ULTIMATE COMPUTING - Quantum Consciousness Studies

From Brain to Cytoskeleton 69
implied that at birth there existed a vast number of redundant and ineffective
synaptic conditions which became “selected” during the individual’s lifetime of
experience. An alternative view is that, at birth, excitations can pass between any
two points of the CNS through a random network of connections. As maturation,
experience, and learning occurred, synaptic activity gradually sculpted usable
patterns by suppressing unwanted interconnections.
Thus the connectionist brain/mind became viewed as one of two types of
systems: a blank slate (“tabula rasa”) in which acquired learning and internal
organization result from direct environmental imprinting, or a “selectionist”
network chosen from a far vaster potential network. Selectionists believe that the
brain/mind spontaneously generates variable patterns of connections during
childhood periods of development referred to as “transient redundancy,” or from
variable patterns of activity called prerepresentations in the adult. Environmental
interactions merely select or selectively stabilize preexisting patterns of
connections and/or neural firings which fit with the external input. Selectionists
further believe that, as a correlate of learning, connections between neurons are
eliminated (pruning) and/or the number of accessible firing patterns is reduced.
Supporting a selectionist viewpoint is the observation that the number of neurons
and apparent synapses decreases during certain important stages of development
in children. However, this reduction could be masking an increase in complexity
among dendritic arborizations, spines, synapses, and cytoskeleton. The
selectionist view is also susceptible to the argument that new knowledge would
appear difficult to incorporate.
On the assumption that the basic mode of learning and consciousness within
the brain is based on synaptic connections among neurons (connectionist view)
several attempts to model learning at the level of large assemblies of
interconnected neurons have been made. Hebb pioneered this field by proposing
that learning occurred by strengthening of specific synaptic connections within a
neuronal network. This led to a concept of functional groups of neurons
connected by variable synapses. These functional groups as anatomical brain
regions have been described by various authors as networks, assemblies, cartels,
modules or crystals. These models are aided by the mathematics of statistical
mechanics and have been rejuvenated due to the work of Hopfield (1982),
Grossberg (1978), Kohonen (1984) and others who drew analogies between
neural networks within the brain and properties of computers leading to
applications for artificial intelligence. They emphasized that computational
properties useful to biological organisms or to the construction of computers can
emerge as collective properties of systems having a large number of simple
equivalent components or neurons with a high degree of interconnection. Neural
networks started as models of how the brain works and have now engendered
chips and computers constructed with neural net connectionist architectures
utilizing hundreds of computing units and linking them with many thousands of
connections. Hopfield (1982) remarks that neural net chips can provide finely
grained and massively parallel computing with:
a brainlike tolerance for fuzzy facts and vague instructions. Some of
the general properties you get in these systems are strikingly like ...
properties we see in neurobiology ... . You don’t have to build them
in; they’re just there ... .
Neural networks had formally appeared in Rosenblatt’s (1962) “perceptron”
model of the 1950’s and 1960’s. Perceptrons created enthusiasm, but failed to
reach their potential due to limitations of the model and its mathematics. Al
experts Marvin Minsky and Seymour Papert (1972) wrote a harshly critical