Biological Modeling

  • George Reeke
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:


Firing Rate Adult Neurogenesis Synaptic Strength Connection Strength Postsynaptic Cell 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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
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  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
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  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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