Computational Neuroscience

This page contains resources about Computational Neuroscience in general.

More specific information is included in each subfield.

Subfields and Concepts
See Category:Computational Neuroscience for some of its subfields.
 * Integrate-and-Fire Model
 * Izhikevich Spiking Neuron Model
 * The Hodgkin-Huxley Model
 * Hebbian Model
 * (Neural) Complexity
 * Consciousness
 * Granger Causality
 * Cognition
 * Embodiment
 * Synaptic Plasticity
 * Connectome and Connectomics

Video Lectures

 * Computational Neuroscience - Coursera

Lecture Notes

 * Computational Neuroscience by Hermann Riecke
 * Kevin Gurney's Lecture Notes in Computational Neuroscience
 * Theoretical Neuroscience by Gatsby Unit
 * Computational Neuroscience of Vision by Mary Cryan
 * Neural Information Processing by Chris Williams and Mark van Rossum

Books

 * Fornito, A., Zalesky, A., & Bullmore, E. (2016). Fundamentals of Brain Network Analysis. Academic Press.
 * Sporns, O. (2016). Discovering the Human Connectome. MIT Press.
 * Sporns, O. (2016). Networks of the Brain. MIT Press.
 * Ballard, D. H. (2015). Brain Computation as Hierarchical Abstraction. MIT Press.
 * Gerstner, W., Kistler, W. M., Naud, R., & Paninski, L. (2014). Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. Cambridge University Press.
 * Blackmore, S. (2013). Consciousness: An Introduction. Routledge.
 * Mallot, H. A. (2013). Computational Neuroscience: A First Course. Springer.
 * Sterratt, D., Graham, B., Gillies, A., & Willshaw, D. (2011). Principles of Computational Modelling in Neuroscience. Cambridge University Press.
 * Izhikevich, E. M. (2010). Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT Press.
 * Trappenberg, T. (2010). Fundamentals of Computational Neuroscience. 2nd Ed. Oxford University Press.
 * Haykin, S. S. J., Príncipe, C., Sejnowski, T. J., & McWhirter, J. (Eds.). (2006). New Directions in Statistical Signal Processing: From Systems to Brain. MIT Press.
 * Dayan, P., & Abbott, L. F. (2005). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press.
 * Arbib, M. A. (Ed.). (2003). The Handbook of Brain Theory and Neural Networks. MIT press.
 * Lytton, W. W. (2002). From Computer to Brain: Foundations of Computational Neuroscience. Springer Science & Business Media.
 * O'Reilly, R. C., & Munakata, Y. (2000). Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MIT press.
 * Sejnowski, T. J., & Churchland, P. S. (1992). The Computational Brain. MIT press.

Scholarly Articles

 * Karwowski, W., Vasheghani Farahani, F., & Lighthall, N. (2019). Application of graph theory for identifying connectivity patterns in human brain networks: A systematic review. Frontiers in Neuroscience, 13, 585.
 * Nicola, W., & Clopath, C. (2017). Supervised learning in spiking neural networks with FORCE training. Nature communications, 8(1), 1-15.
 * Vecchio, F., Miraglia, F., & Rossini, P. M. (2017). Connectome: Graph theory application in functional brain network architecture. Clinical Neurophysiology Practice, 2, 206-213.
 * Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews Neuroscience, 15(10), 683-695.
 * Alexander-Bloch, A., Giedd, J. N., & Bullmore, E. (2013). Imaging structural co-variance between human brain regions. Nature Reviews Neuroscience, 14(5), 322-336.
 * Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013). Evidence for Hubs in Human Functional Brain Networks. Neuron, 79(4), 798-813.
 * Stam, C. V., & Van Straaten, E. C. W. (2012). The organization of physiological brain networks. Clinical neurophysiology, 123(6), 1067-1087.
 * Bullmore, E., & Sporns, O. (2012). The economy of brain network organization. Nature Reviews Neuroscience, 13(5), 336-349.
 * Bullmore, E. T., & Bassett, D. S. (2010). Brain graphs: graphical models of the human brain connectome. Annual Review of Clinical Psychology, 7, 113-140.
 * Meunier, D., Lambiotte, R., & Bullmore, E. T. (2010). Modular and hierarchically modular organization of brain networks. Frontiers in Neuroscience, 4, 200.
 * Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059-1069.
 * Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186-198.
 * Tononi, G. (2008). Consciousness as integrated information: a provisional manifesto. The Biological Bulletin, 215(3), 216-242.
 * Balduzzi, D., & Tononi, G. (2008). Integrated information in discrete dynamical systems: motivation and theoretical framework. PLoS Computational Biology, 4(6).
 * Shanahan, M. (2008). Dynamical complexity in small-world networks of spiking neurons. Physical Review E, 78(4), 041924.
 * Seth, A. K., Dienes, Z., Cleeremans, A., Overgaard, M., & Pessoa, L. (2008). Measuring consciousness: relating behavioural and neurophysiological approaches. Trends in cognitive sciences, 12(8), 314-321.
 * Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., ... & Albert, M. S. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968-980.
 * Bassett, D. S., & Bullmore, E. D. (2006). Small-world brain networks. The Neuroscientist, 12(6), 512-523.
 * Eguiluz, V. M., Chialvo, D. R., Cecchi, G. A., Baliki, M., & Apkarian, A. V. (2005). Scale-free brain functional networks. Physical Review Letters, 94(1), 018102.
 * Seth, A. K., Izhikevich, E., Reeke, G. N., & Edelman, G. M. (2006). Theories and measures of consciousness: an extended framework. Proceedings of the National Academy of Sciences, 103(28), 10799-10804.
 * Sporns, O., Tononi, G., & Kotter, R. (2005). The Human Connectome: A Structural Description of the Human Brain. PLoS Computational Biology, 1(4).
 * Sporns, O., & Zwi, J. D. (2004). The small world of the cerebral cortex. NeuroInformatics, 2(2), 145-162.
 * Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5(1), 42.
 * Izhikevich, E. M. (2004). Which model to use for cortical spiking neurons?. IEEE transactions on Neural Networks, 15(5), 1063-1070.
 * Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569-1572.
 * Tononi, G., Edelman, G. M., & Sporns, O. (1998). Complexity and coherency: integrating information in the brain. Trends in cognitive sciences, 2(12), 474-484.

Software

 * BRIAN, a Python-based simulator
 * Emergent, neural simulation software.
 * GENESIS, a general neural simulation system.
 * ModelDB, a large open-access database of program codes of published computational neuroscience models.
 * MCell, Particle-based Monte Carlo simulator of microphysiology and cell signaling.
 * Nengo, a Python scriptable, GUI simulator for large-scale neural models
 * NEST, a simulation tool for large neuronal systems.
 * Neuroconstruct, software for developing biologically realistic 3D neural networks.
 * NEURON, a neuron simulator also useful to simulate neural networks.
 * SNNAP, a single neuron and neural network simulator tool.
 * ReMoto, a web-based simulator of the spinal cord and innervated muscles of the human leg.
 * EDLUT, a simulation software for large-scale neural networks and real-time control systems.
 * Nilearn - Machine learning for Neuro-Imaging in Python
 * PyMVPA - Python

Tutorials

 * Graph theory and connectomics: An introduction by Alex Fornito

Other Resources

 * Scholarpedia - the peer-reviewed open-access encyclopedia
 * Neural Coding - Notebook
 * Neuroscience - Notebook
 * Computational Neuroscience - Nature
 * Perlewitz's computational neuroscience on the web
 * EyeWire -  a game to map the brain from MIT
 * m2g (GitHub) - code
 * OpenNeuro
 * NIPY - a Python community of practice
 * SpikeLearning (GitHub)
 * 190565 (GitHub)