Biological Modeling

Reference work entry


From the earliest days when it became clear that the brain is the organ that controls behavior, and that the brain is an incredibly complex system of interconnected cells of multiple types, scientists have felt the need to somehow relate the information so laboriously gathered regarding the physiology and connectivities of individual cells with the externally observable functions of the brain. Thus, one would like to have answers to questions like these:


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Further Reading

  1. Bienenstock EL, Cooper LN, Munro PW (1982) Theory for the development of neuron selectivity: Orientation specificity and binocular interaction in visual cortex. J Neurosci 2:32PubMedGoogle Scholar
  2. Dayan P, Abbott LF (2001) Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT Press, Cambridge, MAGoogle Scholar
  3. Izhikevich EM (2007) Dynamical systems in neuroscience: the geometry of excitability and bursting. MIT Press, Cambridge, MAGoogle Scholar
  4. Koch C, Segev I (1998) Methods in neuronal modeling. From ions to networks, 2nd edn. MIT Press, Cambridge, MAGoogle Scholar
  5. Krichmar JL, Edelman GM (2008) Design principles and constraints underlying the construction of brain-based devices. In: Ishikawa M et al (eds) Neural information processing, vol 4985/2008, Lecture notes in computer science. Springer, Berlin/Heidelberg, pp 157–166. doi:10.1007/978-3-540-69162-4_17CrossRefGoogle Scholar
  6. McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115CrossRefGoogle Scholar
  7. Rieke F, Warland D, de Ruyter van Steveninck R, Bialek W (1997) Spikes: exploring the neural code. MIT Press, Cambridge, MAGoogle Scholar
  8. Rosenblatt F (1958) The perceptron: a theory of statistical separability in cognitive systems. Report no. VG-1196-G-1. Cornell Aeronautical Laboratory, Buffalo, New YorkGoogle Scholar
  9. Standage D, Jalil S, Trappenberg T (2007) Computational consequences of experimentally derived spike-time and weight dependent plasticity rules. Biol Cybern 96:615PubMedCrossRefGoogle Scholar
  10. Sterratt D, Graham B, Gillies A, Willshaw D (2011) Principles of computational modelling in neuroscience. Cambridge University Press, Cambridge/New YorkCrossRefGoogle Scholar
  11. Sutton RS, Barto AG (1998) Reinforcement learning: an introduction. MIT Press, Cambridge, MAGoogle Scholar
  12. Traub RD, Miles R (1991) Neuronal networks of the Hippocampus. Cambridge University Press, CambridgeCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2013

Authors and Affiliations

  1. 1.Rockefeller University Laboratory of Biological ModelingNew YorkUSA

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