4-Transitivity and 6-figures in some Moufang-Klingenberg planes

In this paper, the Moufang-Klingenberg plane over a local alternative ring R of dual numbers is studied. It is shown that its collineation group is transitive on quadrangles. It is therefore shown that the coordinatization of these Moufang-Klingenberg planes is independent of the choice of the coordinatization quadrangle. Also, the concept of 6-figures is extended to these Moufang-Klingenberg planes and it is shown that any 6-figure corresponds to only one inversible m ∈ R.     

Circadian synchrony in networks of protein rhythm driven neurons

The interaction between gene activation and cellular activity has recently emerged as a critical aspect of brain behavior, but the dynamics of networks incorporating these interactions are poorly understood. An interesting phenomena arises when the genetic activation oscillates endogenously and a network of such cells synchronize to a coherent rhythm, such as is the case with the suprachiasmatic nucleus. To explain this synchronization, we propose a model in which a mRNA/protein expression cycle drives neurons electrical activity, and synaptic activation shifts the phase of the protein rhythm. Using lattice networks, we demonstrate that these interactions are sufficient to generate coherent oscillation. © 2006 Wiley Periodicals, Inc. Complexity 12: 67–72, 2006     

Turing computability with neural nets

This paper shows the existence of a finite neural network, made up of sigmoidal neurons, which simulates a universal Turing machine. It is composed of less than 10⁵ synchronously evolving processors, interconnected linearly. High-order connections are not required.     

Computation in gene networks

Genetic regulatory networks have the complex task of controlling all aspects of life. Using a model of gene expression by piecewise linear differential equations we show that this process can be considered as a process of computation. This is demonstrated by showing that this model can simulate memory bounded Turing machines. The simulation is robust with respect to perturbations of the system, an important property for both analog computers and biological systems. Robustness is achieved using a condition that ensures that the model equations, that are generally chaotic, follow a predictable dynamics.     

Isotope dilution analysis in column-coupling isotachophoresis

The use of isotope dilution technique to eliminate problems associated with demands for a high load capacity in column-coupling capillary isotachophoresis was studied. Determinations of phosphate present in a model mixture and in white wine served for the evaluation of this approach to quantitative analysis in isotachophoresis. A high selectivity of the analysis with acceptable accuracy and precision of the determination also in complex ionic mixtures in a short time are obvious advantages of this approach.     

Synthesis,Spectroscopic Studies and Structure of 2-[(Benzo[<Emphasis Type="Italic">d</Emphasis>]thiazol-2-ylamino)methyl]phenol

Abstract  

Schiff base (E)-2-[(benzo[d]thiazol-2-ylimino)methyl]phenol (1) has been synthesized from the reaction of 2-hydroxy-benzaldehyde with 2-aminobenzothiazole. The 2-[(benzo[d]thiazol-2-ylamino)methyl]phenol (2) was prepared reduction of the Schiff base 1 with sodium borohydride. The compounds 1 and 2 have been characterized by elemental analysis, FT-IR, ¹H-NMR, ¹³C-NMR and UV–visible spectroscopic techniques. The structure of the compound 2 has also been examined crystallographically. The compound 2 crystallizes in the monoclinic space group P2/c. The unit cell parameters were found as a = 10.017(1), b = 11.725(1), c = 10.341(1) ?, V = 1208.1(1) ?³, D _x = 1.409 g cm⁻³ and Z = 4. The crystal structure was solved by direct methods and refined by the full-matrix least squares method and found as R ₁ = 0.0308 and wR ₂ = 0.0818 for 2032 for the observed reflections [I > 2σ(I)].     

Chaotic Dynamics in an Electronic Model of a Genetic Network

We consider dynamics in a class of piecewise-linear ordinary differential equations and in an electronic circuit that model genetic networks. In these models, gene activity varies continuously in time. However, as in Boolean or discrete-time switching networks, gene activity is driven high or low based only on whether the activities of the regulating genes are high or low (i.e., above or below certain thresholds). Depending on the “regulatory logic”, these models can exhibit simple dynamics, like stable fixed points or oscillation, or chaotic dynamics. The observed qualitative and quantitative differences between the dynamics in the idealized equations and the dynamics in the electronic circuit lead us to focus attention on the analysis of the dynamics as a function of parameter values. We propose new techniques for solving the inverse problem – the problem of inferring the regulatory logic and parameters from time series data. We also give new symbolic and statistical methods for characterizing dynamics in these networks.     

Zero infrared reflectance anomaly in doped silicon lamellar gratings. I. From antireflection to total absorption

Zero-reflectance phenomenon for a binary lamellar grating on n-Si substrate irradiated by normally incident TE polarized plane electromagnetic wave of wavelength 10.6 μm is studied. The treatment is performed in the strong diffraction regime, where the structural dimensions and the wavelength are of the same order of magnitude, using data on the IR dielectric function of bulk doped silicon and a version of rigorous coupled-wave analysis. The evolution of normal reflectance zeros with increasing electron concentration from dielecric to metallic-like n-Si is traced. It is shown that the groove height undergoes sharp increase and the period shrinks when plasma wavelength becomes equal to the radiation wavelength. This marks the transition from the antireflection to the total absorption regime where most of incident power is absorbed in the grating region. The cavity-resonance origin of total absorption and satellite peaks in the spectral response are discussed.     

Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference

One of the brain's most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic conditions, and then use these learned weightings to accurately infer information about the world. We suggest that the brain is, in fact, fully capable of computing this parallel task in a single network and describe a neural inspired circuit with this property. Our implementation suggests the possibility that evidence learning requires a more complex organization of the network than was previously assumed, where neurons have different specialties, whose emergence brings the desired adaptivity seen in human online inference.     

Analog-symbolic memory that tracks via reconsolidation

A fundamental part of a computational system is its memory, which is used to store and retrieve data. Classical computer memories rely on the static approach and are very different from human memories. Neural network memories are based on auto-associative attractor dynamics and thus provide a high level of pattern completion. However, they are not used in general computation since there are practically no algorithms to load an arbitrary landscape of attractors into them. In this sense neural network memory models cannot communicate well with symbolic and prior knowledge.We propose the design of a new memory based on localist attractor dynamics with reconsolidation called Reconsolidation Attractor Network (RAN). RAN combines symbolic and subsymbolic features in a very attractive way: it is based on attractors; enables pattern classification under missing data; and demonstrates dynamic reconsolidation, which is very useful for tracking changing concepts. The perception RAN enables is somewhat reminiscent of human perception due to its context sensitivity. Furthermore, it enables an immediate and clear interface with symbolic memories, including loading of attractors by means of trivial wiring, updating attractors, and retrieving them faster without waiting for full convergence. It also scales to any number of concepts. This provides a useful counterpoint to more conventional memory systems, such as random access memory and auto-associative neural networks.