ULTIMATE COMPUTING - Quantum Consciousness Studies

Brain/Mind/Computer 41
2.2.9 Neural Net Connectionism
This movement has been fortified by computer scientists’ efforts to mimic the
brain by constructing artificial intelligence systems. Based on approximation of
cortical neurons as linear threshold units, a large number of “neural net” models
have been constructed and simulated on computers. A key concept in relating
neural network dynamics to the facts of psychology is the cell assembly
introduced by Donald Hebb in 1949. In his view, cell assemblies are specially
organized reverberatory circuits that constitute elements of thought. An individual
neuron may participate in many of them just as an individual member of society
participates in many social assemblies. By allowing strengthening or
reinforcement of repeatedly used connections (“synaptic plasticity”), recognition,
learning and problem solving become manifest in lowered thresholds of specific
loops. By assigning energy levels to various patterns (“landscapes”) within the
net, mathematical solutions can also be imposed. The “intelligence” or
capabilities of a given neural net model depends on the richness of its
interconnections and nonlinear feedback. Neural net connectionist theory may
help to advance robotic and computer systems for artificial intelligence, and may
provide significant insight into brain function. These theories have provided
evidence that dynamic activities within a given network can at least mimic some
aspects of brain activities.
Shortcomings of early neural net models are that they have been based on
hypothetical neurons with huge assumptions about neural function. Each neuron
has been considered an on/off gate or switch, and interneuron synapses viewed as
variable weight interconnections. More recent models incorporate axonal
impulses, synaptic delays, dendritic analog functions and spatial coherence. In
their most elegant form, neural net theories provide possible representations of
mental objects (“consciousness”) in the transient instantaneous patterns of
network activity. “Temporally stable cooperative coupling” among sets of
neurons are suggested to manifest thoughts and images by the work of Hebb,
Kohonen, Edelman, Thom, von der Malsburg, Hopfield, Pellionisz, Llinas,
Changeux, and others. Some of their work suggests the brain forms sets of
“prerepresentations” of what is expected from which sensory input induces
“selection” of its reality candidate. In this context, Changeux (1985) has defined
consciousness as “a kind of global regulatory system dealing with mental objects
and computation using those objects.” Neural net models and associative
memories have significantly advanced understanding of collective neural
capabilities. Their essential features (parallel processors with lateral variable
connections) may also be operant within neurons in the cytoskeleton.