Computational Neuroscience

From Ioannis Kourouklides
Jump to: navigation, search
Digital-brain.jpg

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
  • Consciousness
  • Cognition
  • Embodiment
  • Synaptic Plasticity

Online Courses[edit]

Video Lectures[edit]


Lecture Notes[edit]

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]

Other Resources[edit]