This document is intended to help newcomers to get into computational neuroscience. Note that this is a living document and it will regularly be updated. Offers of help to complete this are very welcome!
Cognitive Neuroscience, Michael S. Gazzaniga, Richard B. Ivry and George R. Mangun
Principles of Neural Science, Eric Kandel
Computational modelling/theoretical neuroscience
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, Peter Dayan and LF Abbott
Methods in Neuronal Modeling: from Ions to Networks, C. Koch and I. Segev (eds.)
Principles of Computational Modelling in Neuroscience, David Sterratt, Bruce Graham, Andrew Gillies and David Willshaw (eds.)
Neuronal Dynamics - from single neurons to networks and models of cognition, Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski (freely available online!)
Computational Neuroscience: Realistic Modeling for Experimentalists. E. De Schutter (ed.)
Introduction To The Theory Of Neural Computation, John A. Hertz, Anders S. Krogh and Richard G. Palmer
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, David Marr
The handbook of brain theory and neural networks, Michael A. Arbib (ed.)
Pattern Recognition and Machine Learning, Christopher M Bishop
Machine Learning, A Probabilistic perspective, Kevin P. Murphy
Atick, J.J., 1992. Could information theory provide an ecological theory of sensory processing?. Network: Computation in neural systems, 3(2), pp.213-251.
Oztop, E., Kawato, M. and Arbib, M., 2006. Mirror neurons and imitation: A computationally guided review. Neural Networks, 19(3), pp.254-271.
Bower, J.M., 2013. 20 years of computational neuroscience. New York: Springer.
Brette, R., Rudolph, M., Carnevale, T., Hines, M., Beeman, D., Bower, J.M., Diesmann, M., Morrison, A., Goodman, P.H., Harris Jr, F.C. and Zirpe, M., 2007. Simulation of networks of spiking neurons: a review of tools and strategies. Journal of computational neuroscience, 23(3), pp.349-398.
Hodgkin, A.L. and Huxley, A.F., 1952. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of physiology, 117(4), p.500.
McCulloch, W.S. and Pitts, W., 1943. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), pp.115-133.
Donald O.Hebb, The Organization of Behavior, New York: Wiley, Introduction and Chapter 4, “The first stage of perception: growth of the assembly,” pp. xi-xix, 60-78.
Lashley, K.S., 1950. In search of the engram.
Von Neumann, J. and Kurzweil, R., 2012. The computer and the brain. Yale University Press.
Rosenblatt, F., 1958. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), p.386
Marr, D. and Poggio, T., 1976. Cooperative computation of stereo disparity. Science, 194(4262), pp.283-287.
Grossberg, S., 1982. How does a brain build a cognitive code? In Studies of mind and brain (pp. 1-52). Springer Netherlands.
Ackley, D.H., Hinton, G.E. and Sejnowski, T.J., 1985. A learning algorithm for Boltzmann machines. Cognitive science, 9(1), pp.147-169.
Open Source Brain projects#
See here for a list of OSB projects which contain tutorials, exercises, etc. in computational neuroscience.
An overview of the main target simulators for models in Open Source Brain can be found here.
Libraries: Data analysis and scientific computing
Libraries: Data visualization
Libraries: Machine learning
ModelDB: model database for computational neuroscience (ModelDB)
Open Source Brain (OSB)
Digitally Reconstructed Neuron Database (NeuroMorpho)
Neuroscience Information Framework (NIF)
Brain Operation Database System (BODB)
BioModels Database (BioModels)
Institutions, Laboratories and Research Groups#
A more comprehensive list of labs, centers and researchers can be found here.
Mailing Lists, Blogs and News#
Computational Neuroscience (Comp-neuro)
Computational and Systems Neuroscience (Cosyne)
The Connectionists mailing list (connectionists)