Computational Neuroscience

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This page contains resources about Computational Neuroscience in general.

More specific information is included in each subfield.

Subfields and Concepts[edit]

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

Online Courses[edit]

Video Lectures[edit]

Lecture Notes[edit]


  • 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[edit]

  • 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.


  • 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


See also[edit]

Other Resources[edit]