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104 Biomimetics: Biologically Inspired Technologies

Figure 3.A.2 A single thalamocortical module; side view. The module consists of a full-depth patch of cortex
(possibly comprised of multiple separate full-depth disjoint sub-patches — not illustrated here); as well as a
paired zone of thalamus. The green and red neurons in cortical layer II, III or IV illustrate the two collections of
neurons representing two symbols of the module (common neurons shared by the two collections are not shown;
nor are the axons involved in the feature attractor neuronal network function used to implement confabulation). The
complete pool of neurons within the module used to represent symbols contains many tens, or even hundreds, of
thousands of neurons. Each symbol-representing neuron collection has tens to hundreds of neurons in it. Axons
from cortical layer VI to NRT (NRT) and thalamus are shown in dashed blue. Axons from thalamic glomeruli to NRT
and cortical layer IV are shown in dashed red. Axons from NRT neurons to glomeruli are shown in pink. An axon of
the operation command input, which affects a large subset of the neurons of the module, and which arrives from an
external subcortical nucleus, is shown in green. The theory only specifies the overall information processing
function of each cortical module (implementation of the list of symbols, confabulation, and origination or termination
of knowledge links). Details of module operation at the cellular level are not known.

The mathematical model discussed below illustrates the dynamical process involved in carrying
out one confabulation. Keep in mind that this model might represent strictly cortical neuron
dynamics, module neurodynamics between the cortical and thalamic portions of the module, or
even the overall dynamics of a group of smaller attractor networks (e.g., a localized version of the
‘‘network of networks’’ hypothesis of Sutton and Anderson in Hecht-Nielsen and McKenna, 2003;
Sutton and Anderson, 1995).
In 1969, Willshaw and his colleagues (Willshaw et al., 1969) introduced the ‘‘nonholographic’’
associative memory. This ‘‘one-way’’ device (‘‘retrieval key’’ represented on one ‘‘field’’ of neurons
and ‘‘retrieved pattern’’ on a second), based on Hebbian learning, is a major departure in concept
from the previous (linear algebra-based) associative memory concepts (Anderson, 1968, 1972;
Gabor, 1969; Kohonen, 1972). The brilliant Willshaw design (an absolutely essential step towards
the theory presented in this Appendix) is a generalization of the pioneering Steinbuch learnmatrix
(Steinbuch, 1961a,b, 1963, 1965; Steinbuch and Piske, 1963; Steinbuch and Widrow, 1965);
although Willshaw and his colleagues were not aware of this earlier development. For efficiency,
it is assumed that the reader is familiar with the Willshaw network and its theory (Amari, 1989;
Kosko, 1988; Palm, 1980; Sommer and Palm, 1999). A related important idea is the ‘‘Brain State in
a Box’’ architecture of Anderson et al. (1977).
In 1987, I conceived a hybrid of the Willshaw network and the Amari or Hopfield ‘‘energy
function’’ attractor network (Amari, 1974; Amit, 1989; Hopfield, 1982, 1984). In effect, this hybrid