Synthesis Methods of Multiple-valued Structures of Language Systems

The basic construction concepts of many-valued intellectual systems, which are adequate to primal problems of person activity and using hybrid tools with many-valued of coding are considered. The many-valued intellectual systems being two-place, but simulating neuron processes of space toting which are different on a level of actions, inertial and threshold of properties of neurons diaphragms, and also modification of frequency of following of the transmitted messages are created. All enumerated properties and functions in point of fact are essential not only are discrete on time, but also many-valued.

Fourier Neural Networks: An Approach with Sinusoidal Activation Functions

* Supported by INTAS 2000-626, INTAS YSF 03-55-1969, INTAS INNO 182, and TIC 2003-09319-c03-03.

Sensitivity and Bias within the Binary Signal Detection Theory, BSDT

Similar to classic Signal Detection Theory (SDT), recent optimal Binary Signal Detection Theory (BSDT) and based on it Neural Network Assembly Memory Model (NNAMM) can successfully reproduce Receiver Operating Characteristic (ROC) curves although BSDT/NNAMM parameters (intensity of cue and neuron threshold) and classic SDT parameters (perception distance and response bias) are essentially different. In present work BSDT/NNAMM optimal likelihood and posterior probabilities are analytically analyzed and used to generate ROCs and modified (posterior) mROCs, optimal overall likelihood and posterior. It is shown that for the description of basic discrimination experiments in psychophysics within the BSDT a ‘neural space’ can be introduced where sensory stimuli as neural codes are represented and decision processes are defined, the BSDT’s isobias curves can simultaneously be interpreted as universal psychometric functions satisfying the Neyman-Pearson objective, the just noticeable difference (jnd) can be defined and interpreted as an atom of experience, and near-neutral values of biases are observers’ natural choice. The uniformity or no-priming hypotheses, concerning the ‘in-mind’ distribution of false-alarm probabilities during ROC or overall probability estimations, is introduced. The BSDT’s and classic SDT’s sensitivity, bias, their ROC and decision spaces are compared.

The Cascade Orthogonal Neural Network

In the paper new non-conventional growing neural network is proposed. It coincides with the Cascade- Correlation Learning Architecture structurally, but uses ortho-neurons as basic structure units, which can be adjusted using linear tuning procedures. As compared with conventional approximating neural networks proposed approach allows significantly to reduce time required for weight coefficients adjustment and the training dataset size.

Multiple-Valued Structures of Intellectual Systems

The basic construction concepts of many-valued intellectual systems, which are adequate to primal problems of person activity and using hybrid tools with many-valued intellectual systems being two-place, but simulating neuron processes of space toting which are different on a level of actions, inertial and threshold of properties of neuron diaphragms, and also frequency modification of the following transmitted messages are created. All enumerated properties and functions in point of fact are essential not only are discrete on time, but also many-valued.

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units

Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered. It is proposed to treat a spiking neural network in terms of classical automatic control theory apparatus based on the Laplace transform. It is shown that synapse functioning can be easily modeled by a second order damped response unit. Spiking neuron soma is presented as a threshold detection unit. Thus, the proposed fuzzy spiking neural network is an analog-digital nonlinear pulse-position dynamic system. It is demonstrated how fuzzy probabilistic and possibilistic clustering approaches can be implemented on the base of the presented spiking neural network.

Performance Comparison of MATLAB and Neuro Solution Software on Estimation of Fuel Economy by Using Artificial Neural Network

In the world, scientific studies increase day by day and computer programs facilitate the human’s life. Scientists examine the human’s brain’s neural structure and they try to be model in the computer and they give the name of artificial neural network. For this reason, they think to develop more complex problem’s solution. The purpose of this study is to estimate fuel economy of an automobile engine by using artificial neural network (ANN) algorithm. Engine characteristics were simulated by using “Neuro Solution” software. The same data is used in MATLAB to compare the performance of MATLAB is such a problem and show its validity. The cylinder, displacement, power, weight, acceleration and vehicle production year are used as input data and miles per gallon (MPG) are used as target data. An Artificial Neural Network model was developed and 70% of data were used as training data, 15% of data were used as testing data and 15% of data is used as validation data. In creating our model, proper neuron number is carefully selected to increase the speed of the network. Since the problem has a nonlinear structure, multi layer are used in our model.

The Cascade Neo-Fuzzy Architecture and its Online Learning Algorithm

In the paper learning algorithm for adjusting weight coefficients of the Cascade Neo-Fuzzy Neural Network (CNFNN) in sequential mode is introduced. Concerned architecture has the similar structure with the Cascade-Correlation Learning Architecture proposed by S.E. Fahlman and C. Lebiere, but differs from it in type of artificial neurons. CNFNN consists of neo-fuzzy neurons, which can be adjusted using high-speed linear learning procedures. Proposed CNFNN is characterized by high learning rate, low size of learning sample and its operations can be described by fuzzy linguistic “if-then” rules providing “transparency” of received results, as compared with conventional neural networks. Using of online learning algorithm allows to process input data sequentially in real time mode.

Modeling Data for Complications in Diabetics Using Logistic Regression

2010 Mathematics Subject Classification: 62P10.

Stem cell-derived astrocytes:are they physiologically credible?

Astrocytes are now increasingly acknowledged as having fundamental and sophisticated roles in brain function and dysfunction. Unravelling the complex mechanisms that underlie human brain astrocyte-neuron interactions is therefore an essential step on the way to understanding how the brain operates. Insights into astrocyte function to date, have almost exclusively been derived from studies conducted using murine or rodent models. Whilst these have led to significant discoveries, preliminary work with human astrocytes has revealed a hitherto unknown range of astrocyte types with potentially greater functional complexity and increased neuronal interaction with respect to animal astrocytes. It is becoming apparent, therefore, that many important functions of astrocytes will only be discovered by direct physiological interrogation of human astrocytes. Recent advancements in the field of stem cell biology have provided a source of human based models. These will provide a platform to facilitate our understanding of normal astrocyte functions as well as their role in CNS pathology. A number of recent studies have demonstrated that stem cell derived astrocytes exhibit a range of properties, suggesting that they may be functionally equivalent to their in vivo counterparts. Further validation against in vivo models will ultimately confirm the future utility of these stem-cell based approaches in fulfilling the need for human- based cellular models for basic and clinical research. In this review we discuss the roles of astrocytes in the brain and highlight the extent to which human stem cell derived astrocytes have demonstrated functional activities that are equivalent to that observed in vivo.

Survival in the pre-senile dementia frontotemporal lobar degeneration with TDP-43 proteinopathy:effects of genetic, demographic and neuropathological variables

Factors associated with survival were studied in 84 neuropathologically documented cases of the pre-senile dementia frontotemporal dementia lobar degeneration (FTLD) with transactive response (TAR) DNA-binding protein of 43 kDa (TDP-43) proteinopathy (FTLD-TDP). Kaplan-Meier survival analysis estimated mean survival as 7.9 years (range: 1-19 years, SD = 4.64). Familial and sporadic cases exhibited similar survival, including progranulin (GRN) gene mutation cases. No significant differences in survival were associated with sex, disease onset, Braak disease stage, or disease subtype, but higher survival was associated with lower post-mortem brain weight. Survival was significantly reduced in cases with associated motor neuron disease (FTLD-MND) but increased with Alzheimer's disease (AD) or hippocampal sclerosis (HS) co-morbidity. Cox regression analysis suggested that reduced survival was associated with increased densities of neuronal cytoplasmic inclusions (NCI) while increased survival was associated with greater densities of enlarged neurons (EN) in the frontal and temporal lobes. The data suggest that: (1) survival in FTLD-TDP is more prolonged than typical in pre-senile dementia but shorter than some clinical subtypes such as the semantic variant of primary progressive aphasia (svPPA), (2) MND co-morbidity predicts poor survival, and (3) NCI may develop early and EN later in the disease. The data have implications for both neuropathological characterization and subtyping of FTLD-TDP.

Characterization of a Novel Mouse Phenotype with Marked Hydrocephalus

In multigenic diseases, disorders where mutations in multiple genes affect the expressivity of the disease, genetic interactions play a major role in prevalence and phenotypic severity. While studying the genetic interactions between Pax3 and EdnrB in the melanocyte lineage, a new phenotype was noted in 80% of Pax3 mutants that we believe to be a novel murine model for hydrocephalus. Hydrocephalus, an accumulation of cerebrospinal fluid in the cranial cavity due to obstruction of flow in and out of the cavity, is one of the most common birth defects surpassing Down syndrome. Characteristic to hydrocephalus is a "domed" head appearance, expansion of the ventricles of the brain, and loss of neurons with hyperproliferation of glial cell types all three of which were seen in the mutant mice. The phenotype also consisted of craniofacial deformities coupled with skeletal defects including, but not limited to kyphosis, lordosis, and an apparent shortening of the some limbs. For the cellular analysis of the hydrocephalus phenotype, brains were removed and stained with two antibodies: Glial Fibrillary Acidic Protein (GFAP) and Neurofilament (NF), which are astrocyte- and neuron- specific respectively. A higher number of cells expressing GF AP and a lower number of cells expressing NF were seen in the mutant brain, when compared to control. For skeletal deformity analysis, affected mice skeletons were stained with Alizarin Red and Alcian Blue showing no apparent difference in ossification. Future genetic analysis of these mutant mice has the potential to identify novel gene modifiers involved in the promotion of this particular phenotype.

Transgenic Expression of Endothelin Receptor-B Rescues the Aganglionosis Phenotype of Piebald Lethal Mice

Neural crest cells (NCC) are a unique population of cells in vertebrates that arise between the presumptive epidermis and the dorsal most region of the neural tube. During neurulation, NCC migrate to many regions of the body to give rise to a wide variety of cell types. NCC that originate from the neural tube at the levels of somite 1-7 colonize the gut and give rise to the enteric ganglia. The endothelin signaling pathway has been shown to be crucial for proper development of some neural crest derivatives. Mice and humans with mutations in the Endothelin receptor b (Ednrb) gene exhibit similar phenotypes characterized by hypopigmentation, hearing loss, and megacolon. Thesephenotypes are due to lack of melanocytes in the skin, inner ear and enteric ganglia in the distal portion of the colon, respectively. It is well established that Ednrb is required early during the embryonic development for normal innervation of the gut. However, it is not clear if Ednrb acts on enteric neuron precursor cells or in pre-committed NC precursors. Additionally, it is controversial whether the action of Ednrb is cell autonomous or non- autonomous. We generated transgenic mice that express Ednrb under the control of the Nestin second intron enhancer (Nes) which drives expression to pre-migrating NCC. These mice were crosses to the spontaneous mouse mutant piebald lethal, which carriers a null mutation in Ednrb and exhibits enteric aganglionosis. The Nes-Ednrb was capable of rescuing the aganglianosis phenotype of piebald lethal mutants demonstrating that expression of Ednrb in pre-committed precursors is sufficient for normal enteric ganglia development. This study provides insight in early embryonic development of NCC and could eventually have potential use in cellular therapies for Hirschsprung's disease.

Fiber Scaffolds of Poly (glycerol-dodecanedioate) and its Derivative via Electrospinning for Neural Tissue Engineering

Peripheral nerves have demonstrated the ability to bridge gaps of up to 6 mm. Peripheral Nerve System injury sites beyond this range need autograft or allograft surgery. Central Nerve System cells do not allow spontaneous regeneration due to the intrinsic environmental inhibition. Although stem cell therapy seems to be a promising approach towards nerve repair, it is essential to use the distinct three-dimensional architecture of a cell scaffold with proper biomolecule embedding in order to ensure that the local environment can be controlled well enough for growth and survival. Many approaches have been developed for the fabrication of 3D scaffolds, and more recently, fiber-based scaffolds produced via the electrospinning have been garnering increasing interest, as it offers the opportunity for control over fiber composition, as well as fiber mesh porosity using a relatively simple experimental setup. All these attributes make electrospun fibers a new class of promising scaffolds for neural tissue engineering. Therefore, the purpose of this doctoral study is to investigate the use of the novel material PGD and its derivative PGDF for obtaining fiber scaffolds using the electrospinning. The performance of these scaffolds, combined with neural lineage cells derived from ESCs, was evaluated by the dissolvability test, Raman spectroscopy, cell viability assay, real time PCR, Immunocytochemistry, extracellular electrophysiology, etc. The newly designed collector makes it possible to easily obtain fibers with adequate length and integrity. The utilization of a solvent like ethanol and water for electrospinning of fibrous scaffolds provides a potentially less toxic and more biocompatible fabrication method. Cell viability testing demonstrated that the addition of gelatin leads to significant improvement of cell proliferation on the scaffolds. Both real time PCR and Immunocytochemistry analysis indicated that motor neuron differentiation was achieved through the high motor neuron gene expression using the metabolites approach. The addition of Fumaric acid into fiber scaffolds further promoted the differentiation. Based on the results, this newly fabricated electrospun fiber scaffold, combined with neural lineage cells, provides a potential alternate strategy for nerve injury repair.

Modelo comportamental do capacitor ferroelétrico como unidade básica de neurônios artificiais e sua implementação em FPGA

This work proposes the use of the behavioral model of the hysteresis loop of the ferroelectrics capacitor as a new alternative to the usually costly techniques in the computation of nonlinear functions in artificial neurons implemented on reconfigurable hardware platform, in this case, a FPGA device. Initially the proposal has been validated by the implementation of the boolean logic through the digital models of two artificial neurons: the Perceptron and a variation of the model Integrate and Fire Spiking Neuron, both using the model also digital of the hysteresis loop of the ferroelectric capacitor as it’s basic nonlinear unit for the calculations of the neurons outputs. Finally, it has been used the analog model of the ferroelectric capacitor with the goal of verifying it’s effectiveness and possibly the reduction of the number of necessary logic elements in the case of implementing the artificial neurons on integrated circuit. The implementations has been carried out by Simulink models and the synthesizing has been done through the DSP Builder software from Altera Corporation.