Mechanization of Cognition - Biomimetics: Biologically Inspired Technologies - page 109

input from simple feature detector neurons (as opposed to primarily from extra-modular inputs, as
with the simple feature detector neurons themselves).
In certain areas of cortex (e.g., primary visual cortex) secondary feature detector neurons can
receive inputs from primary feature detector neurons ''belonging'' to other nearby modules. This is
an example of why it is not correct to say that modules are disjoint and noninteracting (which
nonetheless is exactly how we will treat them here).
Just as with the primary neurons, the secondary feature detector neurons also self-organize along
the lines of a VQ codebook — except that this codebook sits to some degree ''on top'' of the simple
cell codebook. The net result is that secondary feature neurons tend to learn statistically common
combinations of multiple coexcited simple feature detector neurons, again, with only modest
redundancy and with little information loss.
A new key principle postulated by the theory relative to these populations of feature detector
neurons is that secondary (and tertiary — see below) feature detector neurons also develop
inhibitory connections (via growth of axons of properly interposed inhibitory interneurons that
receive input from the secondary feature detector neurons) that target the simple feature detector
neurons which feed them. Thus, when a secondary feature detector neuron becomes highly excited
(partly) by simple feature detector neuron inputs, it then immediately shuts off these simple
neurons. This is the theory's precedence principle . In effect, it causes groups of inputs that are
statistically ''coherent'' to be re-represented as a whole ensemble; rather than as a collection of
''unassembled'' pieces. For example, in a visual input, an ensemble of simple feature detector
neurons together representing a straight line segment might be re-represented by some secondary
feature detector neurons which together represent the whole segment. Once activated by these
primary neurons, these secondary neurons then, by the precedence principle, immediately shut off
(via learned connections to local inhibitory interneurons) the primary neurons that caused their
activation.
Once the secondary feature detectors of a module have stabilized they too are then frozen and (at
least in certain areas of cortex) tertiary feature detectors (often coding even larger complexes of
statistically meaningful inputs) form their codebook. They too obey the precedence principle. For
example, in primary visual cortical regions, there are probably tertiary feature detectors which code
long line segments (probably both curved and straight) spanning multiple modules. Again, this is one
example of how nearby modules might interact — such tertiary feature detectors might well inhibit
and shut off lower-level feature detector neurons in other nearby modules. Of course, other inhibitory
interactions also develop — such as the line ''end stopping'' that inhibits reactions of line continu-
ation feature detectors beyond its end. In essence, the interactions within cortex during the short time
span of its reaction to external input (20 to 40 msec) are envisioned by this theory as similar to the
''competitive and cooperative neural field interactions'' postulated by Stephen Grossberg and Gail
Carpenter and their colleagues in their visual processing theories (Carpenter and Grossberg, 1991;
Grossberg, 1976, 1987, 1997; Grossberg et al., 1997). When external input (along with an operate
command) is provided to a developed module, the above brief interactions ensue and then a
single symbol (or a small set of symbols, depending upon the manner in which the operate command
to the module is manipulated) representing that input is expressed. The process by which the symbols
are developed from the feature detector neuron responses is now briefly discussed.
Once the feature detector neurons (of all orders) have had their responses frozen, the next step is
to consider the sets of feature detector neurons which become highly excited together across the
cortical region due to external inputs. Because the input wiring of the feature detector neurons is
random and sparse; the feature detector neurons function somewhat like VQ codebook vectors with
many of their components randomly zeroed out (i.e., like ordinary VQ codebook vectors projected
into randomly selected low-dimensional subspaces defined by the relatively sparse random axonal
wiring feeding the feature detector neurons of the module). In general, under these circumstances, it
can be established that any input to the region (again, whether from thalamus, from other cortical
regions, or from other extracortical sources) will cause a roughly equal number of feature detector