Large-scale extraction of brain connectivity from the neuroscientific literature.

Motivation: In neuroscience, as in many other scientific domains, the primary form of knowledge dissemination is through published articles. One challenge for modern neuroinformatics is finding methods to make the knowledge from the tremendous backlog of publications accessible for search, analysis and the integration of such data into computational models. A key example of this is metascale brain connectivity, where results are not reported in a normalized repository.

Offline grammar-based recognition of handwritten sentences.

This paper proposes a sequential coupling of a Hidden Markov Model (HMM) recognizer for offline handwritten English sentences with a probabilistic bottom-up chart parser using Stochastic Context-Free Grammars (SCFG) extracted from a text corpus. Based on extensive experiments, we conclude that syntax analysis helps to improve recognition rates significantly.

Finding instabilities in the community structure of complex networks.

The problem of finding clusters in complex networks has been studied by mathematicians, computer scientists, and, more recently, by physicists. Many of the existing algorithms partition a network into clear clusters without overlap. Here we introduce a method to identify the nodes lying "between clusters," allowing for a general measure of the stability of the clusters.

RST: a connectionist architecture to deal with spatiotemporal relationships.

In the past decade, connectionism has proved its efficiency in the field of static pattern recognition. The next challenge is to deal with spatiotemporal problems. This article presents a new connectionist architecture, RST (réseau spatio temporel [spatio temporal network]), with such spatiotemporal capacities.