Networks of neurons, which form central pattern generators (CPGs), are important for controlling animal behaviors. Of special interest are configurations or CPG motifs composed of reciprocally inhibited neurons, such as half-center oscillators (HCOs). Bursting rhythms of HCOs are shown to include stable synchrony or in-phase bursting. This in-phase bursting can co-exist with anti-phase bursting, commonly expected as the single stable state in HCOs that are connected with fast non-delayed synapses. The finding contrasts with the classical view that reciprocal inhibition has to be slow or time-delayed to synchronize such bursting neurons. Phase-locked rhythms are analyzed via Lyapunov exponents estimated with variational equations, and through the convergence rates estimated with Poincar\'e return maps. A new mechanism underlying multistability is proposed that is based on the spike interactions, which confer a dual property on the fast non-delayed reciprocal inhibition; this reveals the role of spikes in generating multiple co-existing phase-locked rhythms. In particular, it demonstrates that the number and temporal characteristics of spikes determine the number and stability of the multiple phase-locked states in weakly coupled HCOs. The generality of the multistability phenomenon is demonstrated by analyzing diverse models of bursting networks with various inhibitory synapses; the individual cell models include the reduced leech heart interneuron, the Sherman model for pancreatic beta cells, the Purkinje neuron model and Fitzhugh-Rinzel phenomenological model. Finally, hypothetical and experiment-based CPGs composed of HCOs are investigated. This study is relevant for various applications that use CPGs such as robotics, prosthetics, and artificial intelligence.

We consider three problems in this dissertation, all under the unifying theme of random matrix products. The first and second problems are concerned with weak convergence in stochastic matrices and circulant matrices, respectively, and the third is concerned with the numerical calculation of the Lyapunov exponent associated with some random Fibonacci sequences. Stochastic matrices are nonnegative matrices whose row sums are all equal to 1. They are most commonly encountered as transition matrices of Markov chains. Circulant matrices, on the other hand, are matrices where each row after the first is just the previous row cyclically shifted to the right by one position. Like stochastic matrices, circulant matrices are ubiquitous in the literature.In the first problem, we study the weak convergence of the convolution sequence mu to the n, where mu is a probability measure with support S sub mu inside the space S of d by d stochastic matrices, d greater than or equal to 3. Note that mu to the n is precisely the distribution of the product X sub 1 times X sub 2 times and so on times X sub n of the mu distributed independent random variables X sub 1, X sub 2, and so on, X sub n taking values in S. In [CR] Santanu Chakraborty and B.V. Rao introduced a cyclicity condition on S sub mu and showed that this condition is necessary and sufficient for mu to the n to not converge weakly when d is equal to 3 and the minimal rank r of the matrices in the closed semigroup S generated by S sub mu is 2. Here, we extend this result to any d bigger than 3. Moreover, we show that when the minimal rank r is not 2, this result does not always hold.The second problem is an investigation of weak convergence in another direction, namely the case when the probability measure mu's support S sub mu consists of d by d circulant matrices, d greater than or equal to 3, which are not necessarily nonnegative. The resulting semigroup S generated by S sub mu now lacking the nice property of compactness in the case of stochastic matrices, we assume tightness of the sequence mu to the n to analyze the problem. Our approach is based on the work of Mukherjea and his collaborators, who in [LM] and [DM] presented a method based on a bookkeeping of the possible structure of the compact kernel K of S.The third problem considered in this dissertation is the numerical determination of Lyapunov exponents of some random Fibonacci sequences, which are stochastic versions of the classical Fibonacci sequence f sub (n plus 1) equals f sub n plus f sub (n minus 1), n greater than or equal to 1, and f sub 0 equal f sub 1 equals 1, obtained by randomizing one or both signs on the right side of the defining equation and or adding a "growth parameter." These sequences may be viewed as coming from a sequence of products of i.i.d. random matrices and their rate of growth measured by the associated Lyapunov exponent. Following techniques presented by Embree and Trefethen in their numerical paper [ET], we study the behavior of the Lyapunov exponents as a function of the probability p of choosing plus in the sign randomization.

published_or_final_version / abstract / Electrical and Electronic Engineering / Master / Master of Philosophy

We investigate how the formation of heterogeneity and structures in flows through explicit porous microstructures depends upon the geometric and topological observables of the porous medium. Using direct numerical simulations of single-phase, isothermal, laminar fluid flow through realistic three-dimensional stochastically generated pore structures, hereafter referred to as pore spaces, the characteristics of the resulting steady state velocity fields are related to physical characteristics of the pore spaces. The results suggest that the spatially variable resistance offered by the geometry and topology of the pore space induces a highly heterogeneous fluid velocity field therein. Focus is placed on three different length scales: macroscopic (cm), mesoscopic (mm), and microscopic (microns). At the macroscopic length scale, volume averaging is used to relate porosity, mean hydraulic radius, and their product to the permeability of the pore space. At the mesoscopic scale, the effect of a medium's porosity on fluid particle trajectory attributes, such as passage time and tortuosity, is studied. At the final length scale, that of the microscopic in-pore fluid dynamics, finite time Lyapunov exponents are used to determine expanding, contracting, and hyperbolic regions in the flow field, which are then related to the local structure of the pore space. The results have implications to contaminant transport, mixing, and how chemical reactions are induced at the pore-scale. A description of the adopted numerical methods to simulate flow and generate the pore space are provided as well.

Thesis (Ph.D.)--McMaster University (Canada), 1996. / Source: Dissertation Abstracts International, Volume: 57-10, Section: B, page: 6457. Adviser: S. Haykin.

Thesis (Honors)--College of William and Mary, 2009. / Includes bibliographical references (leaves 56-57). Also available via the World Wide Web.

Thesis (M. Phil.)--University of Hong Kong, 2006. / Title proper from title frame. Also available in printed format.

Nas últimas décadas uma considerável parcela das pesquisas nas áreas de Física e Matemática tem sido dedicada ao estudo de fenômenos não lineares. Uma possível explicação para isso foi o rápido desenvolvimento de sistemas computacionais, tanto em nível de hardware quanta em nível de software, algoritmos e técnicas de programação que propiciaram ao homem maiores facilidades no tratamento de sistemas não lineares, o que levou a um maior grau de entendimento de sua complexidade. Geralmente, aos sistemas não lineares esta associada uma geometria irregular, onde comum o aparecimento de regimes caóticos, com um conjunto atrator de órbitas cuja dimensão não é um inteiro positivo, mas sim um número real positivo. Por esta razão, tais atratores, são denominados estranhos e ditos possuírem uma geometria fractal. É possível, através de métodos cuidadosamente desenvolvidos, estimar-se as dimensões associadas à dinâmica de séries temporais. Uma das séries de maior dificuldade de análise através do computador, e de particular interesse na medicina, são as séries de sons pulmonares humanos. Desde quando o estetoscópio foi inventado até os dias de hoje não há uma ferramenta plenamente confiável para a análise destas séries. Recentemente, temos trabalhado com estas séries e verificamos que há uma geometria fractal. Esta tese propõe a utilização da análise não-linear para identificação de padrões sonoros. Além da geometria fractal, a análise por wavelets tem sido utilizada no estudo de sinais complexos, sobretudo naqueles que apresentam estruturas fractais. O conjunto de filtros construído através da translação, expansão ou compressão de uma função wavelet mãe tem uma estrutura auto-similar, mostrando-se particularmente apropriado para a verificação da auto similaridade dos sons. A técnica da estimativa dos expoentes de Lyapunov dependente do tempo, a qual e desenvolvida na tese, tem se mostrado bastante adequada para identificação de padrões sonoros de origem pulmonar. / It has been observed that in the last decades, considerable amount of the research in the areas of Physics and Mathematics have been dedicated to the study of nonlinear phenomena. A possible explanation for this fact is the fast development of computational systems occurring in the level of the hardware as in computer languages, algorithms and programming techniques. These developments propitiated to the researchers a broader contact with nonlinear systems, which led to a better understanding of their complexity. In general, for nonlinear systems an irregular geometry is associated, where the appearance of chaotic regimes has an associated attractor set of orbits whose dimension is not a positive integer number, but a real one. Such attractors are called strange and said to possess fractal geometry. It is possible, through carefully developed methods, to estimate the dimension associated to the dynamics of time series. One of the series with high difficulty to be analyzed through a computer and of particular interest in medicine, is the time series generated out of human pulmonary sounds. Since the creation of the stethoscope, there is not yet a fully trustworthy tool for the lung sound analysis. Recently, we have studied these series and verified that they have a fractal geometry nature. The purpose of this thesis is to investigate non-linear analysis as a tool for pattern recognition in lung sounds. In addition to fractal geometry, the wavelet analysis has been used in the study of complex signs, in particular for those presenting a fractal structure. The set of filters constructed through the translation, expansion or compression of a function wavelet mother has an auto-similar structure, being particularly useful for the verification of self similarity of pulmonary sounds. The largest time dependent Lyapunov exponent estimation technique that has been proposed in this thesis has shown a high degree of confidence for the identification of lung sound patterns.

Nas últimas décadas uma considerável parcela das pesquisas nas áreas de Física e Matemática tem sido dedicada ao estudo de fenômenos não lineares. Uma possível explicação para isso foi o rápido desenvolvimento de sistemas computacionais, tanto em nível de hardware quanta em nível de software, algoritmos e técnicas de programação que propiciaram ao homem maiores facilidades no tratamento de sistemas não lineares, o que levou a um maior grau de entendimento de sua complexidade. Geralmente, aos sistemas não lineares esta associada uma geometria irregular, onde comum o aparecimento de regimes caóticos, com um conjunto atrator de órbitas cuja dimensão não é um inteiro positivo, mas sim um número real positivo. Por esta razão, tais atratores, são denominados estranhos e ditos possuírem uma geometria fractal. É possível, através de métodos cuidadosamente desenvolvidos, estimar-se as dimensões associadas à dinâmica de séries temporais. Uma das séries de maior dificuldade de análise através do computador, e de particular interesse na medicina, são as séries de sons pulmonares humanos. Desde quando o estetoscópio foi inventado até os dias de hoje não há uma ferramenta plenamente confiável para a análise destas séries. Recentemente, temos trabalhado com estas séries e verificamos que há uma geometria fractal. Esta tese propõe a utilização da análise não-linear para identificação de padrões sonoros. Além da geometria fractal, a análise por wavelets tem sido utilizada no estudo de sinais complexos, sobretudo naqueles que apresentam estruturas fractais. O conjunto de filtros construído através da translação, expansão ou compressão de uma função wavelet mãe tem uma estrutura auto-similar, mostrando-se particularmente apropriado para a verificação da auto similaridade dos sons. A técnica da estimativa dos expoentes de Lyapunov dependente do tempo, a qual e desenvolvida na tese, tem se mostrado bastante adequada para identificação de padrões sonoros de origem pulmonar. / It has been observed that in the last decades, considerable amount of the research in the areas of Physics and Mathematics have been dedicated to the study of nonlinear phenomena. A possible explanation for this fact is the fast development of computational systems occurring in the level of the hardware as in computer languages, algorithms and programming techniques. These developments propitiated to the researchers a broader contact with nonlinear systems, which led to a better understanding of their complexity. In general, for nonlinear systems an irregular geometry is associated, where the appearance of chaotic regimes has an associated attractor set of orbits whose dimension is not a positive integer number, but a real one. Such attractors are called strange and said to possess fractal geometry. It is possible, through carefully developed methods, to estimate the dimension associated to the dynamics of time series. One of the series with high difficulty to be analyzed through a computer and of particular interest in medicine, is the time series generated out of human pulmonary sounds. Since the creation of the stethoscope, there is not yet a fully trustworthy tool for the lung sound analysis. Recently, we have studied these series and verified that they have a fractal geometry nature. The purpose of this thesis is to investigate non-linear analysis as a tool for pattern recognition in lung sounds. In addition to fractal geometry, the wavelet analysis has been used in the study of complex signs, in particular for those presenting a fractal structure. The set of filters constructed through the translation, expansion or compression of a function wavelet mother has an auto-similar structure, being particularly useful for the verification of self similarity of pulmonary sounds. The largest time dependent Lyapunov exponent estimation technique that has been proposed in this thesis has shown a high degree of confidence for the identification of lung sound patterns.

Made available in DSpace on 2016-12-12T20:15:48Z (GMT). No. of bitstreams: 1 capa_ate_sumario.pdf: 940719 bytes, checksum: 34845651ded8147831931a5314e46c27 (MD5) Previous issue date: 2012-02-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In this work, we numerically studied a four-dimensional Chua circuit model through bifurcation diagrams and parameter spaces. Our main objective here is to ex-tend the studies already realized in this system, showing a wider range of its behavior. For this purpose, we constructed the parameter spaces using the Lyapunov exponents spectrum through color scales, varying simultaneously two parameters of the system. With this procedure it was possible to discover where are the chaotic regions, the pe-riodic ones and the fixed points for the set of parameters. / Este trabalho tem como foco principal estudar, por métodos numéricos, um circuito eletrônico de Chua composto de quatro equações diferenciais através de diagramas de bifurcação e espaços de parâmetros. Nossa proposta aqui é ampliar os estudos numéricos já realizados neste sistema, revelando uma gama maior do seu comportamento. Para isso, realizamos construções dos espaços de parâmetros nos quais apresentam os valores dos expoentes de Lyapunov através de escalas coloridas, mediante a variação de dois parâmetros que compõem o circuito eletrônico. Com este procedimento é possível descobrir onde existem regiões caóticas, periódicas e pontos fixos para o conjunto de parâmetros do sistema.