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]
- Computational Neuroscience - Coursera
Lecture Notes[edit]
- 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[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.
Software[edit]
- 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[edit]
See also[edit]
- Machine Learning
- Statistical Signal Processing
- Control Theory
- Linear Dynamical Systems
- Nonlinear Systems
- Information Theory
- Medical Imaging
- Network Science
Other Resources[edit]
- 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)