high temporal resolution. Here, we developed a hyperacuity algorithm (HA_time) based on an approach combining a generative model and machine learning to improve spike detection and the precision of spike time inference. First, Bayesian inference estimates the calcium spike model by assuming the constancy of the spike shape and size. A support vector machine employs this information and detects spikes with higher temporal precision than the sampling rate. Compared with conventional thresholding, HA_time improved the precision of spike time estimation up to 20-fold for simulated calcium data. Furthermore, the benchmark analysis of experimental data from different brain regions and simulation of a broader range of experimental conditions showed that our algorithm was among the best in a class of hyperacuity algorithms. We encourage experimenters to use the proposed algorithm to precisely estimate hyperacuity spike times from two-photon imaging.

scholarly journals Macroscopic Traffic State Estimation: Understanding Traffic Sensing Data-Based Estimation Errors

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Paul B. C. van Erp ◽  
Victor L. Knoop ◽  
Serge P. Hoogendoorn
Keyword(s):  
State Estimation ◽  
Traffic Management ◽  
Estimation Error ◽  
Ground Truth ◽  
Traffic Information ◽  
Estimation Errors ◽  
Sensing Data ◽  
Traffic Conditions ◽  
Traffic State ◽  
Traffic State Estimation

Traffic state estimation is a crucial element in traffic management systems and in providing traffic information to road users. In this article, we evaluate traffic sensing data-based estimation error characteristics in macroscopic traffic state estimation. We consider two types of sensing data, that is, loop-detector data and probe speed data. These data are used to estimate the mean speed in a discrete space-time mesh. We assume that there are no errors in the sensing data. This allows us to study the errors resulting from the differences in characteristics between the sensing data and desired estimate together with the incomplete description of the relation between the two. The aim of the study is to evaluate the dependency of this estimation error on the traffic conditions and sensing data characteristics. For this purpose, we use microscopic traffic simulation, where we compare the estimates with the ground truth using Edie’s definitions. The study exposes a relation between the error distribution characteristics and traffic conditions. Furthermore, we find that it is important to account for the correlation between individual probe data-based estimation errors. Knowledge related to these estimation errors contributes to making better use of the available sensing data in traffic state estimation.

scholarly journals Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies

2011 ◽  
Vol 5 (3) ◽  
pp. 617-629 ◽  
Author(s):  
J. I. López-Moreno ◽  
S. R. Fassnacht ◽  
S. Beguería ◽  
J. B. P. Latron
Keyword(s):  
Standard Deviation ◽  
Spatial Variability ◽  
Spatial Autocorrelation ◽  
Snow Depth ◽  
Spatial Configuration ◽  
Estimation Error ◽  
Sampling Strategy ◽  
Ground Truth ◽  
Depth Estimation ◽  
Depth Estimate

Abstract. Snow depth variability over small distances can affect the representativeness of depth samples taken at the local scale, which are often used to assess the spatial distribution of snow at regional and basin scales. To assess spatial variability at the plot scale, intensive snow depth sampling was conducted during January and April 2009 in 15 plots in the Rio Ésera Valley, central Spanish Pyrenees Mountains. Each plot (10 × 10 m; 100 m2) was subdivided into a grid of 1 m2 squares; sampling at the corners of each square yielded a set of 121 data points that provided an accurate measure of snow depth in the plot (considered as ground truth). The spatial variability of snow depth was then assessed using sampling locations randomly selected within each plot. The plots were highly variable, with coefficients of variation up to 0.25. This indicates that to improve the representativeness of snow depth sampling in a given plot the snow depth measurements should be increased in number and averaged when spatial heterogeneity is substantial. Snow depth distributions were simulated at the same plot scale under varying levels of standard deviation and spatial autocorrelation, to enable the effect of each factor on snowpack representativeness to be established. The results showed that the snow depth estimation error increased markedly as the standard deviation increased. The results indicated that in general at least five snow depth measurements should be taken in each plot to ensure that the estimation error is <10 %; this applied even under highly heterogeneous conditions. In terms of the spatial configuration of the measurements, the sampling strategy did not impact on the snow depth estimate under lack of spatial autocorrelation. However, with a high spatial autocorrelation a smaller error was obtained when the distance between measurements was greater.

scholarly journals CosMIC: A Consistent Metric for Spike Inference from Calcium Imaging

2017 ◽  
Author(s):  
Stephanie Reynolds ◽  
Therese Abrahamsson ◽  
P. Jesper Sjöström ◽  
Simon R. Schultz ◽  
Pier Luigi Dragotti
Keyword(s):  
Spike Train ◽  
Success Rate ◽  
Calcium Imaging ◽  
Ground Truth ◽  
Spike Trains ◽  
Imaging Data ◽  
Temporal Precision ◽  
Pulse Trains ◽  
Two Photon ◽  
Average Size

AbstractIn recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention. Meanwhile, few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient — an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximised when the correct number of spikes have been detected. Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate.

scholarly journals Discrete-Time Sliding Mode Control Coupled with Asynchronous Sensor Fusion for Rigid-Link Flexible-Joint Manipulators

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Guangyue Xue ◽  
Xuemei Ren ◽  
Kexin Xing ◽  
Qiang Chen
Keyword(s):  
Sensor Fusion ◽  
Discrete Time ◽  
Tracking Control ◽  
Sliding Mode ◽  
Estimation Error ◽  
Tracking Error ◽  
Sampling Rate ◽  
Experimental Studies ◽  
Flexible Joint ◽  
Rigid Link

This paper proposes a novel discrete-time terminal sliding mode controller (DTSMC) coupled with an asynchronous multirate sensor fusion estimator for rigid-link flexible-joint (RLFJ) manipulator tracking control. A camera is employed as external sensors to observe the RLFJ manipulator’s state which cannot be directly obtained from the encoders since gear mechanisms or flexible joints exist. The extended Kalman filter- (EKF-) based asynchronous multirate sensor fusion method deals with the slow sampling rate and the latency of camera by using motor encoders to cover the missing information between two visual samples. In the proposed control scheme, a novel sliding mode surface is presented by taking advantage of both the estimation error and tracking error. It is proved that the proposed controller achieves convergence results for tracking control in the theoretical derivation. Simulation and experimental studies are included to validate the effectiveness of the proposed approach.

scholarly journals A deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging data

2020 ◽  
Author(s):  
Peter Rupprecht ◽  
Stefano Carta ◽  
Adrian Hoffmann ◽  
Mayumi Echizen ◽  
Kazuo Kitamura ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Action Potentials ◽  
Sampling Rate ◽  
Ground Truth ◽  
Cell Types ◽  
Imaging Data ◽  
Calcium Signals ◽  
Ground Truth Data ◽  
Unseen Data ◽  
User Friendly

ABSTRACTCalcium imaging is a key method to record patterns of neuronal activity across populations of identified neurons. Inference of temporal patterns of action potentials (‘spikes’) from calcium signals is, however, challenging and often limited by the scarcity of ground truth data containing simultaneous measurements of action potentials and calcium signals. To overcome this problem, we compiled a large and diverse ground truth database from publicly available and newly performed recordings. This database covers various types of calcium indicators, cell types, and signal-to-noise ratios and comprises a total of >20 hours from 225 neurons. We then developed a novel algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level. As a consequence, no parameters need to be adjusted by the user. To facilitate routine application of CASCADE we developed systematic performance assessments for unseen data, we openly release all resources, and we provide a user-friendly cloud-based implementation.

scholarly journals Modalflow: Cross-Origin Flow Data Visualization for Urban Mobility

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 298
Author(s):  
Ignacio Pérez-Messina ◽  
Eduardo Graells-Garrido ◽  
María Jesús Lobo ◽  
Christophe Hurter
Keyword(s):  
Survey Data ◽  
Visual Analysis ◽  
Ground Truth ◽  
Secondary Source ◽  
Urban Mobility ◽  
Flow Maps ◽  
Partial View ◽  
Visualization Techniques ◽  
Unsupervised Algorithms ◽  
Source Of Information

Pervasive data have become a key source of information for mobility and transportation analyses. However, as a secondary source, it has a different methodological origin than travel survey data, usually relying on unsupervised algorithms, and so it requires to be assessed as a dataset. This assessment is challenging, because, in general, there is not a benchmark dataset or a ground truth scenario available, as travel surveys only represent a partial view of the phenomenon and suffer from their own biases. For this critical task, which involves urban planners and data scientists, we study the design space of the visualization of cross-origin, multivariate flow datasets. For this purpose, we introduce the Modalflow system, which incorporates and adapts different visualization techniques in a notebook-like setting, presenting novel visual encodings and interactions for flows with modal partition into scatterplots, flow maps, origin-destination matrices, and ternary plots. Using this system, we extract general insights on visual analysis of pervasive and survey data for urban mobility and assess a mobile phone network dataset for one metropolitan area.

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