Graduation Year

2009

Document Type

Thesis

Degree

M.A.

Degree Granting Department

Mathematics and Statistics

Major Professor

Richard Stark, Ph.D.

Co-Major Professor

Natasa Jonoska, Ph.D.

Committee Member

Mile Krajcevski, Ph.D.

Keywords

state, probability, serial, synchronous, halting

Abstract

Information processing in living tissues is dramatically different from what we see in common man-made computer. The data and processing is distributed into the activity of cells which communicate only with neighboring cells. There is no clock for the global synchronization of cellular activities. There is not even one bit of central memory for globally shared data. The communication network between cells is highly irregular and may change without changing the outcome of the computation. A simple network of automata is introduced and analyzed to represent a mathematical model of special group of cells in an imaginary tissue sample. The interaction between the cells, their communication method, and their level of intelligence is studied. Three different structures of this model are demonstrated. Later on a simplification in the cells' program and elimination of a beat keeping clock will lead to a finite state automata network that is surprisingly more powerful in achieving the overall network's goal than its previous generation which had the advantage of more complex programs and a beat keeping clock.

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