Simbrain: A visual framework for neural network analysis and education

Simbrain is a visually-oriented framework for building and analyzing neural networks. It emphasizes the analysis of networks which control agents embedded in virtual environments, and visualization of the structures which occur in the high dimensional state spaces of these networks. The program was originally intended to facilitate analysis of representational processes in embodied agents, however it is also well suited to teaching neural networks concepts to a broader audience than is traditional for neural networks courses. Simbrain was used to teach a course at a new university, UC Merced, in its inaugural year. Experiences from the course and sample lessons are provided.

Articulated Narrowcasting for Privacy and Awareness in Multimedia Conferencing Systems and Design for Implementation Within a SIP Framework

This article proposes a new focus of research for multimedia conferencing systems which allows a participant to flexibly select another participant or a group for media transmission. For example, in a traditional conference system, participants voices might by default be shared with all others, but one might want to select a subset of the conference members to send his/her media to or receive media from. We review the concept of narrowcasting, a model for limiting such information streams in a multimedia conference, and describe a design to use existing standard protocols (SIP and SDP) for controlling fine-grained narrowcasting sessions.