Computational Neuroscience: Theoretical Insights into Brain FunctionEdited by
- Paul Cisek
- Trevor Drew
- John Kalaska
Computational neuroscience is a relatively new but rapidly expanding area of research which is becoming increasingly influential in shaping the way scientists think about the brain. Computational approaches have been applied at all levels of analysis, from detailed models of single-channel function, transmembrane currents, single-cell electrical activity, and neural signaling to broad theories of sensory perception, memory, and cognition. This book provides a snapshot of this exciting new field by bringing together chapters on a diversity of topics from some of its most important contributors. This includes chapters on neural coding in single cells, in small networks, and across the entire cerebral cortex, visual processing from the retina to object recognition, neural processing of auditory, vestibular, and electromagnetic stimuli, pattern generation, voluntary movement and posture, motor learning, decision-making and cognition, and algorithms for pattern recognition. Each chapter provides a bridge between a body of data on neural function and a mathematical approach used to interpret and explain that data. These contributions demonstrate how computational approaches have become an essential tool which is integral in many aspects of brain science, from the interpretation of data to the design of new experiments, and to the growth of our understanding of neural function.
Neuroscientists, psychologists, mathematicians, and computer scientists
Progress in Brain Research
Hardbound, 570 Pages
Published: October 2007
- The neuronal transfer function: contributions from voltage and time-dependent mechanismsE.P. Cook, A.C. Wilhelm, J.A. Guest, Y. Liang, N.Y. Masse and C.M. Colbert (Montreal QC, Canada and Houston, TX, USA).A simple growth model constructs critical avalanche networksL.F. Abbott and R. Rohrkemper (New York, NY, USA and Zurich, Switzerland).The dynamics of visual responses in the primary visual cortexR. Shapley, M. Hawken and D. Xing (New York, NY, USA).A quantitative theory of immediate visual recognitionT. Serre, G. Kreiman, M. Kouh, C. Cadieu, U. Knoblich and T. Poggio (Boston, MA, USA).Attention in hierarchical models of object recognitionD.B. Walther and C. Koch (Urbana, IL, and Pasadena,CA, USA).Towards a unified theory of neocortex: laminar cortical circuits for vision and cognitionS. Grossberg (Boston, MA, USA).Real-time neural coding of memoryJ.Z. Tsien (Boston, MA, USA).Beyond timing in the auditory brainstem: intensity in the avian cochlear nucleus angularis K.M. MacLeod and C.E. Carr (College Park, MD, USA).Neural strategies for optimal processing of sensory signalsL. Maler (Ottawa, ON, Canada).Coordinate transformations and sensory integration in the detection of spatial orientation and self-motion: from models to experimentsA.M. Green and D.E. Angelaki (Montreal, QC, Canada and St. Louis, MO, USA).Sensorimotor optimization in higher dimensions. Tweed (Toronto, ON,USA).How tightly tuned are network parameters? Insight from computational and experimental studies in small rhythmic motor networksE. Marder, A.-E. Tobin and R. Grashow (Waltham, MA, USA).Spatial organization and state-dependent mechanisms for respiratory rhythm and pattern generationI.A. Rybak, A.P.L. Abdala, S.N. Markin, J.F.R. Paton and J.C. Smith (Philadelphia, PA and Bethesda, MD, USA and Bristol, UK). Modeling a vertebrate motor system â pattern generation, steering and control of body orientationS. Grillner, A. Kozlov, P. Dario, C. Stefanini, A. Menciassi, A. Lansner, J. Hellgren Kotaleski (Stockholm, Sweden and Pontedera, Italy).Modeling the mammalian locomotor CPG: insights from mistakes and perturbationsD.A. McCrea and I.A. Rybak (Winnipeg, MB, Canada and Philadelphia, PA, USA).The neuromechanical tuning hypothesisA. Prochazka and S. Yakovenko (Edmonton, AB and Montreal, QC, Canada).Threshold position control and the principle of minimal interaction in motor actionsA.G. Feldman, V. Goussev, A. Sangole and M.F. Levin (Montreal and Laval, QC, Canada).Modelling sensorimotor control of human upright stanceT. Mergner (Freiburg, Germany).Dimensional reduction in sensorimotor systems: a framework for understanding muscle coordination of postureL.H. Ting (Atlanta, GA, USA).Primitives, premotor drives and pattern generation: a combined computational and neuroethological perspectiveS. Giszter, V. Patil and C. Hart (Philadelphia, PA, USA).A multi-level approach to understanding upper limb functionI. Kurtzer and S.H. Scott (Kingston, ON, Canada).How is somatosensory information used to adapt to changes in the mechanical environment?T.E. Milner, M.R. Hinder and D.W. Franklin (Burnaby, BC, Queensland, Australia and Kyoto, Japan).Trial-by-trial motor adaptation: a window into elemental neural computationK.A. Thoroughman, M.S. Fine and J.A. Taylor (Saint Louis, MO, USA).Towards a computational neuropsychology of actionJ.W. Krakauer and R. Shadmehr (New York, NY and Baltimore, MD, USA).Motor control in a meta-network with attractor dynamicsN.I. Krouvhev and J.F. Kalaska (Montreal, WC, Canada).Computing movement geometry â a step in sensory-motor transformationsD. Zipser and E. Torres (Pasadena, CA, USA).Dynamics systems versus optimal control â a unifying viewS. Schaal, P. Mohajerian and A. Ijspeert (Los Angeles, CA, USA, Kyoto, Japan and Lausanne, Switzerland).The place of âcodesâ in nonlinear neurodynamicsW.J. Freeman (Berkeley, CA, USA).From a representation of behaviour to the concept of cognitive syntax: a theoretical frameworkT. Gisiger and M. Kerszberg (Paris, France).A parallel framework for interactive behaviourP. Cisek (Montreal, QC, Canada).Statistical models for neural encoding, decoding, and optimal stimulus designL. Paninski, J. Pillow and J. Lewi (New York, NY, USA and London, UK). Probabilistic population codes and the exponential family of distributionsJ. Beck, W. Ma, P.E. Latham and A. Pouget (Rochester, NY, USA and London, UK).On the challenge of learning complex functionsY. Bengio (Montreal, QC, Canada).To recognize shapes, first learn to generate imagesG.E. Hinton (Toronto, Canada).