ULTIMATE COMPUTING - Quantum Consciousness Studies

12 Toward Ultimate Computing
information (Figure 1.3). The cytoskeleton can convey analog patterns which may
be connected symbols (Chapter 8). Although overlooked by AI researchers, the
cytoskeleton may take advantage of the same attributes used to describe neural
level networks. Properties of networks which can lead to collective effects among
both neurons and cytoskeletal subunits include parallelism, connectionism, and
coherent cooperativity.
1.3.1 Parallelism
The previous generations of computer architecture have been based on the
von Neumann concept of sequential, serial processing. In serial processing,
computing steps are done consecutively which is time consuming. One false bit of
information can cascade to chaotic output. The brain with its highly parallel nerve
tracks shines as a possible alternative. In parallel computing, information enters a
large number of computer pathways which process the data simultaneously. In
parallel computers information processors may be independent of each other and
proceed at individual tempos. Separate processors, or groups of processors, can
address different aspects of a given problem asynchronously. As an example,
Reeke and Edelman (1984) have described a computer model of a parallel pair of
recognition automata which use complementary features (Chapter 4). Parallel
processing requires reconciliation of multiple outputs which may differ due to
individual processors being biased differently than their counterparts, performing
different functions, or because of random error. Voting or reconciliation must
occur by lateral connection, which may also function as associative memory.
Output from a parallel array is a collective effect of the input and processing, and
is generally a consensus which depends on multiple features of the original data
input and how it is processed. Parallel and laterally connected tracks of nerve
fibers inspired AI researchers to appreciate and embrace parallelism. Cytoskeletal
networks within nerve cells are highly parallel and interconnected, a thousand
times smaller, and contain millions to billions of cytoskeletal subunits per nerve
cell!
Present day evolution of computers toward parallelism has engendered the
“Connection Machine” (Thinking Machines, Inc.) which is a parallel assembly of
64,000 microprocessors. Early computer scientists would have been impressed
with an assembly of 64,000 switches without realizing that each one was a
microprocessor. Similarly, present day cognitive scientists are impressed with the
billions of neurons within each human brain without considering that each neuron
is itself complex.
Another stage of computer evolution appears as multidimensional network
parallelism, or “hypercubes.” Hypercubes are processor networks whose
interconnection topology is seen as an “n-dimensional” cube. The “vertices” or
“nodes” are the processors and the “edges” are the interconnections. Parallelism
in “n-dimensions” leads to hypercubes which can maximize available computing
potential and, with optimal programming, lead to collective effects. Complex
interconnectedness observed among brain neurons and among cytoskeletal
structures may be more accurately described as hypercube architecture rather than
simple parallelism. Hypercubes are exemplified in Figures 1.4, 1.5, and 1.6.
Al/Roboticist Hans Moravec (1986) of Carnegie-Mellon University has
attempted to calculate the “computing power” of a computer, and of the human
brain. Considering the number of “next states” available per time in binary digits,
or bits, Moravec arrives at the following conclusions. A microcomputer has a
capacity of about 10 6 bits per second. Moravec calculates the brain “computing”
power by assuming 40 billion neurons which can change states hundreds of times
per second, resulting in 40 x 10 11 bits per second. Including the cytoskeleton