Ockham's razor modeling of the matrisome channels of the basal ganglia thalamocortical loops.

A functional model of the basal ganglia-thalamocortical (BTC) loops is described. In our modeling effort, we try to minimize the complexity of our starting hypotheses. For that reason, we call this type of modeling Ockham's razor modeling.

Parallel and robust skeletonization built on self-organizing elements.

A massively parallel neural architecture is suggested for the approximate computation of the skeleton of a planar shape. Numerical examples demonstrate the robustness of the method. The architecture is constructed from self-organizing elements that allow the extension of the concept of skeletonization to areas remote to image processing.

Prediction of protein functional domains from sequences using artificial neural networks.

An artificial neural network (ANN) solution is described for the recognition of domains in protein sequences. A query sequence is first compared to a reference database of domain sequences by use of and the output data, encoded in the form of six parameters, are forwarded to feed-forward artificial neural networks with six input and six hidden units with sigmoidal transfer function. The recognition is based on the distribution of scores precomputed for the known domain groups in a database versus database comparison.

A unified analysis of value-function-based reinforcement- learning algorithms.

Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms.

An automatic method for the identification and interpretation of clustered microcalcifications in mammograms.

We investigated a method for a fully automatic identification and interpretation process for clustered microcalcifications in mammograms. Mammographic films of 100 patients containing microcalcifications with known histology were digitized and preprocessed using standard techniques. Microcalcifications detected by an artificial neural network (ANN) were clustered and some cluster features served as the input of another ANN trained to differentiate between typical and atypical clusters, while others were fed into an ANN trained on typical clusters to evaluate these lesions.

The SBASE protein domain library, release 6.0: a collection of annotated protein sequence segments.

The sixth release of the SBASE protein domain library sequences contains 130 703 annotated and crossreferenced entries corresponding to structural, functional, ligand-binding and topogenic segments of proteins. The entries were grouped based on standard names (2312 groups) and futher classified on the basis of the BLAST similarity (2463 clusters). Automated searching with BLAST and a new sequence-plot representation of local domain similarities are available at the WWW-server http://www.

Self-organizing multi-resolution grid for motion planning and control.

A fully self-organizing neural network approach to low-dimensional control problems is described. We consider the problem of learning to control an object and solving the path planning problem at the same time. Control is based on the path planning model that follows the gradient of the stationary solution of a diffusion process working in the state space.