... insight into the neuralmechanisms involved in excitatory interlimb coupling and could help design future experiments to better understand the neuralmechanisms of excitatory neural coupling Acknowledgements ... Cite this article as: Huang and Ferris: Computer simulations of neuralmechanisms explaining upper and lower limb excitatory neural coupling Journal of NeuroEngineering and Rehabilitation 2010 7:59 ... simulations of neuralmechanisms explaining upper and lower limb excitatory neural coupling Helen J Huang1*, Daniel P Ferris1,2,3 Abstract Background: When humans perform rhythmic upper and lower...
... the membrane constant, gex and gin denote time-varying and input dependent membrane conductances (separate for excitatory and inhibitory synapses, resp.), and Eex and Ein denote saturation points ... linear property and saturates for increased steady input 2.2 Cascade Architecture and Description of Generic Cortical Processing Stages Our modelling of neuralmechanisms (functionality) and their ... frame, processing two random dot kinematograms (the sequence shows 60 moving dots and consists of 60 frames with 40 × 40 px/frame) Random dots are initialized at random positions and a horizontal velocity...
... range of alternative definitions of employee turnover can prove problematic for those with responsibility in this area Appreciating the subtle differences between similar sounding definitions helps ... at the beginning of a period, and remain with the company at the end of the period This figure can be useful but it hides the departures of employees that joined and subsequently left during the ... improvement over the total turnover definition; retirees and employees dismissed or made redundant no longer included This definition is more precise and more relevant to internal decision-making If...
... sequences using DWT andneural network DWT decomposes one original image into four sub-bands The transformed image includes one average component sub-band and three detail component sub-bands Each detail ... sub-bands in Figure In next subsection, a neural network is employed to learn the features of candidate text regions obtained from those detail component sub-bands Finally, the well trained neural ... operation and the final resulted 2-D Haar DWT is shown in Figure 3(c) 2-D Haar DWT decomposes a gray-level image into one average component sub-band and three detail component sub-bands From...
... Wiley, 1986 [3] M.S Grewal and A.P Andrews, Kalman Filtering: Theory and Practice Englewood Cliffs, NJ: Prentice-Hall, 1993 [4] H.L Van Trees, Detection, Estimation, and Modulation Theory, Part ... where yk is the observable at time k and Hk is the measurement matrix The measurement noise vk is assumed to be additive, white, and Gaussian, with zero mean and with covariance matrix defined by ... scalar random variables; generalization of the theory to vector random variables is a straightforward matter Suppose we are given the observable yk ¼ xk þ vk ; where xk is an unknown signal and vk...
... speed, mapping accuracy, generalization, and overall performance relative to standard backpropagation and related methods Amongst the most promising and enduring of enhanced training methods ... is also maintained and evolved The global EKF (GEKF) training algorithm was introduced by Singhal and Wu [2] in the late 1980s, and has served as the basis for the development and enhancement of ... computationally effective neural network training methods that has enabled the application of feedforward and recurrent neural networks to problems in control, signal processing, and pattern recognition...
... circle moving right and up; square moving right and down; triangle moving right and up; circle moving right and down; square moving right and up; triangle moving right and down Training was ... Cortex, 1, 1–47 (1991) [2] J.S Lund, Q Wu and J.B Levitt, ‘‘Visual cortex cell types and connections’’, in M.A Arbib, Ed., Handbook of Brain Theory andNeural Networks, Cambridge, MA: MIT Press, ... Rao and Ballard [10] have proposed an alternative neural network implementation of the EKF that employs topdown feedback between layers, and have applied their model to both static images and...
... in D.A Rand and L.S Young, Eds Dynamical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics Vol 898 1981, p 230 Berlin: Springer-Verlag [6] A.M Fraser, ‘‘Information and entropy ... x2 ðk þ 1Þ ¼ 1:0 þ mfx1 ðkÞ þ x2 ðkÞ cos½mðkÞg; ð4:6Þ where x1 and x2 are the real and imaginary components, respectively, of x and the parameter m is carefully chosen to be 0.7 so that the produced ... Note that A ¼ initialization and B ¼ one-step phase evaluation, the correlation dimension, Lyapunov exponents and Kolmogorov entropy of both the actual Ikeda series and the autonomously generated...
... @x xk ^ D ð5:9Þ and where Rv and Rn are the covariances of vk and nk , respectively 5.2.2 EKF–Weight Estimation As proposed initially in [30], and further developed in [31] and [32], the EKF ... ð5:63Þ ^ where xkjN and pkjN are defined as the conditional mean and variance of xk ^ ^ kjN given w and all the data, fyk gN The terms xÀ and pÀ are the conditional kjN mean and variance of xÀ ... Atlas, ‘‘Recurrent neural networks and robust time series prediction,’’ IEEE Transactions on Neural Networks, 5(2), 240–254 (1994) [15] S.C Stubberud and M Owen, ‘‘Artificial neural network feedback...
... of f and g and the noise covariances Given observations of the (no longer hidden) states and outputs, f and g can be obtained as the solution to a possibly nonlinear regression problem, and the ... matrices A and B multiplying inputs x and u, respectively; and an output bias vector b, and the noise covariance Q Each RBF is assumed to be a Gaussian in x space, with center ci and width given ... admit exact and efficient inference (Here, and in what follows, we call a system linear if both the state evolution function and the state-to-output observation function are linear, and nonlinear...
... learning the parameters The use of the EKF for training neural networks has been developed by Singhal and Wu [8] and Puskorious and Feldkamp [9], and is covered in Chapter of this book The use of the ... ¼ Ck ¼ @x @n D ^ xk ð7:29Þ n and where Rv and Rn are the covariances of vk and nk , respectively The noise means are denoted by n ¼ E½n and v ¼ E½v, and are usually assumed to equal zero ... filtering (CDF) techniques developed separately by Ito and Xiong [12] and Nørgaard, Poulsen, and Ravn [13] In [7] van der Merwe and Wan show how the UKF and CDF can be unified in a general family of derivativefree...
... in D.A Rand and L.S Young, Eds Dynamical Systems and Turbulence, Warwick 1980, Lecture Notes in Mathematics Vol 898 1981, p 230 Berlin: Springer-Verlag [6] A.M Fraser, ‘‘Information and entropy ... x2 ðk þ 1Þ ¼ 1:0 þ mfx1 ðkÞ þ x2 ðkÞ cos½mðkÞg; ð4:6Þ where x1 and x2 are the real and imaginary components, respectively, of x and the parameter m is carefully chosen to be 0.7 so that the produced ... Note that A ¼ initialization and B ¼ one-step phase evaluation, the correlation dimension, Lyapunov exponents and Kolmogorov entropy of both the actual Ikeda series and the autonomously generated...
... @x xk ^ D ð5:9Þ and where Rv and Rn are the covariances of vk and nk , respectively 5.2.2 EKF–Weight Estimation As proposed initially in [30], and further developed in [31] and [32], the EKF ... ð5:63Þ ^ where xkjN and pkjN are defined as the conditional mean and variance of xk ^ ^ kjN given w and all the data, fyk gN The terms xÀ and pÀ are the conditional kjN mean and variance of xÀ ... Atlas, ‘‘Recurrent neural networks and robust time series prediction,’’ IEEE Transactions on Neural Networks, 5(2), 240–254 (1994) [15] S.C Stubberud and M Owen, ‘‘Artificial neural network feedback...
... of f and g and the noise covariances Given observations of the (no longer hidden) states and outputs, f and g can be obtained as the solution to a possibly nonlinear regression problem, and the ... matrices A and B multiplying inputs x and u, respectively; and an output bias vector b, and the noise covariance Q Each RBF is assumed to be a Gaussian in x space, with center ci and width given ... admit exact and efficient inference (Here, and in what follows, we call a system linear if both the state evolution function and the state-to-output observation function are linear, and nonlinear...
... learning the parameters The use of the EKF for training neural networks has been developed by Singhal and Wu [8] and Puskorious and Feldkamp [9], and is covered in Chapter of this book The use of the ... ¼ Ck ¼ @x @n D ^ xk ð7:29Þ n and where Rv and Rn are the covariances of vk and nk , respectively The noise means are denoted by n ¼ E½n and v ¼ E½v, and are usually assumed to equal zero ... filtering (CDF) techniques developed separately by Ito and Xiong [12] and Nørgaard, Poulsen, and Ravn [13] In [7] van der Merwe and Wan show how the UKF and CDF can be unified in a general family of derivativefree...
... Cherkassky and Mulier = LEARNING FROM DATA: Concepts, Theory, and Methods Diamantaras and Kung = PRINCIPAL COMPONENT NEURAL NETWORKS: Theory and Applications Haykin = KALMAN FILTERING ANDNEURAL ... Sanchez-Pena and Sznaler = ROBUST SYSTEMS THEORY AND ´ ˜ APPLICATIONS Sandberg, Lo, Fancourt, Principe, Katagiri, and Haykin = NONLINEAR DYNAMICAL SYSTEMS: Feedforward Neural Network Perspectives ´ Tao and ... Kristic, Kanellakopoulos, and Kokotovic = NONLINEAR AND ADAPTIVE CONTROL DESIGN Nikias and Shao = SIGNAL PROCESSING WITH ALPHA-STABLE DISTRIBUTIONS AND APPLICATIONS Passino and Burgess = STABILITY...