The Neural Simulation Language: A System for Brain Modeling (A Bradford Book) - Tapa blanda

"This timely book will benefit researchers and practitioners as well as educators and students in the community of neural networks (both biologically realistic and artificial). The Neural Simulation Language offers great flexibility in building neural network models and strong object-oriented programming methodology."--DeLiang Wang, Professor, Center for Cognitive Science, The Ohio State University "Weitzenfeld, Arbib, and Alexander provide an excellent and durable resource--NSL--for the seasoned neural modeler. As the book amply illustrates, NSL offers great versatility for diverse applications in the study of the brain, from vision to learning to motor behavior and beyond." Ronald C. Arkin, Professor and Director of the Mobile Robot Laboratory, College of Computing, Georgia Institute of Technology "Weitzenfeld, Arbib, and Alexander provide an excellent and durable resource--NSL--for the seasoned neural modeler. As the book amply illustrates, NSL offers great versatility for diverse applications in the study of the brain, from vision to learning to motor behavior and beyond."--Ronald C. Arkin, Professor and Director of the Mobile Robot Laboratory, College of Computing, Georgia Institute of Technology

An overview of the Neural Simulation Language (NSL), including examples of a rich variety of brain models. 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.