Information Technology Reference
In-Depth Information
Figure 4.6.
training a three-homeostat system. the lines running from left to
right indicate the positions of the needles on the tops of units 1, 2, and 3. the
punishments administered to unit 3 are marked
D
1
and
D
2
. the shifts in the uniselec-
tors are marked as vertical blips on the bottom line,
u
. note that after the second
punishment a downward displacement of needle 1 evokes an upward displacement of
needle 2, as desired. source: w. r. ashby,
Design for a Brain: The origin of adap-
tive Behaviour
, 2nd ed. (london: chapman & Hall, 1960), 114, fig. 8/9/1. (with kind
permission from springer science and business media.)
system fell into an equilibrium in which the correlation between the needles
1 and 2 was the wrong way around, Ashby would punish homeostat 3 by push-
ing its needle to the end of its range, causing its uniselector to trip, until the
right kind of equilibrium for the entire system, with an anticorrelation of
needles 1 and 2, was achieved. Figure 4.6
shows readouts of needle positions
from such a training session.
Ashby thus sought to establish an equation between his general analysis
of ultrastable systems and brains by setting out a range of exemplary applica-
tions to the latter. Think of the response of animals to surgery, and then think
about it
this
way. Think about training animals; then think about it
this
way.
In these ways, Ashby tried to train his readers to make this specific analogical
leap to the brain.
But something is evidently lacking in this rhetoric. One might be willing
to follow Ashby some of the way, but just what are these step mechanisms that
enable animals to cope with perverse surgery or training? Having warned that
“we have practically no idea of where to look [for them], nor what to look for
[and] in these matters we must be vary careful to avoid making asssumptions
unwittingly, for the possibilities are very wide” (1960, 123), Ashby proceeds to
sketch out some suggestions.
One is to note that “every cell contains many variables that might change
in a way approximating to the step-function form. . . . Monomolecular films,
![](The-Cybernetic-Brain_images/img-gen87.png)