[Comp-neuro] Re: Attractors, variability and noise
andrew.coward at anu.edu.au
Thu Aug 14 02:36:35 CEST 2008
On 13-Aug-08, at 3:36 PM, Prof Leslie Smith wrote:
Andrew Coward wrote
Understanding the brain will require an analogous hierarchy of
descriptions. In other words, we have to find good approximations at
several intermediate levels that can be mapped both into psychology
and into more detailed neuron type models. One attempt at such a
multilevel theory is [Coward 2005]. The danger of massive neuron
modelling efforts is that we can create a system which may have
properties similar to brains but do not help with genuine
Prof Leslie Smith wrote:
Engineers build complex system using hierarchical design: without
question this makes them much easier to design and debug and extend
etc. But biological systems are not designed as such: as such they
don't need to work within hierarchies. Entities within biological
systems can interact across levels. And they do: even simple GA based
experiments like Adrian Thompson's FPGA's took advantage of
unexpected interactions on an FPGA, and (for example) animal brains
often use neuromodulators whose release affects a volume of the
brain. Clearly, crossing levels is dangerous (which is why engineers
avoid it) but it can result in highly efficient "clever" solutions
(which is why evolution can utilise them).
A multi-level theory may help us to take an engineer's view of neural
systems, but the view that it results in is likely to be incomplete.
No question that design “across levels” can have efficiency
advantages. However, such advantages have to be “paid for” whether in
an electronic system or a biological system.
In an electronic system, across levels design makes any future
feature changes much more difficult. I had some experience with an
operating system which took advantage of some timing relationships
within one of its custom integrated circuits. Updating the integrated
circuits as technology evolved became a horrendously complex
undertaking. If across level design occurs in software, the result is
that any future changes have large number of undesirable side
effects, and the correction of these side effects introduces yet more
cross level design fixes, making the next change even more difficult.
Sometimes the only solution is to duplicate large amounts of code in
the software system: one copy is changed for the new features, the
other copy left unchanged for older features. In other words, cross
level design when combined with a need to change has to be paid for
with a large increase in system resources. This obviously results in
increases in the size of the software, and I suspect that the huge
increases on PC operating systems over the years may be partially a
result of this type of problem.
However, biological systems are not immune to this type of problem.
They need to change in the course of evolution, and some biological
systems need to learn. The price of cross level “design” will be much
the same, a much higher resource cost imposed on change and learning.
Hence given resource limitations there will be strong natural
selection advantages in favour of modular hierarchies. Obviously this
is not an absolute, sometimes the competitive advantage of a “cross
level” option will outweigh the resource penalties, but overall the
modular hierarchies will be tend to be relatively “clean” because a
brain that can perform a given set of functions with fewer resources
will have a big selection advantage.
If this were not the case, our brains could be “in principle”
incomprehensible to our own intellectual capabilities, a position
which has found favour with some philosophers. Hence the idea that
natural selection tends to constrain brains into relatively clean
modular hierachies is a relatively optimistic one.
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