Computational Neuroscience
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This page contains resources about Computational Neuroscience in general.
More specific information is included in each subfield.
Contents
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
- Consciousness
- Cognition
- Embodiment
- Synaptic Plasticity
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]
- 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.
- 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.
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.
See also[edit]
- Machine Learning
- Statistical Signal Processing
- Control Theory
- Linear Dynamical Systems
- Nonlinear Systems
- Information Theory
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
