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