Main description:

The annual Computational Neuroscience Meeting (CNS) began in 1990 as a small workshop called Analysis and Modeling of Neural Systems. The goal of the workshop was to explore the boundary between neuroscience and computation. Riding on the success of several seminal papers, physicists had made "Neural Networks" fashionable, and soon the quantitative methods used in these abstract model networks started permeating the methods and ideas of experimental neuroscientists. Although experimental neurophysiological approaches provided many advances, it became increasingly evident that mathematical and computational techniques would be required to achieve a comprehensive and quantitative understanding of neural system function. "Computational Neuroscience" emerged to complement experimental neurophysiology.

The Encyclopedia of Computational Neuroscience, published in conjunction with the Organization for Computational Neuroscience, will be an extensive reference work consultable by both researchers and graduate level students. It will be a dynamic, living reference, updatable and containing linkouts and multimedia content whenever relevant.

Contents:

Information Theory.- Vestibular System.- Brute Force Methods in Computational Neuroscience.- Low Frequency Oscillations (Anesthesia and Sleep).- Invertebrate Pattern Generation.- Gamma and Theta Oscillations, Hippocampus.- Cable Theory.- Vertebrate Pattern Generation.- Neural Population Models and Cortical Field Theory.- Basal Ganglia.- Brain Imaging.- Modeling Software Tools.- Model Reproducibility.- Auditory Sensing Systems.- Neuromorphic Engineering.- Ion Channel Types and Modeling.- Compartmental Modeling.- Dynamical Systems.- Biochemical Signaling Pathways and Diffusion.- Modeling of Disease.- Molecular Level.- Peripheral Nerve Interfaces.- Brain Scale Networks.- Brainstem Processing.- Phase Response Curves.- Computational Neuroanatomy.- Multistability in Neurodynamics.- Decision Making.- Invertebrate Sensory Systems.- Synaptic Dynamics.- Deep Brain Stimulation (Models, Theory, Techniques).- Motor Neuron Models.- Somatosensory System.- Spike Train Analysis.- Spectral Methods in Neural Data Analysis.- Bayesian Approaches in Computational Neuroscience.- Cerebellum.- Databases in Computational Neuroscience.- Dynamics of Disease States.- LFP Analysis.- Brain Machine Interface.- Cortex.- Spinal Interfaces.- Olfaction.- Neuromodulation.- Spinal Cord.- Retinal/Visual Interfaces (Models, Theory, Techniques).- Learning Rules.- Visual System.- Spiking Network Models and Theory.- Neuromechanics.