Paul Hughes <paul@planetp.cc> writes:
> Robin Hanson wrote:
>
> > Paul Hughes wrote:
> > >So the question remains what amount of computational
> > >machinery is required to replace the current role that a
> > >neuron plays? Any takers?
> >
> > Here's a data point I came across recently:
> >
> > http://www.cacr.caltech.edu/Publications/annreps/annrep92/schutt.html
> >
> > It's a 92 paper describing a simulation of one specific
> > complex neuron.
>
> Impressive! Using their somewhat simplified simulation of the most
> complex neuron in the nervous system (the Purkinje), it still took their
> i860 processor almost an hour to run a simulation of a single firing.
> How this translates into computational requirements for the typical
> neuron remains to be seen, but I think it proves that simulating a single
> neuron is not a trivial task.
Definitely not trivial. The Purkinje cell might be a bit more
branching and have more synapses than the vanilla pyramidal cells of
the cortex, but not much.
In most computational neuroscience models of neurons they are divided
into compartments treated as isopotential; they are each described by
their membrane potential, the concentrations of sodium, potassium,
calcium and sometimes chloride ions and the state of various ion
channels. The number of compartment varies a lot, 100,000 is on the
largest scale so far - we are quite limited by our lack of really good
morphological cell data not to mention electrophysiological data.
Other chemical processes need to be added at the synapses, especially
for plasticity models. A fellow researcher made a simplified (!) model
involving just 30-40 chemicals (many of which were variously
phosphorylated states of a protein) of the long term potentiation
phenomenon.
Yes, neurons are much more complex than simple transistors. But it is
not obvious that all this complexity makes them *much* more complex -
there are clever simplifications of certain kinds of dendritic trees
and many of the chemical networks could be described more compactly
using a phenomenological model. There are ways of doing reductions of
cells that (at the price of requiring sharp minds) can create models
that does qualitatively and often quantitatively the same as much more
complex models.
The real complexity lies in the manifold interactions between neurons,
individual neurons have a fairly limited repertoire of behavior but
when you connect a few of them into a network, especially modulated by
different chemicals, then they can do nearly anything.
-- ----------------------------------------------------------------------- Anders Sandberg Towards Ascension! asa@nada.kth.se http://www.nada.kth.se/~asa/ GCS/M/S/O d++ -p+ c++++ !l u+ e++ m++ s+/+ n--- h+/* f+ g+ w++ t+ r+ !y
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