ULTIMATE COMPUTING - Quantum Consciousness Studies

16 Toward Ultimate Computing
Figure 1.6: Ten dimensional hypercube with 1,024 nodes, and 10 connections
per node. Computer generation by Conrad Schneiker.
Hinton and Sejnowski have used this concept of mathematically shaking their
neural net simulations to find optimal solutions. It requires a multilevel hierarchy
of parallel systems so that one level can “shake” or tune a lower level. Such an
arrangement can perhaps explain the relationship between hierarchical layers of
parallel systems within the brain. For example, neural networks based on synaptic
connection may regulate (and be regulated by) smaller, faster, more
comprehensive networks in the intracellular cytoskeleton.
Extensive comparisons between information processing in the brain and
artificial intelligence have been reviewed by A. M. Decallatay (1986) who feels
the laws of thought described in philosophy have been rediscovered by AI: “The
mental world of Plato is reproduced in the physical symbols of Newell and
Simon.” DeCallatay observes that Al represents data by virtual pointers which
connect symbols. In computers these virtual relations are actual wires with
potential gate connection; in the brain they appear to be neuronal synaptic
connections. Within neurons they may be cross-bridge filaments connecting
cytoskeletal microtubules. As a computer expert evaluating the brain, DeCallatay
states that the brain learns by opening gates to build new connections between
elements simultaneously activated. He sees the presence or absence of dendritic
spines playing the role of an “all or none” switch at the neural level. Dendritic
spines are knobby projections of membrane covered cytoplasm on neuronal
dendrites which are generated and maintained by the cytoskeleton and form
synapses with other neurons. The most accepted theory for learning and memory