The Neural Simulation Language
The Neural Simulation Language (NSL), developed by Alfredo Weitzenfeld, Michael Arbib, and Amanda Alexander, provides a simulation environment for modular brain modeling. NSL is an object-oriented language offering object-oriented protocols applicable to all levels of neural simulation. One of NSL's main strengths is that it allows for realistic modeling of the anatomy of macroscopic brain structures.
The book is divided into two parts. The first part presents an overview of neural network and schema modeling, a brief history of NSL, and a detailed discussion of the new version, NSL 3.0. It includes tutorials on several basic schema and neural network models. The second part presents models built in NSL by researchers from around the world, including those for conditional learning, face recognition, associative search networks, and visuomotor coordination. Each chapter provides an explanation of a model, an overview of the NSL 3.0 code, and a representative set of simulation results.
About the Authors
Alfredo Weitzenfeld is Professor of Computer Science and Director of the CANNES Laboratory at the Instituto Tecnológico Autónomo de México.
Michael Arbib has played a leading role at the interface of neuroscience and computer science ever since his first book, Brains, Machines, and Mathematics. From Neuron to Cognition provides a worthy pedagogical sequel to his widely acclaimed Handbook of Brain Theory and Neural Networks. After thirty years at University of Southern California he is now pursuing interests in “how the brain got language” and “neuroscience for architecture” in San Diego.
Amanda Alexander is a Systems Engineer at the University of Southern California.
—DeLiang Wang, Department of Computer and Information Science and Center for Cognitive Science, The Ohio State University
—Ronald C. Arkin, Professor and Director of the Mobile Robot Laboratory, College of Computing, Georgia Institute of Technology