Looking for a paper suggestion

By Brandon Towl

I was hoping the readers of Brains could help me out with a paper suggestion: I am 
looking for a paper that would be a good example of a study from computational 
neuroscience and/or computer modeling in cognitive science. Something that shows
 the basics of the approach-- and, preferably, something I can give to advanced
 undergrads.  Under 20 pages long would be ideal. Any ideas?


  1. Steve Petersen

    If you don’t want it too techy, I think an excerpt either from Thagard’s Computational Philosophy of Science or Churchland’s The Engine of Reason, the Seat of the Soul – those are fun.

  2. Several of Richard FitzHugh’s early papers from the 60s do a good job of explaining the Hodgkin-Huxley model and his simplification of it, both of which are still relevant to computational neuroscience. They’ve got some math, but they’re easier to get through than many others, and they’re the right length:


    For psychology, I would choose Philip Johnson-Laird’s “Comprehension as the Construction of Mental Models” in Philosophical Transactions of the Royal Society of London, Series B, Biological Sciences, Vol. 295, No. 1077, pp. 353-374.

  3. There is little question that Hodgkin and Huxley provide the most important class of models, and the biggest success story of computational neuroscience. This is because the models have been grounded in cellular biophysics, are the basis for “biologically realistic” network-level models, and pretty much establishes how we (neuroscientists) think about single neurons from a computational perspective.

    Given that, I would just have them read one of their original papers, or probably a good summary. I prefer this to the Fitzhugh-Nagumo. The latter is cool, but assumes familiarity with HH, and pretty much is cool because it is able to describe the essential HH dynamics with fewer differential equations. It is hard to appreciate it without an appreciation of HH.

    If all the HH articles are too hard and too
    long for undergrads, I’d just pick the relevant chapter out of any neuroscience textbook
    such as Kandel and Schwartz, or Zigmond and Bloom, or Purves et al..
    The latter is particularly good for undergrads. Any general neuro text
    will give a good overview of what HH did.

    On the other hand, for theoretical psychology with a neuronal taste,
    you could go for the artificial neural network models like the
    connectionists of yore. These have not been very helpful in neuroscience,
    but they have been very helpful in psychology. In that case, I’d
    consider something from Paul Churchland as it doesn’t require all the
    math but expresses things clearly. Or Jeff Elman’s work, like Finding
    Structure in Time is great.

    Link to overview of conductance-based models .

    The HH papers (all available here):
    1. Hodgkin, A.L. and Huxley, A.F., and Katz, B (1952) Measurement of current-voltage relations in the membrane of the giant axon of Loligo. Journal of Physiology, 116: 424-448.
    2. Hodgkin, A.L. and Huxley, A.F. (1952) Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo.  Journal of Physiology, 116: 449-472.
    3. Hodgkin, A.L. and Huxley, A.F. (1952) The components of membrane conductance in the giant axon of Loligo.Journal of Physiology, 116: 473-496.
    4. Hodgkin, A.L. and Huxley, A.F. (1952) The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. Journal of Physiology, 116: 497-506.
    5. Hodgkin, A.L. and Huxley, A.F. (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4): 500-544.

  4. David Kaplan

    All the above suggestions are great. If your aim is to get students to appreciate the role of mathematical modeling and computer simulation in neuroscience then, of course, the H-H model is seminal. However, for a importantly different kind of contribution to computational neuroscience I’d recommend something like the following:

    Zipser D, Andersen RA.
    A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons.
    Nature. 1988 Feb 25;331(6158):679-84.


    This study is classic because it was among the first to use neural network modeling to support a view about the computations parietal cortex performs.

  5. Brandon N Towl

    All great suggestions– thanks! These were also great memory jogs for me, reminding me of papers I had read long ago or had somehow neglected to read long ago…

    We should do this more often on Brains!

  6. Susan Schneider

    The first three chapters of Jeffrey Hawkins’ *On Intelligence* – it is longer than 20 pages but,since he’s not a philosopher, it is not dense or inaccessible:-).

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