The thirty original contributions in this book provide a working definition of "computational neuroscience" as the area in which problems lie simultaneously within computer science and neuroscience. They review this emerging field in historical and philosophical overviews and in stimulating summaries of recent results. Leading researchers address the structure of the brain and the computational problems associated with describing and understanding this structure at the synaptic, neural, map, and system levels.
The overview chapters discuss the early days of the field, provide a philosophical analysis of the problems associated with confusion between brain metaphor and brain theory, and take up the scope and structure of computational neuroscience.
Synaptic-level structure is addressed in chapters that relate the properties of dendritic branches, spines, and synapses to the biophysics of computation and provide a connection between real neuron architectures and neural network simulations.
The network-level chapters take up the preattentive perception of 3-D forms, oscillation in neural networks, the neurobiological significance of new learning models, and the analysis of neural assemblies and local learning rides.
Map-level structure is explored in chapters on the bat echolocation system, cat orientation maps, primate stereo vision cortical cognitive maps, dynamic remapping in primate visual cortex, and computer-aided reconstruction of topographic and columnar maps in primates.
The system-level chapters focus on the oculomotor system VLSI models of early vision, schemas for high-level vision, goal-directed movements, modular learning, effects of applied electric current fields on cortical neural activity neuropsychological studies of brain and mind, and an information-theoretic view of analog representation in striate cortex.
Eric L. Schwartz is Professor of Brain Research and Research Professor of Computer Science, Courant Institute of Mathematical Sciences, New York University Medical Center. Computational Neuroscience is included in the System Development Foundation Benchmark Series.
The overview chapters discuss the early days of the field, provide a philosophical analysis of the problems associated with confusion between brain metaphor and brain theory, and take up the scope and structure of computational neuroscience.
Synaptic-level structure is addressed in chapters that relate the properties of dendritic branches, spines, and synapses to the biophysics of computation and provide a connection between real neuron architectures and neural network simulations.
The network-level chapters take up the preattentive perception of 3-D forms, oscillation in neural networks, the neurobiological significance of new learning models, and the analysis of neural assemblies and local learning rides.
Map-level structure is explored in chapters on the bat echolocation system, cat orientation maps, primate stereo vision cortical cognitive maps, dynamic remapping in primate visual cortex, and computer-aided reconstruction of topographic and columnar maps in primates.
The system-level chapters focus on the oculomotor system VLSI models of early vision, schemas for high-level vision, goal-directed movements, modular learning, effects of applied electric current fields on cortical neural activity neuropsychological studies of brain and mind, and an information-theoretic view of analog representation in striate cortex.
Eric L. Schwartz is Professor of Brain Research and Research Professor of Computer Science, Courant Institute of Mathematical Sciences, New York University Medical Center. Computational Neuroscience is included in the System Development Foundation Benchmark Series.
