In article <10092 at blue.cis.pitt.edu.UUCP> pinto at neurocog.lrdc.pitt.edu (David Pinto) writes:
>If Neurons are being modelled in very large groups, or as node points, without
>taking the specifics of each neuron into account, then connectionist models
>are the current favorite.
>>For models of one, two, three or maybe a few more neurons, compartmental models
>seem to be prefered.
There are also sort of "in between" modeling philosophies. In the
work I am doing large arrays (10,000 or so) of neurons are modeled
with attention to the detail of individual units. I use, essentially,
point neurons based on the phenomenological model of Hill (1936), which
are quite removed from the connectionist "neurons". They are capable
of producing spike waveforms and representing a variety of different
neuronal response types (primary-type, chopper, etc). And they
compute quite a bit faster that Hodgkin-Huxley neurons. For more
details, see Parnas and Lewis (1992).
Parnas BR and ER Lewis (1992), "A computationally efficient spike
initiator model that produces a wide variety of neuronal response
types", in Neural Systems: Analysis and Modeling. FH Eeckman,
editor. Kluwer Academic Press, Norwell, MA.
Hill AV (1936), "Rxcitation an accommodation in nerve", Proc.
Royal Soc. B., vol. 119, pp. 305-355.
(brp at bandit.berkeley.edu)