We explicitly treat two cases.

We examine the marriage of recent probabilistic generative models for social networks with classical frameworks from mathematical economics. Weare particularly interested in how the statistical structure of such networks influences global economic quantities such as price variation. Ourfindings are a mixture of formal analysis, simulation, and experiments on an international trade data set from the United Nations.

We present a biophysically constrained cerebellar model of classical conditioning, implemented using a neuromorphic analog VLSI (aVLSI) chip. Like its biological counterpart, our cerebellar model is able to control adaptive behavior by predicting the precise timing of events. Here we describe the functionality of the chip and present its learning performance, as evaluated in simulated conditioning experiments at the circuit level and in behavioral experiments using a mobile robot. We show that this aVLSI model supports the acquisition and extinction of adaptively timed conditioned responses under real-world conditions with ultra-low power consumption.

Section 2 below gives an overview of the musical background to chorale harmonisation. Section 3 explains how we can create a harmonisation system using Hidden Markov Models. Section 4 examines the system's performance quantitatively and provides example

De novo Sequencing of peptides is a challenging task in proteome research. Whilethere exist reliable DNAsequencing methods, the highthroughput denovo sequencing of proteins by mass spectrometry is still an open problem. Current approaches suffer from a lack in precision to detect mass peaks in the spectrograms. In this paper we present a novel method for de novo peptide sequencing based on a hidden Markov model. Experiments effectively demonstrate that this new method significantly outperformsstandard approaches in matching quality.

We consider the problem of recovering an underwater image distorted by surface waves. A large amount of video data of the distorted image is acquired. The problem is posed in terms of finding an undistorted image patch at each spatial location. This challenging reconstruction task can be formulated as a manifold learning problem, such that the center of the manifold is the image of the undistorted patch. To compute the center, we present a new technique to estimate global distances on the manifold. Our technique achieves robustness through convex flow computations and solves the "leakage" problem inherent in recent manifold embedding techniques.

For example, Yona et al.[10] uses profiles to align distant homologues from

Computation without stable states is a computing paradigm different fromTuring's and has been demonstrated for various types of simulated neural networks. This publication transfers this to a hardware implemented neural network. Results of a software implementation arereproduced showing that the performance peaks when the network exhibits dynamics at the edge of chaos. The liquid computing approach seems well suited for operating analog computing devices such as the used VLSI neural network.

The standard approach to the classification of objects is to consider the examples as independent and identically distributed (iid). In many real world settings, however, this assumption is not valid, because a topographical relationshipexists between the objects. In this contribution we consider the special case of image segmentation, where the objects are pixels and where the underlying topography is a 2D regular rectangular grid. We introduce a classification method which not only uses measured vectorial feature information but also the label configuration within a topographic neighborhood.Due to the resulting dependence between the labels of neighboring pixels, a collective classification of a set of pixels becomes necessary. We propose a new method called'Topographic Support VectorMachine' (TSVM), which is based on a topographic kernel and a self-consistent solution to the label assignment shown to be equivalent toa recurrent neural network. The performance of the algorithm is compared to a conventional SVM on a cell image segmentation task.

Repeated spike patterns have often been taken as evidence for the synfire chain, a phenomenon that a stable spike synchrony propagates through a feedforward network. Inter-spike intervals which represent a repeated spike pattern are influenced by the propagation speed of a spike packet. However, the relation between the propagation speed and network structure isnot well understood. While it is apparent that the propagation speed depends on the excitatory synapse strength, it might also be related to spike patterns. We analyze a feedforward network with Mexican-Hattype connectivity(FMH) using the Fokker-Planck equation. We show that both a uniform and a localized spike packet are stable in the FMH in a certain parameter region. We also demonstrate that the propagation speed depends on the distinct firing patterns in the same network.