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This thesis presents a study of stochastic arrays and learning networks. These arrays will be shown to consist of simple elements utilising probabilistic coding techniques which may interact with a random and noisy environment to produce useful results. Such networks have generated considerable interest since it is possible to design large parallel self-organising arrays of these elements which are trained by example rather than explicit instruction. Once the learning process has been completed, they then have the potential ability to form generalisations, perform global optimisation of traditionally difficult problems such as routing and incorporate an associative memory capability which can enable such tasks as image recognition and reconstruction to be performed, even when given a partial or noisy view of the target. Since the method of operation of such elements is thought to emulate the basic properties of the neurons of the brain, these arrays have been termed neural 'networks. The research demonstrates the use of stochastic elements for digital signal processing by presenting a novel systolic array, utilising a simple, replicated cell structure, which is shown to perform the operations of Cyclic Correlation and the Discrete Fourier Transform on inherently random and noisy probabilistic single bit inputs. This work is then extended into the field of stochastic learning automata and to neural networks by examining the Associative Reward-Punish (A(_R-P)) pattern recognising learning automaton. The thesis concludes that all the networks described may potentially be generalised to simple variations of one standard probabilistic element utilising stochastic coding, whose properties resemble those of biological neurons. A novel study is presented which describes how a powerful deterministic algorithm, previously considered to be biologically unviable due to its nature, may be represented in this way. It is expected that combinations of these methods may lead to a series of useful hybrid techniques for training networks. The nature of the element generalisation is particularly important as it reveals the potential for encoding successful algorithms in cheap, simple hardware with single bit interconnections. No claim is made that the particular algorithms described are those actually utilised by the brain, only to demonstrate that those properties observed of biological neurons are capable of endowing collective computational ability and that actual biological algorithms may perhaps then become apparent when viewed in this light.

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There are already excellent techniques for imaging the anatomy of the human brain, and changes in its functional state over seconds. Furthermore, the function of the individual neuron has been studied at time resolutions of less than a millisecond. There is, at present, no technique which combines this fine time resolution with tomographic imaging, although this may be essential if we are ever to understand the processing of information by the brain. The work described in this thesis used mathematical models and in-vivo measurement to investigate whether applied potential tomography (APT), a recently-developed imaging technique, may be used for imaging neuronal depolarisation in the brain. Two factors suggest that APT may be suitable: first, it can acquire data sets at the required rate; second, there is known to be a decrease in the resistance of neuronal membranes, perhaps by up to a factor of 40, when they depolarise. The practicality of APT depends on whether there is corresponding measurable change in the macroscopic impedance of brain tissue. A mathematical model was used to estimate the magnitude and frequency-dependence of brain impedance changes in two tissues: crustacean peripheral nerve and mammalian cortex. The model predicted that at 30 kHz, a typical working frequency for APT, the resistivity change would be 600 times smaller than at DC, for which the predictions were 3.70/0 for nerve and 0.01% for cortex. Measurements of DC resistivity change during depolarisation of crab nerve (0.2% ~ 2.5%), and afferent stimulation of rabbit cortex (0.01%), were in good agreement with the predicted resistivity changes. Finite element modeling of the head suggested that, with a cosine excitation pattern, the scalp voltage changes would be about 0.001%. Suggestions are given for improving the data collection process to make measurements of these small DC voltage changes more tractable.

This work presents an in vitro platform for studying the effects of physiologically relevant, axonally applied mechanical insult to neurons in culture. Design considerations are presented, and finite element studies are shown to establish proof of concept. The mold for creating the device was microfabricated in a cleanroom for use in the replica molding of the device. The process of fabricating the final device is then detailed. Initial biocompatibility assays are presented, and experiments for characterizing the function of the device are proposed.

Thesis (Ph. D.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 82-86). Also available on the World Wide Web.

Diss. (sammanfattning) Stockholm : Karol. inst. / Härtill 6 uppsatser.