Neural Networks With Asynchronous Control.

Neural network studies have previously focused on monolithic structures. The brain has a bicameral nature, however, and so it is natural to expect that bicameral structures will perform better. This dissertation offers an approach to the development of such bicameral structures. The companion neural structure takes advantage of the global and subset characteristics of the stored memories. Specifically we propose the use of an asynchronous controller C that implies the following update of a probe vector x by the connection matrix T: x\sp\prime = sgn (C(x, TX)). For a VLSI-implemented neural network the controller block can be easily placed in the feedback loop. In a network running asynchronously, the updating of the probe generally offers a choice among several components. If the right components are not updated the network may converge to an incorrect stable point. The proposed asynchronous controller together with the basic neural net forms a bicameral network that can be programmed in various ways to exploit global and local characteristics of stored memory. Several methods to do this are proposed. In one of the methods the update choices are based on bit frequencies. In another method handles are appended to the memories to improve retrieval. The new methods have been analyzed and their performance studies it is shown that there is a marked improvement in performance. This is illustrated by means of simulations. The use of an asynchronous controller allows the implementation of conditional rules that occur frequently in AI applications. It is shown that a neural network that uses conditional rules can solve problems in natural language understanding. The introduction of the asynchronous controller may be viewed as a first step in the development of truly bicameral structures that may be seen as the next generation of neural computers

Analog Photonics Computing for Information Processing, Inference and Optimisation

This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

Theory of Brain Function, Quantum Mechanics and Superstrings

Recent developments/efforts to understand aspects of the brain function at the {\em sub-neural} level are discussed. MicroTubules (MTs) participate in a wide variety of dynamical processes in the cell, especially in bioinformation processes such as learning and memory, by possessing a well-known binary error-correcting code with 64 words. In fact, MTs and DNA/RNA are unique cell structures that possess a code system. It seems that the MTs' code system is strongly related to a kind of ``Mental Code" in the following sense. The MTs' periodic paracrystalline structure make them able to support a superposition of coherent quantum states, as it has been recently conjectured by Hameroff and Penrose, representing an external or mental order, for sufficient time needed for efficient quantum computing. Then the quantum superposition collapses spontaneously/dynamically through a new, string-derived mechanism for collapse proposed recently by Ellis, Mavromatos, and myself. At the moment of collapse, organized quantum exocytosis occurs, and this is how a ``{\em mental order}" may be translated into a ``{\em physiological action}". Our equation for quantum collapse, tailored to the MT system, predicts that it takes 10,000 neurons ${\cal O}(1\,{\rm sec})$ to dynamically collapse (process and imprint information). Different observations/experiments and various schools of thought are in agreement with the above numbers concerning ``{\em conscious events}". If indeed MTs, may be considered as the {\em microsites of consciousness}, then several unexplained properties of consciousness/awareness, get easily explained, including ``{\em backward masking}", ``{\em referal backwards in time}". The {\em non-locality} in the cerebral cortex of neurons related to particular missions, and the related {\em unitary sense of self} as well asComment: 72 pages, 1 figure (uuencoded

A Theory of Cortical Neural Processing.

This dissertation puts forth an original theory of cortical neural processing that is unique in its view of the interplay of chaotic and stable oscillatory neurodynamics and is meant to stimulate new ideas in artificial neural network modeling. Our theory is the first to suggest two new purposes for chaotic neurodynamics: (i) as a natural means of representing the uncertainty in the outcome of performed tasks, such as memory retrieval or classification, and (ii) as an automatic way of producing an economic representation of distributed information. We developed new models, to better understand how the cerebral cortex processes information, which led to our theory. Common to these models is a neuron interaction function that alternates between excitatory and inhibitory neighborhoods. Our theory allows characteristics of the input environment to influence the structural development of the cortex. We view low intensity chaotic activity as the a priori uncertain base condition of the cortex, resulting from the interaction of a multitude of stronger potential responses. Data, distinguishing one response from many others, drives bifurcations back toward the direction of less complex (stable) behavior. Stability appears as temporary bubble-like clusters within the boundaries of cortical columns and begins to propagate through frequency sensitive and non-specific neurons. But this is limited by destabilizing long-path connections. An original model of the post-natal development of ocular dominance columns in the striate cortex is presented and compared to autoradiographic images from the literature with good matching results. Finally, experiments are shown to favor computed update order over traditional approaches for better performance of the pattern completion process

An analytical study on image databases

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.Includes bibliographical references (leaves 87-88).by Francine Ming Fang.M.Eng

Categorical Ontology of Complex Systems, Meta-Systems and Theory of Levels: The Emergence of Life, Human Consciousness and Society

Single cell interactomics in simpler organisms, as well as somatic cell interactomics in multicellular organisms, involve biomolecular interactions in complex signalling pathways that were recently represented in modular terms by quantum automata with ‘reversible behavior’ representing normal cell cycling and division. Other implications of such quantum automata, modular modeling of signaling pathways and cell differentiation during development are in the fields of neural plasticity and brain development leading to quantum-weave dynamic patterns and specific molecular processes underlying extensive memory, learning, anticipation mechanisms and the emergence of human consciousness during the early brain development in children. Cell interactomics is here represented for the first time as a mixture of ‘classical’ states that determine molecular dynamics subject to Boltzmann statistics and ‘steady-state’, metabolic (multi-stable) manifolds, together with ‘configuration’ spaces of metastable quantum states emerging from complex quantum dynamics of interacting networks of biomolecules, such as proteins and nucleic acids that are now collectively defined as quantum interactomics. On the other hand, the time dependent evolution over several generations of cancer cells --that are generally known to undergo frequent and extensive genetic mutations and, indeed, suffer genomic transformations at the chromosome level (such as extensive chromosomal aberrations found in many colon cancers)-- cannot be correctly represented in the ‘standard’ terms of quantum automaton modules, as the normal somatic cells can. This significant difference at the cancer cell genomic level is therefore reflected in major changes in cancer cell interactomics often from one cancer cell ‘cycle’ to the next, and thus it requires substantial changes in the modeling strategies, mathematical tools and experimental designs aimed at understanding cancer mechanisms. Novel solutions to this important problem in carcinogenesis are proposed and experimental validation procedures are suggested. From a medical research and clinical standpoint, this approach has important consequences for addressing and preventing the development of cancer resistance to medical therapy in ongoing clinical trials involving stage III cancer patients, as well as improving the designs of future clinical trials for cancer treatments.\ud \ud \ud KEYWORDS: Emergence of Life and Human Consciousness;\ud Proteomics; Artificial Intelligence; Complex Systems Dynamics; Quantum Automata models and Quantum Interactomics; quantum-weave dynamic patterns underlying human consciousness; specific molecular processes underlying extensive memory, learning, anticipation mechanisms and human consciousness; emergence of human consciousness during the early brain development in children; Cancer cell ‘cycling’; interacting networks of proteins and nucleic acids; genetic mutations and chromosomal aberrations in cancers, such as colon cancer; development of cancer resistance to therapy; ongoing clinical trials involving stage III cancer patients’ possible improvements of the designs for future clinical trials and cancer treatments. \ud \u

Kronecker Product of Tensors and Hypergraphs: Structure and Dynamics

Hypergraphs and graph products extend traditional graph theory by incorporating multi-way and coupled relationships, which are ubiquitous in real-world systems. While the Kronecker product, rooted in matrix analysis, has become a powerful tool in network science, its application has been limited to pairwise networks. In this paper, we extend the coupling of graph products to hypergraphs, enabling a system-theoretic analysis of network compositions formed via the Kronecker product of hypergraphs. We first extend the notion of the matrix Kronecker product to the tensor Kronecker product from the perspective of tensor blocks. We present various algebraic and spectral properties and express different tensor decompositions with the tensor Kronecker product. Furthermore, we study the structure and dynamics of Kronecker hypergraphs based on the tensor Kronecker product. We establish conditions that enable the analysis of the trajectory and stability of a hypergraph dynamical system by examining the dynamics of its factor hypergraphs. Finally, we demonstrate the numerical advantage of this framework for computing various tensor decompositions and spectral properties.Comment: 29 pages, 4 figures, 2 table

Proceedings of AUTOMATA 2010: 16th International workshop on cellular automata and discrete complex systems

International audienceThese local proceedings hold the papers of two catgeories: (a) Short, non-reviewed papers (b) Full paper