In this paper we propose a new receiver for digital communications. We focus on the application of Gaussian Processes (GPs) to the multiuser detection (MUD) in code division multiple access (CDMA) systems to solve the near-far problem. Hence, we aim to reduce the interference from other users sharing the same frequency band. While usual approaches minimizethe mean square error (MMSE) to linearly retrieve the user of interest, we exploit the same criteria but in the design of a nonlinear MUD. Since the optimal solution is known to be nonlinear, the performance of this novel method clearly improves that of the MMSE detectors. Furthermore,the GP based MUD achieves excellent interference suppression even for short training sequences. We also include some experiments toillustrate that other nonlinear detectors such as those based on Support Vector Machines (SVMs) exhibit a worse performance.

While classical experiments on spike-timing dependent plasticity analyzed synapticchanges as a function of the timing of pairs of pre-and postsynaptic spikes, more recent experiments also point to the effect of spike triplets. Here we develop a mathematical framework that allows us to characterize timing based learning rules. Moreover, we identify a candidate learning rule with five variables (and 5 free parameters) that captures a variety of experimental data, including the dependence of potentiation anddepression upon pre-and postsynaptic firing frequencies. The relation to the Bienenstock-Cooper-Munro rule as well as to some timing-based rules is discussed.

The parameters of the model are learned using contrastive divergence and an optimization procedure for finding appropriate marginal directions.

While classical kernel-based learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lankriet et al. (2004) considered conic combinations of kernel matrices for classification, leadingto a convex quadratically constraint quadratic program. We show that it can be rewritten as a semi-infinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover,we generalize the formulation and our method to a larger class of problems, including regression and one-class classification. Experimentalresults show that the proposed algorithm helps for automatic model selection, improving the interpretability of the learning resultand works for hundred thousands of examples or hundreds of kernels to be combined.

Ridge regression can "fix" such problems numerically,

Separation of music signals is an interesting but difficult problem. It is helpful for many other music researches such as audio content analysis. In this paper, a new music signal separation method is proposed, which is based on harmonic structure modeling. The main idea of harmonic structure modelingis that the harmonic structure of a music signal is stable, so a music signal can be represented by a harmonic structure model. Accordingly, acorresponding separation algorithm is proposed. The main idea is to learn a harmonic structure model for each music signal in the mixture, and then separate signals by using these models to distinguish harmonic structures of different signals. Experimental results show that the algorithm can separate signals and obtain not only a very high Signalto-Noise Ratio(SNR) but also a rather good subjective audio quality.

Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active brain. However, interactivity between functional brain regions, is still little studied. In this paper, we contribute a novel framework for modeling the interactions between multiple active brain regions, using Dynamic Bayesian Networks (DBNs) as generative models forbrain activation patterns. This framework is applied to modeling of neuronal circuits associated with reward. The novelty of our framework froma Machine Learning perspective lies in the use of DBNs to reveal the brain connectivity and interactivity. Such interactivity models whichare derived from fMRI data are then validated through a group classification task.

In multi-agent systems, individuals within a group coordinate and interact to achieve a goal.

To investigate how top-down (TD) and bottom-up (BU) information is weighted in the guidance of human search behavior, we manipulated the proportions of BU and TD components in a saliency-based model. The model is biologically plausible and implements an artificial retina and a neuronal population code. The BU component is based on featurecontrast. TheTD component is defined by a feature-template match to a stored target representation. We compared the model's behavior at different mixturesof TD and BU components to the eye movement behavior of human observers performing the identical search task. We found that a purely TD model provides a much closer match to human behavior than any mixture model using BU information. Only when biological constraints areremoved (e.g., eliminating the retina) did a BU/TD mixture model begin to approximate human behavior.

Spiking activity from neurophysiological experiments often exhibits dynamics beyondthat driven by external stimulation, presumably reflecting the extensive recurrence of neural circuitry. Characterizing these dynamics may reveal important features of neural computation, particularly duringinternally-driven cognitive operations. For example, the activity of premotor cortex (PMd) neurons during an instructed delay periodseparating movement-target specification and a movementinitiation cueis believed to be involved in motor planning. We show that the dynamics underlying this activity can be captured by a lowdimensional non-lineardynamical systems model, with underlying recurrent structure and stochastic point-process output.