Based on computational principles, the concept of an internal model for adaptive control has been divided into a forward and an inverse model. However, there is as yet little evidence that learning control by the eNS is through adaptation of one or the other. Here we examine two adaptive control architectures, one based only on the inverse model and other based on a combination of forward and inverse models. We then show that for reaching movements of the hand in novel force fields, only the learning of the forward model results in key characteristics of performance that match the kinematics of human subjects. In contrast, the adaptive control system that relies only on the inverse model fails to produce the kinematic patterns observed in the subjects, despite the fact that it is more stable.

W is often taken to be diagonal, but if we allow W to be a general positive definite matrix which can be tuned on the basis of training data, then an eigen-analysis of W shows that we are effectively creating hidden features, where the dimensionality of the hidden-feature space is determined by the data. We demonstrate the superiority of predictions usi ng the general matrix over those based on a diagonal matrix on two test problems.

In this paper we present a novel approach to multichannel blind that both mixingseparation/generalized deconvolution, assuming and demixing models are described by stable linear state-space systems. Based on the minimization of Kullback-Leibler Divergence, we develop a novel learning algorithm to train the matrices in the output equation. To estimate the state of the demixing model, we introduce a new concept, called to numerically implement the Kalman filter.hidden Referany priori knowledge of to review papers [lJ and [5J for the current state of theory and methods in the field. There are several reasons why as blind deconvolution models.

AR-NNs are a natural generalization of the classic linear autoregressive AR(p) process (2) See, e.g., Brockwell & Davis (1987) for a comprehensive introduction into AR and ARMA (autoregressive moving average) models. F. Leisch, A. Trapletti and K. Hornik 268 One of the most central questions in linear time series theory is the stationarity of the model, i.e., whether the probabilistic structure of the series is constant over time or at least asymptotically constant (when not started in equilibrium). Surprisingly, this question has not gained much interest in the NN literature, especially there are-up to our knowledge-no results giving conditions for the stationarity of AR NN models. There are results on the stationarity of Hopfield nets (Wang & Sheng, 1996), but these nets cannot be used to estimate conditional expectations for time series prediction. The rest of this paper is organized as follows: In Section 2 we recall some results from time series analysis and Markov chain theory defining the relationship between a time series and its associated Markov chain. In Section 3 we use these results to establish that standard AR-NN models without shortcut connections are stationary. We also give conditions for AR-NN models with shortcut connections to be stationary. Section 4 examines the NN modeling of an important class of non-stationary to the appendix.time

In High Energy Physics experiments one has to sort through a high flux of events, at a rate of tens of MHz, and select the few that are of interest. One of the key factors in making this decision is the location of the vertex where the interaction, that led to the event, took place. Here we present a novel solution to the problem of finding the location of the vertex, based on two feedforward neural networkswith fixed architectures, whose parameters are chosen so as to obtain a high accuracy. The system is tested on simulated datasets, and is shown to perform better than conventional algorithms. 1 Introduction An event in High Energy Physics (HEP) is the experimental result of an interaction during the collision of particles in an accelerator. The result of this interaction is the production of tens of particles, each of which is ejected in a different direction and energy. Due to the quantum mechanical effects involved, the events differ from one another in the number of particles produced, the types of particles, and their energies. The trajectories of produced particles are detected by a very large and sophisticated detector.

We present a method for learning complex appearance mappings.

We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for its maximum a posteriori (MAP) estimator. The MAP estimate minimizes uncertainty in all respects: cross-entropy between model and data; entropy of the model; entropy of the data's descriptive statistics. Iterative estimation extinguishes weakly supported parameters, compressing and sparsifying the model. Trimming operators accelerate this process by removing excess parameters and, unlike most pruning schemes, guarantee an increase in posterior probability. Entropic estimation takes a overcomplete random model and simplifies it, inducing the structure of relations between hidden and observed variables. Applied to hidden Markov models (HMMs), it finds a concise finite-state machine representing the hidden structure of a signal. We entropically model music, handwriting, and video time-series, and show that the resulting models are highly concise, structured, predictive, and interpretable: Surviving states tend to be highly correlated with meaningful partitions of the data, while surviving transitions provide a low-perplexity model of the signal dynamics.

The perfonnance of dedicated VLSI neural processing hardware depends critically on the design of the implemented algorithms. We have previously proposedan algorithm for acoustic transient classification [1]. Having implemented and demonstrated this algorithm in a mixed-mode architecture, we now investigate variants on the algorithm, using time and frequency channel differencing, input and output nonnalization, and schemes to binarize and train the template values, with the goal of achieving optimalclassification perfonnance for the chosen hardware.

In fact, for many of the kernels used (not the polynomial kernels) like Gaussian rbf-kernels it can be shown [6] that SV machines are universal approximators. While this is advantageous in general, parametric models are useful techniques in their own right. Especially if one happens to have additional knowledge about the problem, it would be unwise not to take advantage of it. For instance it might be the case that the major properties of the data are described by a combination of a small set of linear independent basis functions {¢Jt (.), ..., ¢n (.)}. Or one may want to correct the data for some (e.g.

Feature extraction is a typical example: The distance between a small pattern (i.e.