Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models.

Neuronal ensembles are groups of neurons with coordinated activity that could represent sensory, motor, or cognitive states. The study of how neuronal ensembles are built, recalled, and involved in the guiding of complex behaviors has been limited by the lack of experimental and analytical tools to reliably identify and manipulate neurons that have the ability to activate entire ensembles. Such pattern completion neurons have also been proposed as key elements of artificial and biological neural networks.

Robust learning algorithms for capturing oceanic dynamics and transport of Noctiluca blooms using linear dynamical models.

The blooms of Noctiluca in the Gulf of Oman and the Arabian Sea have been intensifying in recent years, posing now a threat to regional fisheries and the long-term health of an ecosystem supporting a coastal population of nearly 120 million people. We present the results of a local-scale data analysis to investigate the onset and patterns of the Noctiluca blooms, which form annually during the winter monsoon in the Gulf of Oman and in the Arabian Sea. Our approach combines methods in physical and biological oceanography with machine learning techniques.

Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure.

Motivation: Drawing inferences from large, heterogeneous sets of biological data requires a theoretical framework that is capable of representing, e.g. DNA and protein sequences, protein structures, microarray expression data, various types of interaction networks, etc.

Clustered blockwise PCA for representing visual data.

Principal Component Analysis (PCA) is extensively used in computer vision and image processing. Since it provides the optimal linear subspace in a least-square sense, it has been used for dimensionality reduction and subspace analysis in various domains. However, its scalability is very limited because of its inherent computational complexity.

A novel drill set for the enhancement and assessment of robotic surgical performance.

Background: There currently exist several training modules to improve performance during video-assisted surgery. The unique characteristics of robotic surgery make these platforms an inadequate environment for the development and assessment of robotic surgical performance.

Methods: Expert surgeons (n=4) (>50 clinical robotic procedures and >2 years of clinical robotic experience) were compared to novice surgeons (n=17) (<5 clinical cases and limited laboratory experience) using the da Vinci Surgical System.

The LapSim: a learning environment for both experts and novices.

Background: Simulated environments present challenges to both clinical experts and novices in laparoscopic surgery. Experts and novices may have different expectations when confronted with a novel simulated environment. The LapSim is a computer-based virtual reality laparoscopic trainer.