scholarly journals Climbing fibers predict movement kinematics and performance errors

2017 ◽  
Vol 118 (3) ◽  
pp. 1888-1902 ◽  
Author(s):  
Martha L. Streng ◽  
Laurentiu S. Popa ◽  
Timothy J. Ebner
Keyword(s):  
Motor Control ◽  
Purkinje Cells ◽  
Linear Regression Analysis ◽  
Linear Representation ◽  
Complete Characterization ◽  
Position Error ◽  
Climbing Fibers ◽  
Data Set ◽  
Complex Spike ◽  
Position Errors

Requisite for understanding cerebellar function is a complete characterization of the signals provided by complex spike (CS) discharge of Purkinje cells, the output neurons of the cerebellar cortex. Numerous studies have provided insights into CS function, with the most predominant view being that they are evoked by error events. However, several reports suggest that CSs encode other aspects of movements and do not always respond to errors or unexpected perturbations. Here, we evaluated CS firing during a pseudo-random manual tracking task in the monkey ( Macaca mulatta). This task provides extensive coverage of the work space and relative independence of movement parameters, delivering a robust data set to assess the signals that activate climbing fibers. Using reverse correlation, we determined feedforward and feedback CSs firing probability maps with position, velocity, and acceleration, as well as position error, a measure of tracking performance. The direction and magnitude of the CS modulation were quantified using linear regression analysis. The major findings are that CSs significantly encode all three kinematic parameters and position error, with acceleration modulation particularly common. The modulation is not related to “events,” either for position error or kinematics. Instead, CSs are spatially tuned and provide a linear representation of each parameter evaluated. The CS modulation is largely predictive. Similar analyses show that the simple spike firing is modulated by the same parameters as the CSs. Therefore, CSs carry a broader array of signals than previously described and argue for climbing fiber input having a prominent role in online motor control. NEW & NOTEWORTHY This article demonstrates that complex spike (CS) discharge of cerebellar Purkinje cells encodes multiple parameters of movement, including motor errors and kinematics. The CS firing is not driven by error or kinematic events; instead it provides a linear representation of each parameter. In contrast with the view that CSs carry feedback signals, the CSs are predominantly predictive of upcoming position errors and kinematics. Therefore, climbing fibers carry multiple and predictive signals for online motor control.

scholarly journals Correlation Between Average Segment Width and Gamma Passing Rate as a Function of MLC Position Error in Volumetric Modulated Arc Therapy

2021 ◽  
Vol 20 ◽  
pp. 153303382110599
Author(s):  
Young Min Moon ◽  
Sang Il Bae ◽  
Moo Jae Han ◽  
Wan Jeon ◽  
Tosol Yu ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Linear Regression Analysis ◽  
Volumetric Modulated Arc Therapy ◽  
Position Error ◽  
Coefficient Of Determination ◽  
Position Errors ◽  
Visual Basic For Applications ◽  
Passing Rate ◽  
Arc Therapy ◽  
Gamma Passing Rate

Objective: This study analyzed the correlation between the average segment width (ASW) and gamma passing rate according to the multi-leaf collimator (MLC) position error. Method: To evaluate the changes in the gamma passing rate according to the MLC position error, 21 volumetric modulated arc therapy (VMAT) plans were generated using pelvic lymph node metastatic prostate cancer patient's data which is sensitive to MLC position errors as they involve several long, narrow, irregular fields. The ASW for each VMAT plan was calculated using our own code developed using Visual Basic for Applications (VBA). The gamma passing rate of the VMAT plan according to the MLC position error was evaluated using ArcCHECK (Sun Nuclear, Melbourne, FL, USA) while inducing symmetric MLC position errors in 0.25 mm intervals from −1 mm to +1 mm in the infinity medical linear accelerator (Elekta AB, Stockholm, Sweden). Finally, we examined the correlation between the change in the passing rate ([Formula: see text]) due to the MLC position error and the ASW in VMAT through linear regression analysis using the least squares method. Results: The ASW and [Formula: see text] were found to have a linear correlation according to the MLC position error, and the coefficient of determination was 0.88. For a 1 mm position error of MLC in VMAT, the gamma passing rate improved by approximately 11.9% as the ASW increased by 10 mm. Conclusion: These results are expected to be employed as guidelines to minimize the dose uncertainty due to MLC position error in VMAT.

scholarly journals Association Between Dendritic Lamellar Bodies and Complex Spike Synchrony in the Olivocerebellar System

1997 ◽  
Vol 77 (4) ◽  
pp. 1747-1758 ◽  
Author(s):  
C. I. De Zeeuw ◽  
S.K.E. Koekkoek ◽  
D.R.W. Wylie ◽  
J. I. Simpson
Keyword(s):  
Purkinje Cell ◽  
Gap Junctions ◽  
Purkinje Cells ◽  
Inferior Olive ◽  
Cerebellar Nuclei ◽  
Climbing Fibers ◽  
Lamellar Bodies ◽  
Spike Synchrony ◽  
Complex Spike ◽  
Simple Spike

De Zeeuw, C. I., S.K.E. Koekkoek, D.R.W. Wylie, and J. I. Simpson. Association between dendritic lamellar bodies and complex spike synchrony in the olivocerebellar system. J. Neurophysiol. 77: 1747–1758, 1997. Dendritic lamellar bodies have been reported to be associated with dendrodendritic gap junctions. In the present study we investigated this association at both the morphological and electrophysiological level in the olivocerebellar system. Because cerebellar GABAergic terminals are apposed to olivary dendrites coupled by gap junctions, and because lesions of cerebellar nuclei influence the coupling between neurons in the inferior olive, we postulated that if lamellar bodies and gap junctions are related, then the densities of both structures will change together when the cerebellar input is removed. Lesions of the cerebellar nuclei in rats and rabbits resulted in a reduction of the density of lamellar bodies, the number of lamellae per lamellar body, and the density of gap junctions in the inferior olive, whereas the number of olivary neurons was not significantly reduced. The association between lamellar bodies and electrotonic coupling was evaluated electrophysiologically in alert rabbits by comparing the occurrence of complex spike synchrony in different Purkinje cell zones of the flocculus that receive their climbing fibers from olivary subnuclei with different densities of lamellar bodies. The complex spike synchrony of Purkinje cell pairs, that receive their climbing fibers from an olivary subnucleus with a high density of lamellar bodies, was significantly higher than that of Purkinje cells, that receive their climbing fibers from a subnucleus with a low density of lamellar bodies. To investigate whether the complex spike synchrony is related to a possible synchrony between simple spikes, we recorded simultaneously the complex spike and simple spike responses of Purkinje cell pairs during natural visual stimulation. Synchronous simple spike responses did occur, and this synchrony tended to increase as the synchrony between the complex spikes increased. This relation raises the possibility that synchronously activated climbing fibers evoke their effects in part via the simple spike response of Purkinje cells. The present results indicate that dendritic lamellar bodies and dendrodendritic gap junctions can be downregulated concomitantly, and that the density of lamellar bodies in different olivary subdivisions is correlated with the degree of synchrony of their climbing fiber activity. Therefore these data support the hypothesis that dendritic lamellar bodies can be associated with dendrodendritic gap junctions. Considering that the density of dedritic lamellar bodies in the inferior olive is higher than in any other area of the brain, this conclusion implies that electrotonic coupling is important for the function of the olivocerebellar system.

scholarly journals Using deep neural networks to detect complex spikes of cerebellar Purkinje Cells

2019 ◽  
Author(s):  
Akshay Markanday ◽  
Joachim Bellet ◽  
Marie E. Bellet ◽  
Ziad M. Hafed ◽  
Peter Thier
Keyword(s):  
Motor Control ◽  
Deep Learning ◽  
Action Potential ◽  
Purkinje Cell ◽  
Purkinje Cells ◽  
Local Field ◽  
Visual Inspection ◽  
Complex Spike ◽  
Cerebellar Purkinje Cells ◽  
Complex Spikes

AbstractOne of the most powerful excitatory synapses in the entire brain is formed by cerebellar climbing fibers, originating from neurons in the inferior olive, that wrap around the proximal dendrites of cerebellar Purkinje cells. The activation of a single olivary neuron is capable of generating a large electrical event, called “complex spike”, at the level of the postsynaptic Purkinje cell, comprising of a fast initial spike of large amplitude followed by a slow polyphasic tail of small amplitude spikelets. Several ideas discussing the role of the cerebellum in motor control are centered on these complex spike events. However, these events are extremely rare, only occurring 1-2 times per second. As a result, drawing conclusions about their functional role has been very challenging, as even few errors in their detection may change the result. Since standard spike sorting approaches cannot fully handle the polyphasic shape of complex spike waveforms, the only safe way to avoid omissions and false detections has been to rely on visual inspection of long traces of Purkinje cell recordings by experts. Here we present a supervised deep learning algorithm for rapidly and reliably detecting complex spikes as an alternative to tedious visual inspection. Our algorithm, utilizing both action potential and local field potential signals, not only detects complex spike events much faster than human experts, but it also excavates key features of complex spike morphology with a performance comparable to that of such experts.Significance statementClimbing fiber driven “complex spikes”, fired at perplexingly low rates, are known to play a crucial role in cerebellum-based motor control. Careful interpretations of these spikes require researchers to manually detect them, since conventional online or offline spike sorting algorithms (optimized for analyzing the much more frequent “simple spikes”) cannot be fully trusted. Here, we present a deep learning approach for identifying complex spikes, which is trained on local field and action potential recordings from cerebellar Purkinje cells. Our algorithm successfully identifies complex spikes, along with additional relevant neurophysiological features, with an accuracy level matching that of human experts, yet with very little time expenditure.

scholarly journals Consistency of the Empirical Distributions of Navigation Positioning System Errors with Theoretical Distributions—Comparative Analysis of the DGPS and EGNOS Systems in the Years 2006 and 2014

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 31
Author(s):  
Mariusz Specht
Keyword(s):  
Normal Distribution ◽  
Root Mean Square ◽  
Statistical Tests ◽  
Position Error ◽  
Positioning System ◽  
Navigation Systems ◽  
Mean Square ◽  
Positioning Systems ◽  
Position Errors ◽  
System Errors

Positioning systems are used to determine position coordinates in navigation (air, land and marine). The accuracy of an object’s position is described by the position error and a statistical analysis can determine its measures, which usually include: Root Mean Square (RMS), twice the Distance Root Mean Square (2DRMS), Circular Error Probable (CEP) and Spherical Probable Error (SEP). It is commonly assumed in navigation that position errors are random and that their distribution are consistent with the normal distribution. This assumption is based on the popularity of the Gauss distribution in science, the simplicity of calculating RMS values for 68% and 95% probabilities, as well as the intuitive perception of randomness in the statistics which this distribution reflects. It should be noted, however, that the necessary conditions for a random variable to be normally distributed include the independence of measurements and identical conditions of their realisation, which is not the case in the iterative method of determining successive positions, the filtration of coordinates or the dependence of the position error on meteorological conditions. In the preface to this publication, examples are provided which indicate that position errors in some navigation systems may not be consistent with the normal distribution. The subsequent section describes basic statistical tests for assessing the fit between the empirical and theoretical distributions (Anderson-Darling, chi-square and Kolmogorov-Smirnov). Next, statistical tests of the position error distributions of very long Differential Global Positioning System (DGPS) and European Geostationary Navigation Overlay Service (EGNOS) campaigns from different years (2006 and 2014) were performed with the number of measurements per session being 900’000 fixes. In addition, the paper discusses selected statistical distributions that fit the empirical measurement results better than the normal distribution. Research has shown that normal distribution is not the optimal statistical distribution to describe position errors of navigation systems. The distributions that describe navigation positioning system errors more accurately include: beta, gamma, logistic and lognormal distributions.

scholarly journals Osmotically inactive sodium and potassium storage: lessons learned from the Edelman and Boling data

2016 ◽  
Vol 311 (3) ◽  
pp. F539-F547 ◽  
Author(s):  
Minhtri K. Nguyen ◽  
Dai-Scott Nguyen ◽  
Minh-Kevin Nguyen
Keyword(s):  
Linear Regression ◽  
Measurement Errors ◽  
Linear Regression Analysis ◽  
Sodium Concentration ◽  
Lessons Learned ◽  
Dynamic Changes ◽  
Data Set ◽  
Storage Pool ◽  
Total Body ◽  
Sodium And Potassium

Because changes in the plasma water sodium concentration ([Na+]pw) are clinically due to changes in the mass balance of Na+, K+, and H2O, the analysis and treatment of the dysnatremias are dependent on the validity of the Edelman equation in defining the quantitative interrelationship between the [Na+]pw and the total exchangeable sodium (Nae), total exchangeable potassium (Ke), and total body water (TBW) (Edelman IS, Leibman J, O'Meara MP, Birkenfeld LW. J Clin Invest 37: 1236–1256, 1958): [Na+]pw = 1.11(Nae + Ke)/TBW − 25.6. The interrelationship between [Na+]pw and Nae, Ke, and TBW in the Edelman equation is empirically determined by accounting for measurement errors in all of these variables. In contrast, linear regression analysis of the same data set using [Na+]pw as the dependent variable yields the following equation: [Na+]pw = 0.93(Nae + Ke)/TBW + 1.37. Moreover, based on the study by Boling et al. (Boling EA, Lipkind JB. 18: 943–949, 1963), the [Na+]pw is related to the Nae, Ke, and TBW by the following linear regression equation: [Na+]pw = 0.487(Nae + Ke)/TBW + 71.54. The disparities between the slope and y-intercept of these three equations are unknown. In this mathematical analysis, we demonstrate that the disparities between the slope and y-intercept in these three equations can be explained by how the osmotically inactive Na+ and K+ storage pool is quantitatively accounted for. Our analysis also indicates that the osmotically inactive Na+ and K+ storage pool is dynamically regulated and that changes in the [Na+]pw can be predicted based on changes in the Nae, Ke, and TBW despite dynamic changes in the osmotically inactive Na+ and K+ storage pool.

Evidence for a multiple innervation of purkinje cells by climbing fibers in the immature rat cerebellum

1976 ◽  
Vol 7 (6) ◽  
pp. 567-578 ◽  
Author(s):  
Francis Crepel ◽  
Jean Mariani ◽  
Nicole Delhaye-Bouchaud
Keyword(s):  
Purkinje Cells ◽  
Climbing Fibers ◽  
Immature Rat ◽  
Rat Cerebellum

Consistency analysis of global positioning system position errors with typical statistical distributions

2021 ◽  
pp. 1-18
Author(s):  
Mariusz Specht
Keyword(s):  
Normal Distribution ◽  
Global Positioning System ◽  
Position Error ◽  
Statistical Distribution ◽  
Positioning System ◽  
Statistical Distributions ◽  
Position Errors ◽  
Global Positioning ◽  
Gps System ◽  
Best Fit

Abstract Research into statistical distributions of φ, λ and two-dimensional (2D) position errors of the global positioning system (GPS) enables the evaluation of its accuracy. Based on this, the navigation applications in which the positioning system can be used are determined. However, studies of GPS accuracy indicate that the empirical φ and λ errors deviate from the typical normal distribution, significantly affecting the statistical distribution of 2D position errors. Therefore, determining the actual statistical distributions of position errors (1D and 2D) is decisive for the precision of calculating the actual accuracy of the GPS system. In this paper, based on two measurement sessions (900,000 and 237,000 fixes), the distributions of GPS position error statistics in both 1D and 2D space are analysed. Statistical distribution measures are determined using statistical tests, the hypothesis on the normal distribution of φ and λ errors is verified, and the consistency of GPS position errors with commonly used statistical distributions is assessed together with finding the best fit. Research has shown that φ and λ errors for the GPS system are normally distributed. It is proven that φ and λ errors are more concentrated around the central value than in a typical normal distribution (positive kurtosis) with a low value of asymmetry. Moreover, φ errors are clearly more concentrated than λ errors. This results in larger standard deviation values for φ errors than λ errors. The differences in both values were 25–39%. Regarding the 2D position error, it should be noted that the value of twice the distance root mean square (2DRMS) is about 10–14% greater than the value of R95. In addition, studies show that statistical distributions such as beta, gamma, lognormal and Weibull are the best fit for 2D position errors in the GPS system.

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