Cognitive Neurodynamics

, Volume 9, Issue 5, pp 479–485 | Cite as

Teaching computational neuroscience

  • Péter ÉrdiEmail author
Review Paper


The problems and beauty of teaching computational neuroscience are discussed by reviewing three new textbooks.


Computational neuroscience Education Models 

Mathematics Subject Classification

00A17 97U20 



Thanks for my numerous teaching assistants over the years. I had many conversation with them about the method of teaching of this discipline. I also thank to the Henry Luce Foundation to let me to serve as a Henry R Luce Professor. Thank you for Brian Dalluge (who is now in my Computational Neuroscience class) for copy editing the manuscript.


  1. Izhikevich E (2007) Dynamical systems in neuroscience. MIT press, CambridgeGoogle Scholar
  2. Anderson B (2014) Computational neuroscience and cognitive modelling. A student’s introduction to methods and procedures. Sage Publ. Ltd, Beverley HillsCrossRefGoogle Scholar
  3. Arbib M (2002) The handbook of brain theory and neural networks. MIT Press, CambridgeGoogle Scholar
  4. Bower J (2013) 20 Years of computational neuroscience. Springer, VerlagCrossRefGoogle Scholar
  5. Dayan P, Abbott L (2001) Theoretical neuroscience. MIT Press, CambridgeGoogle Scholar
  6. Ermentrout G, Terman T (2010) Mathematical foundations of neuroscience. Springer, BerlinCrossRefGoogle Scholar
  7. Gerstein GL, Mandelbrot B (1964) random walk models for the spike activity of a single neuron. Biophys J 4(1):41–68PubMedCentralCrossRefPubMedGoogle Scholar
  8. Gerstner W, Kistler W, Naud R, Paninski L (2014) Neuronal dynamics—from single neurons to networks and models of cognition. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  9. Grossberg S (1980) How does a brain build a cognitive code? Psychol Rev 87:1–51CrossRefPubMedGoogle Scholar
  10. Hirsch M (1989) Convergent activation dynamics in continuous time networks. Neural Netw 2:331349CrossRefGoogle Scholar
  11. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational properties. Proc Nat Acad Sci (USA) 79:2554–2558CrossRefGoogle Scholar
  12. Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-sate neurons. Proc Natl Acad Sci (USA) 81:3088–3092CrossRefGoogle Scholar
  13. Levy W, Steward O (1983) Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neuroscience 8:791–797CrossRefPubMedGoogle Scholar
  14. Mallot H (2013) Computational neuroscience. A first course. Springer, BerlinCrossRefGoogle Scholar
  15. Schwartz E (ed) (1990) Computational neuroscience. MIT Press, CambridgeGoogle Scholar
  16. Trappenberg T (2010) Fundamentals of computational neuroscience. Oxford Univ. Press, OxfordGoogle Scholar
  17. Tsuda I (1992) Dynamic link of memorychaotic memory map in nonequilibrium neural networks. Neural Netw 5:313–326CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Center for Complex Systems StudiesKalamazoo CollegeKalamazooUSA
  2. 2.Institute for Particle and Nuclear Physics, Wigner Research Centre for PhysicsHungarian Academy of SciencesBudapestHungary

Personalised recommendations