A Functional Architecture Approach to Neural Systems

The technology for the design of systems to perform extremely complex combinations of real-time functionality has developed over a long period. This technology is based on the use of a hardware architecture with a physical separation into memory and processing, and a software architecture which divides functionality into a disciplined hierarchy of software components which exchange unambiguous information. This technology experiences difficulty in design of systems to perform parallel processing, and extreme difficulty in design of systems which can heuristically change their own functionality. These limitations derive from the approach to information exchange between functional components. A design approach in which functional components can exchange ambiguous information leads to systems with the recommendation architecture which are less subject to these limitations. Biological brains have been constrained by natural pressures to adopt functional architectures with this different information exchange approach. Neural networks have not made a complete shift to use of ambiguous information, and do not address adequate management of context for ambiguous information exchange between modules. As a result such networks cannot be scaled to complex functionality. Simulations of systems with the recommendation architecture demonstrate the capability to heuristically organize to perform complex functionality

Improving the efficiency of streaming video processing through the use of serverless technologies

An architecture with classical dedicated servers for video analytics is considered, and architecture using serverless technologies for video analysis with more than one video streaming source is proposed. A comparison of two architectural approaches is made, and for the revealed shortcomings the architecture with the use of serverless functions is offered

An examination and analysis of the Boltzmann machine, its mean field theory approximation, and learning algorithm

It is currently believed that artificial neural network models may form the basis for inte1ligent computational devices. The Boltzmann Machine belongs to the class of recursive artificial neural networks and uses a supervised learning algorithm to learn the mapping between input vectors and desired outputs. This study examines the parameters that influence the performance of the Boltzmann Machine learning algorithm. Improving the performance of the algorithm through the use of a naïve mean field theory approximation is also examined. The study was initiated to examine the hypothesis that the Boltzmann Machine learning algorithm, when used with the mean field approximation, is an efficient, reliable, and flexible model of machine learning. An empirical analysis of the performance of the algorithm supports this hypothesis. The performance of the algorithm is investigated by applying it to training the Boltzmann Machine, and its mean field approximation, the exclusive-Or function. Simulation results suggest that the mean field theory approximation learns faster than the Boltzmann Machine, and shows better stability. The size of the network and the learning rate were found to have considerable impact upon the performance of the algorithm, especially in the case of the mean field theory approximation. A comparison is made with the feed forward back propagation paradigm and it is found that the back propagation network learns the exclusive-Or function eight times faster than the mean field approximation. However, the mean field approximation demonstrated better reliability and stability. Because the mean field approximation is local and asynchronous it has an advantage over back propagation with regard to a parallel implementation. The mean field approximation is domain independent and structurally flexible. These features make the network suitable for use with a structural adaption algorithm, allowing the network to modify its architecture in response to the external environment

Impact of alife simulation of Darwinian and Lamarckian evolutionary theories

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementUntil nowadays, the scientific community firmly rejected the Theory of Inheritance of Acquired Characteristics, a theory mostly associated with the name of Jean-Baptiste Lamarck (1774-1829). Though largely dismissed when applied to biological organisms, this theory found its place in a young discipline called Artificial Life. Based on the two abstract models of Darwinian and Lamarckian evolutionary theories built using neural networks and genetic algorithms, this research aims to present a notion of the potential impact of implementation of Lamarckian knowledge inheritance across disciplines. In order to obtain our results, we conducted a focus group discussion between experts in biology, computer science and philosophy, and used their opinions as qualitative data in our research. As a result of completing the above procedure, we have found some implications of such implementation in each mentioned discipline. In synthetic biology, this means that we would engineer organisms precisely up to our specific needs. At the moment, we can think of better drugs, greener fuels and dramatic changes in chemical industry. In computer science, Lamarckian evolutionary algorithms have been used for quite some years, and quite successfully. However, their application in strong ALife can only be approximated based on the existing roadmaps of futurists. In philosophy, creating artificial life seems consistent with nature and even God, if there is one. At the same time, this implementation may contradict the concept of free will, which is defined as the capacity for an agent to make choices in which the outcome has not been determined by past events. This study has certain limitations, which means that larger focus group and more prepared participants would provide more precise results