ULTIMATE COMPUTING - Quantum Consciousness Studies

70 From Brain to Cytoskeleton
review which discouraged neural net research until Hopfield’s resurgence in the
1980’s. Hopfield introduced an energy function so that information in a neural net
circuit would settle into a series of stable energy states much like rain water
falling on mountains flows through valleys into lakes and rivers. Depending on
the rainfall, an information state (i.e. memory, conscious image, thought) would
be a given watershed pattern. Hopfield’s neural nets are loosely based on aspects
of neurobiology but readily adapted to integrated circuits. The collective
properties of his model produce a content addressable memory (described by a
phase space flow of the state of the system) which correctly yields an entire
memory from any sub-part of sufficient size. The algorithm for the time evolution
of the state of the system is based on asynchronous parallel processing. Additional
emergent collective properties include some capacity for generalization,
familiarity recognition, categorization, error correction, time sequence retention,
and insensitivity to failure of individual components. Hopfield nets and similar
models are best categorized with the “tabula rasa” view of learning in which the
initial state is taken as a flat energy landscape which becomes progressively
contoured, eroded and complicated by direct interactions with the environment.
A selectionist approach to neural net theory has been taken by Jean Pierre
Changeux, who pioneered description of allosteric interactions among proteins.
Turning to the brain/mind, Changeux and colleagues (1984, 1985) have proposed
a model of learning by selection based on the most recent advances in the
statistical mechanics of disordered systems, namely the theory of spin glasses.
Spin glasses are materials which are not crystalline, yet whose atoms possess a
high degree of similar neighbor relationships and a finite number (i.e. 2) of
magnetic spin states influenced by their neighbors. Aggregates of “like spin”
states beget similar states among neighbors. Consequently the spin states of atoms
in a spin glass can be viewed as a network (or cellular automaton) much like a
collection of neurons in a nervous system. Changeux also uses terms from
mathematical chaos theory like basins and attractors to describe the states to
which the spin glass model evolves. Unlike the blank slate approach, the brain’s
initial state is viewed by Changeux as a complex energy landscape with an
exuberance of valleys typical of spin glasses. Each valley corresponds to a
particular set of active neurons and plays the role of a prerepresentation. An input
pattern sets an initial configuration which converges towards a valley whose entry
threshold is lowered by synaptic modification. Starting from a hierarchical
distribution of valleys, the “lowest” valleys (sea level fjords) would correspond to
maximal comprehension, ultimate answer, best correlation. The learning process
is viewed as smoothening, gardening, and evolutionary pruning as already stored
information influences the prerepresentations available for the next learning
event. Changeux’s spin glass model of neural nets is elegant, and successfully
presents a hierarchical pattern of static information sorting. It’s shortcomings are
that it is unidirectional and fails to describe dynamic, “real time” information
processing.
Another selective connectionist network model of learning is that of George
Reeke and Gerald Edelman (1984) of Rockefeller University. They describe two
parallel recognition automaton networks which communicate laterally. Automata
are dynamic patterns of neighbor interactions capable of information processing
(Chapter 1). The two parallel recognition automata which Edelman and Reeke
devised have distinct and complementary personalities. They are named Darwin
and Wallace after the co-developers of the theory of evolution, and utilize
different approaches to the problem of recognition. “Darwin” is highly analytical,
keyed to recognizing edges, dimensions, orientation, intensity, color, etc.
“Wallace” is more “gestalt” and attempts to merely categorize objects into