Dissertations / Theses on the topic 'Neural fields'

付記する学位プログラム名: 京都大学卓越大学院プログラム「先端光・電子デバイス創成学」
京都大学
新制・課程博士
博士(工学)
甲第23205号
工博第4849号
京都大学大学院工学研究科電気工学専攻
(主査)教授 小林 哲生, 教授 松尾 哲司, 特定教授 中村 武恒
学位規則第4条第1項該当
Doctor of Philosophy (Engineering)
Kyoto University
DFAM

Binocular rivalry is an interesting phenomenon where perception oscillates between different images presented to the two eyes. This thesis is primarily concerned with modelling travelling waves of visual perception during transitions between these perceptual states. In order to model this effect in such a way that we retain as much analytical insight into the mechanisms as possible we employed neural field theory. That is, rather than modelling individual neurons in a neural network we treat the cortical surface as a continuous medium and establish integro-differential equations for the activity of a neural population. Our basic model which has been used by many previous authors both within and outside of neural field theory is to consider a one dimensional network of neurons for each eye. It is assumed that each network responds maximally to a particular feature of the underlying image, such as orientation. Recurrent connections within each network are taken to be excitatory and connections between the networks are taken to be inhibitory. In order for such a topology to exhibit the oscillations found in binocular rivalry there needs to be some form of slow adaptation which weakens the cross-connections under continued firing. By first considering a deterministic version of this model, we will show that, in fact, this slow adaptation also serves as a necessary "symmetry breaking" mechanism. Using this knowledge to make some mild assumptions we are then able to derive an expression for the shape of a travelling wave and its wave speed. We then go on to show that these predictions of our model are consistent not only with numerical simulations but also experimental evidence. It will turn out that it is not acceptable to completely ignore noise as it is a fundamental part of the underlying biology. Since methods for analyzing stochastic neural fields did not exist before our work, we first adapt methods originally intended for reaction-diffusion PDE systems to a stochastic version of a simple neural field equation. By regarding the motion of a stochastic travelling wave as being made up of two distinct components, firstly, the drift-diffusion of its overall position, secondly, fast fluctuations in its shape around some average front shape, we are able to derive a stochastic differential equation for the front position with respect to time. It is found that the front position undergoes a drift-diffusion process with constant coefficients. We then go on to show that our analysis agrees with numerical simulation. The original problem of stochastic binocular rivalry is then re-visited with this new toolkit and we are able to predict that the first passage time of a perceptual wave hitting a fixed barrier should be an inverse Gaussian distribution, a result which could potentially be experimentally tested. We also consider the implications of our stochastic work on different types of neural field equation to those used for modelling binocular rivalry. In particular, for neural fields which support pulled fronts propagating into an unstable state, the stochastic version of such an equation has wave fronts which undergo subdiffusive motion as opposed to the standard diffusion in the binocular rivalry case.

Neural stem cells showed strong electrotaxis, evidenced by highly directed migration towards the cathode.  Optimal electrotaxis was found to require growth factors and phosphoinositide 3-kinase (PI3-K) signalling, although reduced electrotaxis could be obtained without growth factors at the highest EFs used.  After EF exposure, neural stem cell trajectories became much more linear, and a reduction in the number of cell protrusions oriented towards the anode was observed.  Also, protrusions initially orienting towards the cathode retracted after the polarity of the EF was reversed, suggesting that EFs could inhibit the extension of anodal protrusions.  A simple model of neural stem cell migration was built with only two key parameters, which reproduced accurately neural stem cell migration patterns, and predicted that PI3-K functions in electrotaxis mainly by controlling cell orientation.  Finally, wild-type and Pax6^-/- embryonic neural stem cells were exposed simultaneously to EFs and contact guidance cues in conflicting orientations.  Only wild-type neural stem cells showed significant integrative migratory responses, suggesting that Pax6 is important for integration of diverse guidance cues during cell migration. The results obtained in this thesis show that neural stem cells display strong electrotaxis in vitro, which is accompanied by a qualitative change in the pattern of migration.  The results also identify the control of protrusion orientation by EFs as an important element in neural stem cell electrotaxis, contributing insight into the mechanisms of electrotaxis.  Finally, these results warrant further studies to assess the possibility of using EFs in brain repair therapies.

Spatiotemporal activity patterns with complex dynamics have been widely observed in the cortex, but their formation mechanisms and functional roles remain unclear. In this thesis, we first analyze how interactions of distributed bump activity patterns give rise to anomalous subdiffusive dynamics. Unlike normal diffusion whose mean squared error in representing working memory increases linearly with time, subdiffusion is characterized by a sublinear increase in its error, thereby significantly reducing memory degradation. The computational role of subdiffusive pattern dynamics in working memory is confirmed by our analysis of existing experimental data. To obtain theoretical insights into the mechanism underlying the formation of these activity patterns, we develop a new type of two-dimensional neural field model that incorporates refractoriness as a nonlinear negative feedback. We construct explicit bump solutions and perform a linear stability analysis, which reveals the emergence of stable bump activity as well as a critical transition from the bump state into propagating waves. Using numerical simulation, we show that the neural field exhibits local propagating patterns with rich dynamics including periodic, rotating and chaotic dynamics. We then show that propagating local patterns undergoing Levy flight emerge from a realistic cortical circuit model and that they can account for a wide range of neural response properties during both spontaneous and stimulus-driven activities. The fractional Levy dynamics of the spatiotemporal activity patterns provide a dynamic mechanism for a novel type of probabilistic representation which we refer to as fractional neural sampling (FNS). The Levy process naturally incorporates large discontinuous jumps into the sample path, enabling the neural sampler to effectively ‘tunnel’ through high energy barriers.

The ability for unmanned autonomous aerial vehicles (UAV) to localize themselves in an environment is fundamental for them to be able to function, even if they do not have access to a global positioning system. Recently, with the success of deep learning in vision based tasks, there have been some proposed methods for absolute geolocalization using vison based deep learning with satellite and UAV images. Most of these are only tested in urban environments, which begs the question: How well do they work in non-urban areas like forests and fields? One drawback of deep learning is that models are often regarded as black boxes, as it is hard to know why the models make the predictions they do, i.e. what information is important and is used for the prediction. To solve this, several neural network interpretation methods have been developed. These methods provide explanations so that we may understand these models better. This thesis investigates the localization accuracy of one geolocalization method in both urban and non-urban environments as well as applies neural network interpretation in order to see if it can explain the potential difference in localization accuracy of the method in these different environments. The results show that the method performs best in urban environments, getting a mean absolute horizontal error of 38.30m and a mean absolute vertical error of 16.77m, while it performed significantly worse in non-urban environments, getting a mean absolute horizontal error of 68.11m and a mean absolute vertical error 22.83m. Further, the results show that if the satellite images and images from the unmanned aerial vehicle are collected during different seasons of the year, the localization accuracy is even worse, resulting in a mean absolute horizontal error of 86.91m and a mean absolute vertical error of 23.05m. The neural network interpretation did not aid in providing an explanation for why the method performs worse in non-urban environments and is not suitable for this kind of problem.
Obemannade autonoma luftburna fordons (UAV) förmåga att lokaliera sig själva är fundamental för att de ska fungera, även om de inte har tillgång till globala positioneringssystem. Med den nyliga framgången hos djupinlärning applicerat på visuella problem har det kommit metoder för absolut geolokalisering med visuell djupinlärning med satellit- och UAV-bilder. De flesta av dessa metoder har bara blivit testade i stadsmiljöer, vilket leder till frågan: Hur väl fungerar dessa metoder i icke-urbana områden som fält och skogar? En av nackdelarna med djupinlärning är att dessa modeller ofta ses som svarta lådor eftersom det är svårt att veta varför modellerna gör de gissningar de gör, alltså vilken information som är viktig och används för gissningen. För att lösa detta har flera metoder för att tolka neurala nätverk utvecklats. Dessa metoder ger förklaringar så att vi kan förstå dessa modeller bättre. Denna uppsats undersöker lokaliseringsprecisionen hos en geolokaliseringsmetod i både urbana och icke-urbana miljöer och applicerar även en tolkningsmetod för neurala nätverk för att se ifall den kan förklara den potentialla skillnaden i precision hos metoden i dessa olika miljöer. Resultaten visar att metoden fungerar bäst i urbana miljöer där den får ett genomsnittligt absolut horisontellt lokaliseringsfel på 38.30m och ett genomsnittligt absolut vertikalt fel på 16.77m medan den presterade signifikant sämre i icke-urbana miljöer där den fick ett genomsnittligt absolut horisontellt lokaliseringsfel på 68.11m och ett genomsnittligt absolut vertikalt fel på 22.83m. Vidare visar resultaten att om satellitbilderna och UAV-bilderna är tagna från olika årstider blir lokaliseringsprecisionen ännu sämre, där metoden får genomsnittligt absolut horisontellt lokaliseringsfel på 86.91m och ett genomsnittligt absolut vertikalt fel på 23.05m. Tolkningsmetoden hjälpte inte i att förklara varför metoden fungerar sämre i icke-urbana miljöer och är inte passande att använda för denna sortens problem.

The recent demonstration of endogenous progenitor cells in the adult mammalian brain raises the exciting possibility that these undifferentiated cells may be able to generate new neurons for cell replacement in diseases such as Huntington's disease (HD). Previous studies have shown that neural stem cells in the rodent brain subependymal layer (SEL), adjacent to the caudate nucleus, proliferate and differentiate into neurons and glial cells but no previous study has characterised the human SEL or shown neurogenesis in the diseased human brain. In this study, histochemical and immunohistochemical techniques were used to demonstrate the regional anatomy and staining characteristics of the normal and HD brain SEL using light and laser scanning confocal microscopy. The results demonstrated that the normal and HD SEL contained migrating neuroblasts, glial cells and precursor cells but there were more of each cell type present in the HD brain, and that the increase in cell numbers correlated with HD neuropathological grade. The normal and HD SEL was stained with a proliferative marker, proliferating cell nuclear antigen (PCNA), to label dividing cells. The results showed a significant increase in the number of dividing cells in the HD brain that correlated with HD grade and with CAG repeat length. Furthermore, the results showed that neurogenesis had occurred in the SEL as evidenced by co-localisation of PCNA and the neuronal marker βIII-tubulin. Also, gliogenesis had occurred in the SEL as evidenced by the co-localisation of PCNA with the glial marker GFAP. These studies also revealed a 2.6 fold increase in the number of new neurons in the HD SEL. PCNA positive cells were distributed throughout the SEL overlying the caudate nucleus but most notably the ventral and central regions of the SEL adjacent to the caudate nucleus contained the highest number of proliferating cells. I examined the SEL for mature cell markers and demonstrated many of the same cell types that are present in the normal striatum. With the exception of neuropeptide Y (NPY) neurons, there was a reduction in the number of mature neurons in the HD SEL. The NPY neurons were more abundant in the HD SEL suggesting they play a role in progenitor cell proliferation. The results in this thesis provide evidence of increased progenitor cell proliferation and neurogenesis in the diseased adult human brain and indicate the regenerative potential of the human brain. These findings may be of major relevance to the development of therapeutic approaches in the treatment of neurodegenerative diseases.

In materials science, the traditional methodological framework is the identification of the composition-processing-structure-property causal pathways that link hierarchical structure to properties. However, all the properties of materials can be derived ultimately from structure and bonding, and so the properties of a material are interrelated to varying degrees. The work presented in this thesis, employed artificial neural networks (ANNs) to explore the correlations of different material properties with several examples in different fields. Those including 1) to verify and quantify known correlations between physical parameters and solid solubility of alloy systems, which were first discovered by Hume-Rothery in the 1930s. 2) To explore unknown crossproperty correlations without investigating complicated structure-property relationships, which is exemplified by i) predicting structural stability of perovskites from bond-valence based tolerance factors tBV, and predicting formability of perovskites by using A-O and B-O bond distances; ii) correlating polarizability with other properties, such as first ionization potential, melting point, heat of vaporization and specific heat capacity. 3) In the process of discovering unanticipated relationships between combination of properties of materials, ANNs were also found to be useful for highlighting unusual data points in handbooks, tables and databases that deserve to have their veracity inspected. By applying this method, massive errors in handbooks were found, and a systematic, intelligent and potentially automatic method to detect errors in handbooks is thus developed. Through presenting these four distinct examples from three aspects of ANN capability, different ways that ANNs can contribute to progress in materials science has been explored. These approaches are novel and deserve to be pursued as part of the newer methodologies that are beginning to underpin material research.

Thesis: S.M., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 22-24).
Molecular dynamics and Monte Carlo methods allow the properties of a system to be determined from its potential energy surface (PES). In the domain of crystalline materials, the PES is needed for electronic structure calculations, critical for modeling semiconductors, optical, and energy-storage materials. While first principles techniques can be used to obtain the PES to high accuracy, their computational complexity limits applications to small systems and short timescales. In practice, the PES must be approximated using a computationally cheaper functional form. Classical force field (CFF) approaches simply define the PES as a sum over independent energy contributions. Commonly included terms include bonded (pair, angle, dihedral, etc.) and non bonded (van der Waals, Coulomb, etc.) interactions, while more recent CFFs model polarizability, reactivity, and other higher-order interactions.
Simple, physically-justified functional forms are often implemented for each energy type, but this choice - and the choice of which energy terms to include in the first place - is arbitrary and often hand-tuned on a per-system basis, severely limiting PES transferability. This flexibility has complicated the quest for a universal CFF. The simplest usable CFFs are tailored to specific classes of molecules and have few parameters, so that they can be optimally parameterized using a small amount of data; however, they suffer low transferability. Highly-parameterized neural network potentials can yield predictions that are extremely accurate for the entire training set; however, they suffer over-fitting and cannot interpolate.
We develop a tool, called AuTopology, to explore the trade-offs between complexity and generalizability in fitting CFFs; focus on simple, computationally fast functions that enforce physics-based regularization and transferability; use message-passing neural networks to featurized molecular graphs and interpolate CFF parameters across chemical space; and utilize high performance computing resources to improve the efficiency of model training and usage. A universal, fast CFF would open the door to high-throughput virtual materials screening in the pursuit of novel materials with tailored properties.
by William H. Harris.
S.M.
S.M. Massachusetts Institute of Technology, Department of Materials Science and Engineering

This thesis deals with mesoscopic models of cortex called neural fields. The neural field equations describe the activity of neuronal populations, with common anatomical / functional properties. They were introduced in the 1950s and are called the equations of Wilson and Cowan. Mathematically, they consist of integro-differential equations with delays, the delays modeling the signal propagation and the passage of signals across synapses and the dendritic tree. In the first part, we recall the biology necessary to understand this thesis and derive the main equations. Then, we study these equations with the theory of dynamical systems by characterizing their equilibrium points and dynamics in the second part. In the third part, we study these delayed equations in general by giving formulas for the bifurcation diagrams, by proving a center manifold theorem, and by calculating the principal normal forms. We apply these results to one-dimensional neural fields which allows a detailed study of the dynamics. Finally, in the last part, we study three models of visual cortex. The first two models are from the literature and describe respectively a hypercolumn, i.e. the basic element of the first visual area (V1) and a network of such hypercolumns. The latest model is a new model of V1 which generalizes the two previous models while allowing a detailed study of specific effects of delays

To describe learning, as an alternative to a neural network recently dynamical systems were introduced whose vector fields were plastic and self-organising. Such a system automatically modifies its velocity vector field in response to the external stimuli. In the simplest case under certain conditions its vector field develops into a gradient of a multi-dimensional probability density distribution of the stimuli. We illustrate with examples how such a system carries out categorisation, pattern recognition, memorisation and forgetting without any supervision.

Cette thèse traite de modèles mésoscopiques de cortex appelés champs neuronaux. Les équations des champs neuronaux décrivent l'activité corticale de populations de neurones, ayant des propriétés anatomiques/fonctionnelles communes. Elles ont été introduites dans les années 1950 et portent le nom d'équations de Wilson et Cowan. Mathématiquement, elles consistent en des équations intégro-différentielles avec retards, les retards modélisant les délais de propagation des signaux ainsi que le passage des signaux à travers les synapses et l'arbre dendritique. Dans la première partie, nous rappelons la biologie nécessaire à la compréhension de cette thèse et dérivons les équations principales. Puis, nous étudions ces équations du point de vue des systèmes dynamiques en caractérisant leurs points d'équilibres et la dynamique dans la seconde partie. Dans la troisième partie, nous étudions de façon générale ces équations à retards en donnant des formules pour les diagrammes de bifurcation, en prouvant un théorème de la variété centrale et en calculant les principales formes normales. Nous appliquons tout d'abord ces résultats à des champs neuronaux simples mono-dimensionnels qui permettent une étude détaillée de la dynamique. Enfin, dans la dernière partie, nous appliquons ces différents résultats à trois modèles de cortex visuel. Les deux premiers modèles sont issus de la littérature et décrivent respectivement une hypercolonne, /i.e./ l'élément de base de la première aire visuelle (V1) et un réseau de telles hypercolonnes. Le dernier modèle est un nouveau modèle de V1 qui généralise les deux modèles précédents tout en permettant une étude poussée des effets spécifiques des retards
This thesis deals with mesoscopic models of cortex called neural fields. The neural field equations describe the activity of neuronal populations, with common anatomical / functional properties. They were introduced in the 1950s and are called the equations of Wilson and Cowan. Mathematically, they consist of integro-differential equations with delays, the delays modeling the signal propagation and the passage of signals across synapses and the dendritic tree. In the first part, we recall the biology necessary to understand this thesis and derive the main equations. Then, we study these equations with the theory of dynamical systems by characterizing their equilibrium points and dynamics in the second part. In the third part, we study these delayed equations in general by giving formulas for the bifurcation diagrams, by proving a center manifold theorem, and by calculating the principal normal forms. We apply these results to one-dimensional neural fields which allows a detailed study of the dynamics. Finally, in the last part, we study three models of visual cortex. The first two models are from the literature and describe respectively a hypercolumn, i.e. the basic element of the first visual area (V1) and a network of such hypercolumns. The latest model is a new model of V1 which generalizes the two previous models while allowing a detailed study of specific effects of delays

In our country farmers have to face many difficulties because of the poor irrigation system. During flood situation, excessive waters will be staged in paddy field producing great loss and pain to farmers. So, proper Irrigation mechanism is an essential component of paddy production. Poor irrigation methods and crop management are rapidly depleting the country’s water table. Most small hold farmers cannot afford new wells or lawns and they are looking for alternative methods to reduce their water consumption. So proper irrigation mechanism not only leads to high crop production but also pave a way for water saving techniques. Automation of irrigation system has the potential to provide maximum water usage efficiency by monitoring soil moistures. The control unit based on Artificial Neural Network is the pivotal block of entire irrigation system. Using this control unit certain factors like temperature, kind of the soil and crops, air humidity, radiation in the ground were estimated and this will help to control the flow of water to acquire optimized results.
Water is an essential resource in the earth. It is also essential for irrigation, so irrigation technique is essential for agriculture. To irrigate large area of lands is a tedious process. In our country farmers are not following proper irrigation techniques. Currently, most of the irrigation scheduling systems and their corresponding automated tools are in fixed rate. Variable rate irrigation is very essential not only for the improvement of irrigation system but also to save water resource for future purpose. Most of the irrigation controllers are ON/OFF Model. These controllers cannot give optimal results for varying time delays and system parameters. Artificial Neural Network (ANN) based intelligent control system is used for effective irrigation scheduling in paddy fields. The input parameters like air, temperature, soil moisture, radiations and humidity are modeled. Using appropriate method, ecological conditions, Evapotranspiration, various growing stages of crops are considered and based on that the amount of water required for irrigation is estimated. Using this existing ANN based intelligent control system, the water saving procedure in paddy field can be achieved. This model will lead to avoid flood in paddy field during the rainy seasons and save that water for future purposes.

The aim of this thesis was to investigate the input-specificity of sensory-induced plasticity in humans. This was achieved by varying the characteristics of sine gratings so that they selectively targeted distinct populations of neurons in the visual cortex. In Experiments 1-3, specificity was investigated with electroencephalography using horizontally- and vertically-oriented sine gratings (Experiment 1) or gratings of differing spatial frequency (Experiments 2 & 3). Increases in the N1b potential were observed only for sine gratings that were the same in orientation or spatial frequency as that used as the tetanus, suggesting that the potentiation is specific to the visual pathways stimulated during the induction of the tetanus. However, the increase in the amplitude of the N1b in Experiment 1 was not maintained when tested again at 50 minutes post-tetanus. This may have been due to depotentiation caused by the temporal frequency of stimulus presentation in the first post-tetanus block. To try to circumvent this potential confound, immediate and maintained (tested 30 minutes post-tetanus) spatial-frequency-specific potentiation were tested separately in Experiments 2 and 3, respectively. Experiment 3 demonstrated that the increased N1b was maintained for up to half an hour post-tetanus. In addition, the findings from Experiment 1, as well as the pattern of results from Experiments 2 and 3, indicate that the potentiation must be occurring in the visual cortex rather than further upstream at the lateral geniculate nucleus. In Experiment 4 functional magnetic resonance imaging was used to more accurately localise where these plastic changes were taking place using sine gratings of differing spatial frequency. A small, focal post-tetanic increase in the blood-oxygen-level-dependent (BOLD) response was observed for the tetanised grating in the right temporo-parieto-occipital junction. For the non-tetanised grating, decreases in BOLD were found in the primary visual cortex and bilaterally in the cuneus and pre-cuneus. These decreases may have been due to inhibitory interconnections between neurons tuned to different spatial frequencies. These data indicate that tetanic sensory stimulation selectively targets and potentiates specific populations of neurons in the visual cortex.

The creation of anatomically correct three-dimensional joints for the simulation of humans is a complex process, a key difficulty being the correction of invalid joint configurations to the nearest valid alternative. Personalised models based on individual joint mobility are in demand in both animation and medicine [1]. Medical models need to be highly accurate animated models less so, however if either are to be used in a real time environment they must have a low temporal cost (high performance). This work briefly explores Support Vector Machine neural networks as joint configuration classifiers that group joint configurations into invalid and valid. A far more detailed investigation is carried out into the use of topologically evolved feed forward neural networks for the generation of appropriately proportioned corrective components which when applied to an invalid joint configuration result in a valid configuration and the same configuration if the original configuration was valid. Discontinuous vector fields were used to represent constraints of varying size, dimensionality and complexity. This culminated in the creation corrective quaternion constraints represented by discontinuous vector fields, learned by topologically evolved neural networks and trained via the resilient back propagation algorithm. Quaternion constraints are difficult to implement and although alternative methods exist [2-6] the method presented here is superior in many respects. This method of joint constraint forms the basis of the contribution to knowledge along with the discovery of relationships between the continuity and distribution of samples in quaternion space and neural network performance. The results of the experiments for constraints on the rotation of limb with regular boundaries show that 3.7 x lO'Vo of patterns resulted in errors greater than 2% of the maximum possible error while for irregular boundaries 0.032% of patterns resulted in errors greater than 7.5%.

Thesis (M.S.)--West Virginia University, 1999.
Title from document title page. Document formatted into pages; contains x, 76 p. : ill. (some col.), map (some col.). Includes abstract. Includes bibliographical references (p. 54-55).

The mammalian primary visual cortex (V1) has been extensively studied over the last decades to probe the neural mechanisms behind visual perception of elementary visual features such as edges, direction of motion, and colour. Numerous experiments have visualized the ordered arrangement of various functional maps in V1 and measured the neural activity patterns associated with them. However, only a few studies have quantitatively modelled the influences of the spatial structure of the functional maps on the neural activities. Moreover, the experimental maps usually show a great degree of irregularity and contain a large number of neurons, which makes them difficult to describe in analytic forms and computationally inefficient to integrate into quantitative neural models of large scale brain dynamics. The present work approximates the functional maps of V1 in a compact analytic representation that complies with the main characteristics of the experimental maps, and integrates such map structure into the established neural field model with interacting neural populations to reproduce oscillatory neural activities in V1.

This thesis makes two contributions to the research domain of relation extraction (RE), i.e., the automated discovery of semantic links in unstructured text. The first contribution is a method for creating a dataset for RE, and using it to create the first Swedish RE dataset involving nine relationships between persons, locations and vehicles. The second contribution is a variety of experiments on this new dataset providing baselines. The relation extraction systems created in this thesis include deep multi-channel convolutional neural networks, and Word2Vec embeddings. A manual labeling of a subset of our data shows an accuracy of 73%. We find that using a discrete representation of part-of-speech and dependency tags in the multi-channel convolutional network yields the best performance with a micro-average F1-score of 77%. The thesis discusses a variety of problems and future avenues of research, including the underlying motivation of this work: the automatic summarization of police reports in Sweden.
Detta arbete bidrar med två insikter till forskning inom relationsextrahering (RE), det vill säga, att automatiskt upptäcka semantiska länkar i ostrukturerad text. Det första bidraget är en metod för att skapa ett dataset för RE och för att använda det till att skapa ett svenskt RE-dataset som involverar nio relationer mellan personer, platser och fordon. Det andra bidraget är en baslinje via experiment på detta nya dataset. Relationsextraheringssystemet skapat i detta arbete inkluderar ett djupt flerkanaligt faltningsnätverk med ordvektorer viaWord2Vec-algoritmen. En manuell kategorisering av en delmängd av datan visar en tillförlitlighet på 73%. Resultaten visar att användningen av en diskret representation av ordklasser och beroende-taggar i det flerkanaliga neurala nätverket presterar bäst med ett medelvärde av mikro-F1 på 77%. Detta arbete diskuterar problem och framtida tillämpningar, inkluderat den underliggande motiveringen för detta arbete: automatisk summering av svenska polisrapporter.

The central theme of this thesis is the development of both a generalised computational model for large-scale brain networks and the neuroinformatics platform that enables a systematic exploration and analysis of those models. In this thesis we describe the mathematical framework of the computational model at the core of the tool The Virtual brain (TVB), designed to recreate collective whole brain dynamics by virtualising brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. We also review previous studies related to brain network models and multimodal neuroimaging integration and detail how they are related to the general model presented in this work. Practical examples describing how to build a minimal *in silico* primate brain model are given. Finally, we explain how the resulting software tool, TVB, facilitates the collaboration between experimentalists and modellers by exposing both a comprehensive simulator for brain dynamics and an integrative framework for the management, analysis, and simulation of structural and functional data in an accessible, web-based interface
The central theme of this thesis is the development of both a generalised computational model for large-scale brain networks and the neuroinformatics platform that enables a systematic exploration and analysis of those models. In this thesis we describe the mathematical framework of the computational model at the core of the tool The Virtual brain (TVB), designed to recreate collective whole brain dynamics by virtualising brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. We also review previous studies related to brain network models and multimodal neuroimaging integration and detail how they are related to the general model presented in this work. Practical examples describing how to build a minimal *in silico* primate brain model are given. Finally, we explain how the resulting software tool, TVB, facilitates the collaboration between experimentalists and modellers by exposing both a comprehensive simulator for brain dynamics and an integrative framework for the management, analysis, and simulation of structural and functional data in an accessible, web-based interface

Les champs électromagnétiques sont couramment utilisés pour stimuler de manière non-invasive le cerveau humain soit à des fins thérapeutiques ou dans un contexte de recherche. Les effets de la stimulation magnétique varient en fonction de la fréquence et de l'intensité du champ magnétique. Les mécanismes mis en jeu restent inconnus, d'autant plus lors de stimulations à faible intensité. Dans cette thèse, nous avons évalué les effets de stimulations magnétiques répétées à différentes fréquences appliqués à faible intensité (10-13 mT ; Low Intensity Repetitive Magnetic Stimulation : LI-rMS) in vitro, sur des cultures corticales primaires et sur des modèles de réparation neuronale. De plus, nous décrivons une méthodologie pour la construction d'un dispositif instrumental fait sur mesure pour stimuler des cultures cellulaires.Les résultats montrent des effets dépendant de la fréquence sur la libération du calcium des stocks intracellulaires, sur la mort cellulaire, sur la croissance des neurites, sur la réparation neuronale, sur l'activation des neurones et sur l'expression de gènes impliqués. En conclusion, nous avons montré pour la première fois un nouveau mécanisme d'activation cellulaire par les champs magnétiques à faible intensité. Cette activation se fait en l'absence d'induction de potentiels d'action. Les résultats soulignent l'importance biologique de la LI-rMS par elle-même mais aussi en association avec les effets de la rTMS à haute intensité. Une meilleure compréhension des effets fondamentaux de la LI-rMS sur les tissus biologiques est nécessaire afin de mettre au point des applications thérapeutiques efficaces pour le traitement des conditions neurologiques
Electromagnetic fields are widely used to non-invasively stimulate the human brain in clinical treatment and research. This thesis investigates the effects of different low intensity (mT) repetitive magnetic stimulation (LI-rMS) parameters on single neurons and neural networks and describes key aspects of custom tailored LI-rMS delivery in vitro. Our results show stimulation specific effects of LI-rMS on cell survival, neuronal morphology, neural circuit repair and gene expression. We show novel mechanisms underlying cellular responses to stimulation below neuronal firing threshold, extending our understanding of the fundamental effects of LI-rMS on biological tissue which is essential to better tailor therapeutic applications

During the past half-century, the experimental use of looking measures have led to many new discoveries about the origins of cognition. Across the first year, infants' looking changes in predictable ways, they form memories more quickly, and they begin to discriminate between subtly different stimuli. However, a rich understanding of the link between looking and cognitive dynamics has yet to be achieved. This was the overarching goal of this thesis. I developed a new Dynamic Field Theory of infant looking and memory and formally implemented this theory in a Dynamic Neural Field model. In Experiment 1, I tested and confirmed a prediction of the model with 10-month-old infants. The prediction was that robust memory can induce both a familiarity and novelty bias depending on the metric similarity of the familiar and novel items at which infants look. This prediction is a radical one because all existing theories posit that familiarity biases arise from weak memory. One central innovation of the DNF model is that it captures developmental change in the rate at which memories are formed and discrimination within the same system and from the same developmental mechanism. With a validated theory in hand, in Experiment 2 I tested this theoretical assumption. In particular, I measured the looking dynamics and discrimination performance of 5-, 7-, and 10-month-old infants. The results showed that infants' exhibited an increased ability to discriminate between dissimilar familiar and novel items between 5 and 7 months of age. The results also showed that three well-known looking indices of memory formation also generally change between 5 and 7 months of age. Additionally, individual differences in these looking indices were predictive of infants' discrimination performance. These findings indicate that, indeed, looking and discrimination change together, and are linked within individuals, over development. In Experiment 3 I tested developmental change in the discrimination abilities of at-risk infants. Previous studies have shown that the looking dynamics and recognition performance of at-risk infants is delayed but, critically, follows the same developmental trajectory as typically developing infants. Consistent with these previous studies, the looking dynamics of at-risk infants did change in predictable ways over development. However, their discrimination performance did not - young at-risk infants, unlike young typically developing infants or older at-risk infants, discriminated between dissimilar familiar and novel items.

Thesis (Ph. D.)--University of California, San Diego, 2007.
Title from first page of PDF file (viewed January 8, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 112-124).

La thèse propose une approche de l'apprentissage temporel par des mécanismes d'auto-organisation à grain fin. Le manuscrit situe dans un premier temps le travail dans la perspective de contribuer à promouvoir une informatique cellulaire. Il s'agit d'une informatique où les calculs se répartissent en un grand nombre de calculs élémentaires, exécutés en parallèle, échangeant de l'information entre eux. Le caractère cellulaire tient à ce qu'en plus d’être à grain fin, une telle architecture assure que les connexions entre calculateurs respectent une topologie spatiale, en accord avec les contraintes des évolutions technologiques futures des matériels. Dans le manuscrit, la plupart des architectures informatiques distribuées sont examinées suivant cette perspective, pour conclure que peu d'entre elles relèvent strictement du paradigme cellulaire.Nous nous sommes intéressé à la capacité d'apprentissage de ces architectures, du fait de l'importance de cette notion dans le domaine connexe des réseaux de neurones par exemple, sans oublier toutefois que les systèmes cellulaires sont par construction des systèmes complexes dynamiques. Cette composante dynamique incontournable a motivé notre focalisation sur l'apprentissage temporel, dont nous avons passé en revue les déclinaisons dans les domaines des réseaux de neurones supervisés et des cartes auto-organisatrices.Nous avons finalement proposé une architecture qui contribue à la promotion du calcul cellulaire en ce sens qu'elle exhibe des propriétés d'auto-organisation pour l'extraction de la représentation des états du système dynamique qui lui fournit ses entrées, même si ces dernières sont ambiguës et ne reflètent que partiellement cet état. Du fait de la présence d'un cluster pour nos simulations, nous avons pu mettre en œuvre une architecture complexe, et voir émerger des phénomènes nouveaux. Sur la base de ces résultats, nous développons une critique qui ouvre des perspectives sur la suite à donner à nos travaux
The thesis proposes a sequence learning approach that uses the mechanism of fine grain self-organization. The manuscript initially starts by situating this effort in the perspective of contributing to the promotion of cellular computing paradigm in computer science. Computation within this paradigm is divided into a large number of elementary calculations carried out in parallel by computing cells, with information exchange between them.In addition to their fine grain nature, the cellular nature of such architectures lies in the spatial topology of the connections between cells that complies with to the constraints of the technological evolution of hardware in the future. In the manuscript, most of the distributed architecture known in computer science are examined following this perspective, to find that very few of them fall within the cellular paradigm.We are interested in the learning capacity of these architectures, because of the importance of this notion in the related domain of neural networks for example, without forgetting, however, that cellular systems are complex dynamical systems by construction.This inevitable dynamical component has motivated our focus on the learning of temporal sequences, for which we reviewed the different models in the domains of neural networks and self-organization maps.At the end, we proposed an architecture that contributes to the promotion of cellular computing in the sense that it exhibits self-organization properties employed in the extraction of a representation of a dynamical system states that provides the architecture with its entries, even if the latter are ambiguous such that they partially reflect the system state. We profited from an existing supercomputer to simulate complex architecture, that indeed exhibited a new emergent behavior. Based on these results we pursued a critical study that sets the perspective for future work

Thesis: S.B., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 27-30).
Glial scar tissue forms in the brain as a response to the implant injury and hampers the effectiveness of the implant treatment. Constant relative micromotion between the mechanically mismatched neural implant and brain tissue is thought to play a key role glial scar formation. This study investigated the effects of poly(ethylene glycol) (PEG) hydrogel coatings for glass brain implant devices on strain fields imposed by those devices to brain tissue due to micromotion in the brain. PEG hydrogels were created using macromers of 2000-8000 Mw and 5-20 wt.% in solution. The moduli of the hydrogels were calculated via Hertzian analysis of force-deflection curves produced using an AFM tip as a nanoindenter. The moduli of the samples did not change significantly with change in macromer Mw, but did change with solution concentration. 20% gels had moduli of 120-180 kPa and 5-10% gels had moduli of 0-20 kPa. The strains imposed by the coated devices were found to be lower at the surface by ~30% as compared to uncoated and the strain field dropped off much more quickly.
by Maxwell Ethan Plaut.
S.B.

The aim of this work is to find out which algorithm is the best at recognizing named entities in e-mail messages. The theoretical part explains the existing tools in this field. The practical part describes the design of two tools specifically designed to create new models capable of recognizing named entities in e-mail messages. The first tool is based on a neural network and the second tool uses a CRF graph model. The existing and newly created tools and their ability to generalize are compared on a subset of e-mail messages provided by Kiwi.com.

This thesis deals with the current methods of semantic segmentation using deep learning. Other approaches of neaural networks in the area of deep learning are also discussed. It contains historical solutions of neural networks, their development, and basic principle. Convolutional neural networks are nowadays the most preferable networks in solving tasks as detection, classification, and image segmentation. The functionality was verified on a freely available environment based on conditional random fields as recurrent neural networks and compered with the deep convolutional neural networks using conditional random fields as postprocess. The latter mentioned method has become the basis for training of new models on two different datasets. There are various enviroments used to implement neural networks using deep learning, which offer diverse perform possibilities. For demonstration purposes a Python application leveraging the BVLC\,/\,Caffe framework was created. The best achieved accuracy of a trained model for clothing segmentation is 50,74\,\% and 68,52\,\% for segmentation of VOC objects. The application aims to allow interaction with image segmentation based on trained models.

Bovine mastitis adversely affects the dairy industry around the world. This disease is caused by a diverse range of bacteria, broadly categorised as minor and major pathogens. In-line tools that help identify these bacterial groupings in the early stages of the disease are advantageous as timely decisions could be made before the cow develops any clinical symptoms. The first objective of this research was to identify the most informative milk parameters for the detection of minor and major bacterial pathogens. The second objective of this research was to evaluate the potential of supervised and unsupervised neural network learning paradigms for the detection of minor infected and major infected quarters in the early stages of the disease. The third objective was to evaluate the effects of different proportions of infected to non-infected cases in the training data set on the correct classification rate of the supervised neural network models as there are proportionately more non-infected cases in a herd than infected cases. A database developed at Lincoln University was used to achieve the research objectives. Starting at calving, quarter milk samples were collected weekly from 112 cows for a period of fourteen weeks, resulting in 4852 samples with complete records for somatic cell count (SCC), electrical resistance, protein percentage, fat percentage, and bacteriological status. To account for the effects of the stage of lactation on milk parameters with respect to days in milking, data was divided into three days in milk ranges. In addition, cow variation was accounted for by the sire family from which the cow originated and the lactation number of each cow. Data was pre-processed before the application of advanced analytical techniques. Somatic cell score (SCS) and electrical resistance index were derived from somatic cell count and electrical resistance, respectively. After pre-processing, the data was divided into training and validation sets for the unsupervised neural network modelling experiment and, for the supervised neural network modelling experiments, the data was divided into training, calibration and validation sets. Prior to any modelling experiments, the data was analysed using statistical and multivariate visualisation techniques. Correlations (p<0.05) were found between the infection status of a quarter and its somatic cell score (SCS, 0.86), electrical resistance index (ERI, -0.59) and protein percentage (PP, 0.33). The multivariate parallel visualisation analysis validated the correlation analysis. Due to significant multicolinearity [Correlations: SCS and ERI (-0.65: p<0.05); SCS and PP (0.32: p<0.05); ERI and PP (-0.35: p<0.05)], the original variables were decorrelated using principle component analysis. SCS and ERI were found to be the most informative variables for discriminating between non-infected, minor infected and major infected cases. Unsupervised neural network (USNN) model was trained using the training data set which was extracted from the database, containing approximately equal number of randomly selected records for each bacteriological status [not infected (NI), infected with a major pathogen (MJI) and infected with a minor pathogen (MNI)]. The USNN model was validated with the remaining data using the four principle components, days in milk (DIM), lactation number (LN), sire number, and bacteriological status (BS). The specificity of the USNN model in correctly identifying non infected cases was 97%. Sensitivities for correctly detecting minor and major infections were 89% and 80%, respectively. The supervised neural network (SNN) models were trained, calibrated and validated with several sets of training, calibration and validation data, which were randomly extracted from the database in such a way that each set has a different proportion of infected to non-infected cases ranging from 1:1 to 1:10. The overall accuracy of these models based on validation data sets gradually increased with increase in the number of non-infected cases in the data sets (80% for the 1:1, 84% for 1:2, 86% for 1:4 and 93% for 1:10). Specificities of the best models for correctly recognising non-infected cases for the four data sets were 82% for 1:1, 91% for 1:2, 94% for 1:4 and 98% for 1:10. Sensitivities for correctly recognising minor infected cases for the four data sets were 86% for 1:1, 76% for 1:2, 71% for 1:4 and 44% for 1:10. Sensitivities for correctly recognising major infected cases for the four data sets were 20% for 1:1, 20% for 1:2, 30% for 1:4 and 40% for 1:10. Overall, sensitivity for the minor infected cases decreased while that of major infected cases increased with increase in the number non-infected cases in the training data set. Due to the very low prevalence of MJI category in this particular herd, results for this category may be inconclusive. This research suggests that somatic cell score and electrical resistance index of milk were the most effective variables for detecting the infection status of a quarter followed by milk protein and fat percentage. The neural network models were able to differentiate milk containing minor and major bacterial pathogens based on milk parameters associated with mastitis. It is concluded that the neural network models can be developed and incorporated into milking machines to provide an efficient and effective method for the diagnosis of mastitis.

Artificial Neural Networks (ANNs) can be viewed as a mathematical model to simulate natural and biological systems on the basis of mimicking the information processing methods in the human brain. The capability of current ANNs only focuses on approximating arbitrary deterministic input-output mappings. However, these ANNs do not adequately represent the variability which is observed in the systems’ natural settings as well as capture the complexity of the whole system behaviour. This thesis addresses the development of a new class of neural networks called Stochastic Neural Networks (SNNs) in order to simulate internal stochastic properties of systems. Developing a suitable mathematical model for SNNs is based on canonical representation of stochastic processes or systems by means of Karhunen-Loève Theorem. Some successful real examples, such as analysis of full displacement field of wood in compression, confirm the validity of the proposed neural networks. Furthermore, analysis of internal workings of SNNs provides an in-depth view on the operation of SNNs that help to gain a better understanding of the simulation of stochastic processes by SNNs.

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-11T14:07:58Z No. of bitstreams: 1 HUGO SARAIVA TAVARES - DISSERTAÇÃO PPGMAT 2016..pdf: 830543 bytes, checksum: d315a3d95e0d2b08d4251cbb487964b6 (MD5)
Made available in DSpace on 2018-08-11T14:07:58Z (GMT). No. of bitstreams: 1 HUGO SARAIVA TAVARES - DISSERTAÇÃO PPGMAT 2016..pdf: 830543 bytes, checksum: d315a3d95e0d2b08d4251cbb487964b6 (MD5) Previous issue date: 2016-06
Capes
Para ler o resumo deste trabalho recomendamos o download do arquivo, uma vez que o mesmo possui fórmulas e caracteres matemáticos que não foram possíveis trascreve-los aqui.
To read the summary of this work we recommend downloading the file, since it has formulas and mathematical characters that were not possible to transcribe them here.

Two types of artificial neural networks, Multilayer Perceptron (MLP) and Self-organizing Feature Map (SOM), were employed to detect mastitis for robotic milking stations using the preprocessed data relating to the electrical conductivity and milk yield. The SOM was developed to classify the health status into three categories: healthy, moderately ill and severely ill. The clustering results were successfully evaluated and validated by using statistical techniques such as K-means clustering, ANOVA and Least Significant Difference. The result shows that the SOM could be used in the robotic milking stations as a detection model for mastitis. For developing MLP models, a new mastitis definition based on higher EC and lower quarter yield was created and Principle Components Analysis technique was adopted for addressing the problem of multi-colinearity existed in the data. Four MLPs with four combined datasets were developed and the results manifested that the PCA-based MLP model is superior to other non-PCA-based models in many respects such as less complexity, higher predictive accuracy. The overall correct classification rate (CCR), sensitivity and specificity of the model was 90.74 %, 86.90 and 91.36, respectively. We conclude that the PCA-based model developed here can improve the accuracy of prediction of mastitis by robotic milking stations.

Two decades of event-related potential (ERP) research have established that the most consistent correlates of the onset of visual consciousness are the early visual awareness negativity (VAN), a negative component in the N2 time range over posterior electrode sites, and the late positivity (LP), a positive component in the P3 time range over fronto-parietal electrode sites. A review by Koivisto & Revonsuo (2010) had looked at 39 studies and concluded that the VAN is the earliest and most reliable correlate of visual phenomenal consciousness, whereas the LP probably reflects later processes associated with reflective/access consciousness. However, an “early” vs. “late” debate still persists. This thesis provides an update to that earlier review. All ERP and MEG studies that have appeared since 2010 and directly compared ERPs of aware and unaware conditions are considered. The result corroborates the view that VAN is the earliest and most consistent signature of visual phenomenal consciousness, and casts further doubt on the LP as an ERP correlate of consciousness. Important new methodological, empirical, and theoretical developments in the field are described, and the empirical results are related to the theoretical background debates.

Solar transient eruptions are the main cause of interplanetary-magnetospheric disturbances leading to the phenomena known as geomagnetic storms. Eruptive solar events such as coronal mass ejections (CMEs) are currently considered the main cause of geomagnetic storms (GMS). GMS are strong perturbations of the Earth’s magnetic field that can affect space-borne and ground-based technological systems. The solar-terrestrial impact on modern technological systems is commonly known as Space Weather. Part of the research study described in this thesis was to investigate and establish a relationship between GMS (periods with Dst ≤ −50 nT) and their associated solar and interplanetary (IP) properties during solar cycle (SC) 23. Solar and IP geoeffective properties associated with or without CMEs were investigated and used to qualitatively characterise both intense and moderate storms. The results of this analysis specifically provide an estimate of the main sources of GMS during an average 11-year solar activity period. This study indicates that during SC 23, the majority of intense GMS (83%) were associated with CMEs, while the non-associated CME storms were dominant among moderate storms. GMS phenomena are the result of a complex and non-linear chaotic system involving the Sun, the IP medium, the magnetosphere and ionosphere, which make the prediction of these phenomena challenging. This thesis also explored the predictability of both the occurrence and strength of GMS. Due to their nonlinear driving mechanisms, the prediction of GMS was attempted by the use of neural network (NN) techniques, known for their non-linear modelling capabilities. To predict the occurrence of storms, a combination of solar and IP parameters were used as inputs in the NN model that proved to predict the occurrence of GMS with a probability of 87%. Using the solar wind (SW) and IP magnetic field (IMF) parameters, a separate NN-based model was developed to predict the storm-time strength as measured by the global Dst and ap geomagnetic indices, as well as by the locally measured K-index. The performance of the models was tested on data sets which were not part of the NN training process. The results obtained indicate that NN models provide a reliable alternative method for empirically predicting the occurrence and strength of GMS on the basis of solar and IP parameters. The demonstrated ability to predict the geoeffectiveness of solar and IP transient events is a key step in the goal towards improving space weather modelling and prediction.

Individuals of the cnidarian genus Physalia are a common sight at New Zealand beaches and are the primary cause of jellyfish stings to beachgoers each year. The identity of the species and the environmental factors that determine its presence are unknown. Lack of knowledge of many marine species is not unusual, as pelagic invertebrates often lack detailed taxonomic descriptions as well as information about their dispersal mechanisms such that meaningful patterns of distribution and dispersal are almost impossible to determine. Molecular systematics has proven to be a powerful tool for species identification and for determining geographical distributions. However, other techniques are needed to indicate the causal mechanisms that may result in a particular species distribution. The aim of this study was to apply molecular techniques to the cnidarian genus Physalia to establish which species occur in coastal New Zealand, and to apply models to attempt to forecast its occurrence and infer some mechanisms of dispersal. Physalia specimens were collected from New Zealand, Australia and Hawaii and sequenced for Cytochrome c oxidase I (COI) and the Internal transcribed spacer 1 (ITS1). Three clans were found: a Pacific-wide clan, an Australasian clan and New Zealand endemic clan with a distribution confined to the Bay of Plenty and the East Coast of the North Island. Forecasting Physalia occurrence directly from presence data using artificial neural networks (ANN) proved unsuccessful and it was necessary to pre-process the presence data using a variable sliding window to reduce noise and improve accuracy. This modelling approach outperformed the time lagged based networks giving improved forecasts in both regions that were assessed. The ANN models were able to indicated significant trends in the data but would require more data at higher resolution to give more accurate forecasts of Physalia occurrence suitable for decision making on New Zealand beaches. To determine possible causal mechanisms of recorded occurrences and to identify possible origins of Physalia the presence and absence of Physalia on swimming beaches throughout the summer season was modelled using ANN and Naϊve Bayesian Classifier (NBC). Both models were trained on the same data consisting of oceanographic variables. The modelling carried out in this study detected two dynamic systems, which matched the distribution of the molecular clans. One system was centralised in the Bay of Plenty matching the New Zealand endemic clan. The other involved a dynamic system that encompassed four other regions on both coasts of the country that matched the distribution of the other clans. By combining the results it was possible to propose a framework for Physalia distribution including a mechanism that has driven clan divergence. Moreover, potential blooming areas that are notoriously hard to establish for jellyfish were hypothesised for further study and/or validation.

Biophysical processes related to the modulation of cellular mechanisms occur due to either presynaptic or postsynaptic effects. These processes involve some physiological phenomena whose different dynamics can potentially be distinguished via traces they leave in the power spectra of brain activity and/or connectivity fluctuations. Systematic expansion of NFT equations in terms of nonlinear response functions is formulated in Chapter 2 to enable a wide variety of nonlinear wave-wave and wave-neuron processes. This theory helps to handle neural quantities such as firing rates, neural field, soma voltage, threshold, and coupling strength, along with their steady state values and perturbations. Many physiological processes such as facilitation, habituation, and refractoriness can be interpreted as the consequences of neural feedbacks that allow presynaptic and postsynaptic firing rates to modulate firing thresholds or synaptic strengths at a given location. NFT is used to analyze such feedback processes to determine their signatures, which are measurable through fluctuations in the power spectra of brain activity in Chapter 3. Depending on the feedback processes, these signatures include either enhancement or reduction of low-frequency activities, effects near the alpha resonance (enhancements and/or resonance splitting), and the appearance of new resonances. Physiological phenomena related to nonlinear feedback processes can potentially be identified and distinguished by means of these different spectral signatures. The spatiotemporal power spectra of connectivity fluctuations are also analyzed via NFT in Chapter 4, which also show distinctive features. Some spectral signatures result from the contributions from discrete spatial modes to the frequency power spectra depending on feedbacks. Some of these appear to be characteristic of just one feedback type and can potentially be used as diagnostics in experiments. Some ideas for future work are mentioned in Chapter 5.

Dans la recherche permanente de solutions pour dépasser les limitations de plus en plus visibles de nos architectures matérielles, le calcul non-conventionnel offre des alternatives variées comme l’ingénierie neuromorphique et le calcul cellulaire. Comme von Neumann qui s’était initialement inspiré du cerveau pour concevoir l’architecture des ordinateurs, l’ingénierie neuromorphique prend la même inspiration en utilisant un substrat analogique plus proche des neurones et des synapses. Le calcul cellulaire s’inspire lui des substrats de calcul naturels (chimique, physiques ou biologiques) qui imposent une certaine localité des calculs de laquelle va émerger une organisation et des calculs. La recherche sur les mécanismes neuronaux permet de comprendre les grands principes de calculs émergents des neurones. Un des grands principes que nous allons utiliser dans cette thèse est la dynamique d’attracteurs d’abord décrite par Amari (champs neuronaux dynamiques, ou DNF pour dynamic neural fields), Amit et Zhang (réseaux de neurones à attracteurs continus). Ces champs de neurones ont des propriétés de calcul variées mais sont particulièrement adaptés aux représentations spatiales et aux fonctions des étages précoces du cortex visuel. Ils ont été utilisés entre autres dans des applications de robotique autonome, dans des tâches de classification et clusterisation. Comme de nombreux modèles de calcul neuronal, ils sont également intéressants du point de vue des architectures matérielles en raison de leur robustesse au bruit et aux fautes. On voit donc l’intérêt que ces modèles de calcul peuvent avoir comme solution permettant de dépasser (ou poursuivre) la loi de Moore. La réduction de la taille des transistors provoque en effet beaucoup de bruit, de même que la relaxation de la contrainte de ~ 0% de fautes lors de la production ou du fonctionnement des circuits permettrait d’énormes économies. Par ailleurs, l’évolution actuelle vers des circuits many-core de plus en plus distribués implique des difficultés liées au mode de calcul encore centralisés de la plupart des modèles algorithmiques parallèles, ainsi qu’au goulot d’étranglement des communications. L’approche cellulaire est une réponse naturelle à ces enjeux. Partant de ces différents constats, l’objectif de cette thèse est de rendre possible les calculs et applications riches des champs neuronaux dynamiques sur des substrats matériels grâce à des modèles neuro-cellulaires assurant une véritable localité, décentralisation et mise à l’échelle des calculs. Cette thèse est donc une proposition argumentée pour dépasser les limites des architectures de type von Neumann en utilisant des principes de calcul neuronal et cellulaire. Nous restons cependant dans le cadre numérique en explorant les performances des architectures proposées sur FPGA. L’utilisation de circuits analogiques (VLSI) serait tous aussi intéressante mais n’est pas étudiée ici. Les principales contributions sont les suivantes : 1) Calcul DNF dans un environnement neuromorphique ; 2) Calcul DNF avec communication purement locale : modèle RSDNF (randomly spiking DNF) ; 3) Calcul DNF avec communication purement locale et asynchrone : modèle CASAS-DNF (cellular array of stochastic asynchronous spiking DNF)
In the constant search for design going beyond the limits of the von Neumann architecture, non conventional computing offers various solutions like neuromorphic engineering and cellular computing. Like von Neumann who roughly reproduced brain structures to design computers architecture, neuromorphic engineering takes its inspiration directly from neurons and synapses using analog substratum. Cellular computing influence comes from natural substratum (chemistry, physic or biology) imposing locality of interactions from which organisation and computation emerge. Research on neural mechanisms was able to demonstrate several emergent properties of the neurons and synapses. One of them is the attractor dynamics described in different frameworks by Amari with the dynamic neural fields (DNF) and Amit and Zhang with the continuous attractor neural networks. These neural fields have various computing properties and are particularly relevant for spatial representations and early stages of visual cortex processing. They were used, for instance, in autonomous robotics, classification and clusterization. Similarly to many neuronal computing models, they are robust to noise and faults and thus are good candidates for noisy hardware computation models which would enable to keep up or surpass the Moore law. Indeed, transistor area reductions is leading to more and more noise and the relaxation of the approx. 0% fault during production and operation of integrated circuits would lead to tremendous savings. Furthermore, progress towards many-cores circuits with more and more cores leads to difficulties due to the centralised computation mode of usual parallel algorithms and their communication bottleneck. Cellular computing is the natural answer to these problems. Based on these different arguments, the goal of this thesis is to enable rich computations and applications of dynamic neural fields on hardware substratum with neuro-cellular models enabling a true locality, decentralization and scalability of the computations. This work is an attempt to go beyond von Neumann architectures by using cellular and neuronal computing principles. However, we will stay in the digital framework by exploring performances of proposed architectures on FPGA. Analog hardware like VLSI would also be very interesting but is not studied here. The main contributions of this work are : 1) Neuromorphic DNF computation ; 2) Local DNF computations with randomly spiking dynamic neural fields (RSDNF model) ; 3) Local and asynchronous DNF computations with cellular arrays of stochastic asynchronous spiking DNFs (CASAS-DNF model)

In this work, a Representative Volume Element (RVE) generator based on the distance fields of arbitrary shaped inclusion packing is used to obtain morphologies of open-foam materials. When the inclusions are spherical, the tessellations of the resultant packing creates morphologies that are similar to physical foam samples in terms of their face-to-pore ratio, edge-to-face ratio and strut length distribution among others. Functions that combine the distance fields can be used to obtain the tessellations along with the necessary variations in the strut geometry and extract these open-foam morphologies. It is also possible to replace the inclusion packing with a predefined set of inclusions that are directly extracted from CT-scan based images.The use of discrete level-set functions results in steep discontinuities in the distance function derivatives. A multiple level-set based approach is presented that can appropriately capture the sharp edges of the open-foam struts from the resultant distance fields. Such an approach can circumvent the discontinuities presented by the distance fields which might lead to spurious stress concentrations in a material behavior analysis.The individual cells are then extracted as inclusion surfaces based on said combinations of the distance functions and their modifications. These surfaces can be joined together to obtain the final geometry of the open-foam morphologies. The physical attributes of the extracted geometries are compared to the experimental data. A statistical comparison is presented outlining the various features. The study is extended to morphologies that have been extracted using CT-scan images. With the help of mesh optimization tools, surface triangulations can be obtained, merged and developed as finite element (FE) models. The models are ready to use in a multi-scale study to obtain the homogenized material behavior. The upscaling can help assess the practical applications of these models by comparing with experimental data of physical samples. The material behavior of the RVEs are also compared with the experimental observations. To increase the computational efficiency of the study, a neural network based surrogate is presented that can replace the micro-scale boundary value problem (BVP) in the multi-scale analysis. The neural networks are built with the help of modules that are specifically designed to predict history dependent behavior and are called Recurrent Neural Networks (RNN). The surrogates are trained to take into account the randomness of the loading that complex material undergo during any given material behavior analysis.
Dans ce travail, un générateur de volumes élémentaires représentatifs (VER) basé sur les champs de distance d'un agrégat d'inclusions de forme arbitraire est développé dans le cadre de matériaux moussés à structure ouverte. Lorsque les inclusions sont sphériques, la tessellation de l'agrégat résulte en des morphologies similaires aux échantillons de mousse physique en termes de rapports des nombres de face par pores et de bords par faces, ainsi que de la distribution de la longueur des entretoises, entre autres. Les fonctions qui combinent les champs de distance peuvent être utilisées pour obtenir des tesselations avec les variations nécessaires aux géométries des entretoises et extraire ces morphologies de mousse ouverte. Il est également possible de remplacer l'agrégat d'inclusions par un ensemble prédéfini d'inclusions qui sont directement extraites d'images tomographiques.L'utilisation de fonctions de niveaux discrètes entraîne de fortes discontinuités dans les dérivées des champs de distance. Une approche basée sur des ensembles de niveaux multiples est présentée qui peut capturer de manière appropriée les arêtes vives des entretoises des mousses ouvertes à partir des champs de distance résultants. Une telle approche peut contourner les discontinuités présentées par les champs de distance qui pourraient conduire à des concentrations de contraintes parasites dans une analyse ducomportement des matériaux.Les pores individuels sont ensuite extraits en tant que surfaces d'inclusions sur la base desdites combinaisons des fonctions de distance et de leurs modifications. Ces surfaces peuvent être réunies pour obtenir la géométrie finale des morphologies de mousse ouverte. Les attributs physiques des géométries extraites sont comparés aux données expérimentales. Une comparaison statistique est présentée décrivant les différentes caractéristiques. L'étude est étendue aux morphologies qui ont été extraites à l'aide d'images tomographiques.À l'aide d'outils d'optimisation de maillage, les triangulations des surfaces peuvent être obtenues, fusionnées et développées sous forme de modèles d'éléments finis (FE). Les modèles sont prêts à être utilisés dans une étude multi-échelle pour obtenir le comportement homogénéisé du matériau. La mise à l'échelle peut aider à évaluer les applications pratiques de ces modèles en les comparant aux données expérimentales d'échantillons physiques. Le comportement des matériaux des VERs est également comparé aux observations expérimentales.Pour augmenter l'efficacité de calcul de l'étude, un modèle de substitution basé sur un réseau neuronal est présenté. Ce modèle peut remplacer le problème aux valeurs limites à l'échelle micro dans une analyse multi-échelle. Les réseaux de neurones sont construits à l'aide de modules spécialement conçus pour prédire le comportement dépendant de l'histoire et sont appelés réseaux de neurones récurrents (RNN). Les modèles de substitution sont entrainés pour prendre en compte le caractère aléatoire du chargement que subit un matériau complexe lors d'une analyse de comportement d'un matériau.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished

Neural fields models have been developed. to emulate large scale brain dynamics. They exhibit similar types of patterns as observed in real cortical tissue, such as travelling waves and persistent localised activity. The study of neural field models is yet a growing field of research, and in this thesis we contribute by developing new approaches to the analysis of pattern formation. A particular focus is on interface methods in one and two spatial dimensions. In the first part of this thesis we Study the influence of inhomogeneities on the velocity of propagating waves. We examine periodically modulated connectivity functions as well as fluctuating firing thresholds. For strong inhomogeneities we observe wave propagation failure and the emergence of stable localised solutions that do not exist in the homogeneous model. In the second part we develop a method to approximate stationary localised solutions and travelling waves in neural field models with sigmoidal firing rates. In particular, we devise a scheme that approximates the slope of these solutions and yields refined results upon iteration. We calculate explicit solutions for piecewise linear and piecewise polynomial firing rates. In the third part we develop an interface approach for planar neural field models. We derive the equations of motion for a certain class of synaptic connectivity function. In the interface description the evolution of a contour, which is defined by a level set condition, is governed by the normal velocity which depends exclusively on the shape of the contour. We present results for the existence and stability of various types of patterns. The interface description is also incorporated into a numerical scheme which allows to investigate pattern formation beyond instabilities.

En els últims anys s’han produït desenvolupaments tecnològics innovadors en el camp dels implants neuronals per a aplicacions mèdiques. La comprensió de el cervell humà es considera com un dels majors reptes científics del nostre temps; com a conseqüència, estem sent testimonis d’una intensificació de la investigació en el desenvolupament de les interfícies cervell-màquina (IMC) per llegir i estimular l’activitat cerebral. No obstant això, els implants neuronals actualment disponibles ofereixen una eficàcia clínica modesta, en part a causa de les limitacions que plantegen la invasivitat dels materials. Aquests materials comprometen la resolució de la interfície, el rendiment i l’estabilitat a llarg termini dels implants. El desenvolupament d’una electrònica flexible que utilitzi materials biocompatibles és clau per al desenvolupament d’implants neuronals mínimament invasius, que puguin implantar-se de forma crònica. Un camp d’investigació molt prometedor, és l’ús de materials bidimensionals, com el grafè, per a aplicacions bioelectròniques. El transistor d’efecte de camp en solució de grafè (gSGFET) és una de d’aquestes noves tecnologies neuronals emergents. Aquests dispositius poden superar les limitacions esmentades anteriorment gràcies a les extraordinàries propietats del grafè, com ara la seva alta flexibilitat mecànica, estabilitat electroquímica, biocompatibilitat i alta sensibilitat. En aquesta tesi doctoral, s’han fabricat matrius de gSGFET i s’han optimitzat iterativament en termes de sensibilitat i relació senyal / soroll, adoptant mètodes de microfabricació a escala d’oblia. S’ha caracteritzat el soroll 1 / f en els gSGFETs i s’ha optimitzat amb un tractament UVO de la interfície metall / grafè i desacoblant el grafè del substrat utilitzant diferents nanomaterials com ara l’encapsulació del grafè amb nitrur de bor hexagonal (hBN), monocapes autoacoblades i grafè bicapa. A més, s’han fabricat amb èxit sondes neuronals epicorticals i intracorticals flexibles, que contenien matrius de gSGFET, i s’han fet enregistraments de microelectrocorticografia in vivo en rosegadors. S’han inserit dispositius intracorticals flexibles en el cervell utilitzant un protocol de reforç de la capa posterior del dispositiu amb proteïna de fibroïna de seda biorresistent. Els resultats presentats en aquesta tesi demostren la superior resolució espai-temporal dels gSGFET en comparació amb la tecnologia estàndard de microelèctrodes; en particular, la capacitat de mapejar amb alta fidelitat, l’activitat de molt baixa freqüència (ISA, <0,1 Hz) juntament amb els senyals en el típic ample de banda dels LFP. Avui dia se sap que l’activitat cerebral de molt baixa freqüència, contribueix a la fisiopatologia de diversos trastorns neurològics com el vessament cerebral, la lesió cerebral traumàtica, la migranya i l’epilèpsia. No obstant això, aquesta activitat rares vegades es registra a causa de les limitacions tècniques intrínseques dels elèctrodes convencionals acoblats a la CA. S’han obtingut mesures neuronals amb sondes de profunditat flexibles i multicanal de grafè (gDNP) en models animals desperts amb convulsions i epilèpsia. S’ha detectat i cartografiat l’AIS a través de diferents capes corticals i regions subcorticals, registrant simultàniament l’activitat epilèptica en bandes de freqüència més convencionals (1-600Hz). A més, com a part d’aquesta tesi s’ha demostrat també l’estabilitat i funcionalitat de registres a llarg termini, així com la biocompatibilitat dels gDNPs. La tecnologia bioelectrònica basada en grafè aquí descrita té el potencial d’esdevenir una eina de referència per a l’electrofisiologia d’ample de banda complet. Es preveu que aquesta tecnologia tingui un gran impacte en una comunitat àmplia i multidisciplinària que inclogui investigadors en neurotecnologia, enginyers biomèdics, neurocientífics que estudien la dinàmica cortical de banda ampla associada amb el comportament espontani i /o els estats cerebrals, així com investigadors clínics interessats en el paper de l’activitat de molt baixa freqüència en epilèpsia, els accidents cerebrovasculars i la migranya.
En los últimos años se han producido nuevos desarrollos tecnológicos en el campo de los implantes neuronales para aplicaciones médicas. La comprensión del cerebro humano se considera uno de los mayores desafíos científicos de nuestro tiempo; como consecuencia, estamos siendo testigos de una intensificación de la investigación en el desarrollo de las interfaces cerebro-máquina (IMC) para leer y estimular la actividad cerebral. No obstante, los implantes neuronales actualmente disponibles ofrecen una eficacia clínica modesta, en parte debido a las limitaciones que plantea la invasividad de los materiales. Esos materiales comprometen la resolución de la interfaz, el rendimiento y la estabilidad a largo plazo de los implantes neurales. El desarrollo de una electrónica flexible que utilice materiales biocompatibles es clave para la realización de implantes neuronales mínimamente invasivos que puedan implantarse de forma crónica. Un campo de investigación muy prometedor es el uso de materiales bidimensionales, como el grafeno, para aplicaciones bioelectrónicas. El transistor de efecto de campo en solución de grafeno (gSGFET) es una de dichas nuevas tecnologías neurales emergentes. Estos dispositivos pueden superar las limitaciones mencionadas anteriormente gracias a las extraordinarias propiedades del grafeno, como su alta flexibilidad mecánica, estabilidad electroquímica, biocompatibilidad y sensibilidad. En esta tesis doctoral, se han fabricado matrices de gSGFET y se han optimizado iterativamente en términos de sensibilidad y relación señal/ruido, adoptando métodos de microfabricación a escala de oblea. Se ha caracterizado el ruido 1/f en los gSGFETs y optimizado haciendo un tratamiento UVO en la interfaz metal/grafeno y desacoplando el canal de grafeno del sustrato utilizando diferentes nanomateriales como la encapsulación con nitruro de boro hexagonal (hBN), monocapas autoensambladas y bicapas de grafeno. Además, se han fabricado con éxito sondas neurales epicorticales e intracorticales flexibles con matrices de gSGFET y se han utilizado durante las medidas de microelectrocorticografía in vivo en roedores. Se han insertado dispositivos intracorticales flexibles en el cerebro utilizando un protocolo de refuerzo de la capa posterior de los dispositivos con proteína de fibroína de seda biorresistente. Los resultados presentados en esta tesis demuestran la superior resolución espacio-temporal de los gSGFET en comparación con la tecnología estándar de microelectrodos; en particular, referente a la capacidad de mapear con alta fidelidad, la actividad de muy baja frecuencia (ISA, < 0,1 Hz) junto con las señales en el típico ancho de banda LFP. Hoy en día se sabe que la actividad cerebral de muy baja frecuencia, contribuye a la fisiopatología de varios trastornos neurológicos como el derrame cerebral, la lesión cerebral traumática, la migraña y la epilepsia. Sin embargo, esta actividad rara vez se registra debido a las limitaciones técnicas intrínsecas de los electrodos convencionales acoplados a la CA. Se han obtenido registros con sondas neuronales de profundidad de grafeno (gDNP) en modelos animales de epilepsia. Se detectó ISA a través de diferentes capas corticales y regiones subcorticales, registrando simultáneamente la actividad epiléptica en bandas de frecuencia más convencionales (1-600Hz). Además, se ha demostrado también la evaluación de la estabilidad y funcionalidad en registros crónicos, así como la biocompatibilidad del gDNP. La tecnología bioelectrónica basada en el grafeno aquí descrita tiene el potencial de convertirse en una herramienta de referencia para la electrofisiología de ancho de banda completo. Se prevé que esta tecnología tenga un gran impacto en una comunidad amplia y multidisciplinaria que incluya investigadores en neurotecnología, ingenieros biomédicos, neurocientíficos que estudien la dinámica cortical de banda ancha asociada con el comportamiento espontáneo y/o los estados cerebrales, así como investigadores clínicos interesados en la actividad de baja frecuencia en la epilepsia, los accidentes cerebrovasculares y la migraña.
Recent years have witnessed novel technology developments of neural implants for medical applications which are expected to pave the way to unveil functionalities of the central nervous system. Understanding the human brain is commonly considered one of the biggest scientific challenges of our time; as a consequence, we are witnessing an intensified research in the development of brain-machine-interfaces (BMIs), which would allow us to both read and stimulate brain activity. Nevertheless, currently available neural implants offer a modest clinical efficacy, partly due to the limitations posed by the invasiveness of the implants materials and technology and by the metals used at the electrical interface with the tissue. Such materials compromise the interfacing resolution, the performance and the long term stability of neural implants. Development of flexible electronics using biocompatible materials is key for the realisation of minimally invasive neural implants, which can be chronically implanted without causing rejection from the immune system. A relatively young yet very promising research field, that is increasingly drawing attention is the use of two dimensional materials, such as graphene, for bioelectronic applications. Graphene solution-gated field effect transistor (gSGFET) is one of several emerging new neural technologies. These devices can overcome the above-mentioned limitations thanks to the outstanding properties of graphene, such as mechanical flexibility, electrochemical inertness, biocompatibility and high sensitivity. In this PhD thesis, arrays of gSGFETs have been fabricated and iteratively optimized in terms of sensitivity and signal-to-noise ratio, adopting wafer-scale micro-fabrication methods. The 1/f noise in gSGFETs has been characterised and the optimisation of both, contact and channel noises was achieved by UVO-treatment at the metal/graphene interface, as well as by decoupling the graphene channel from the substrate, using different nanomaterials such as graphene encapsulation with hexagonal boron nitride (hBN), self assembled monolayers and double transferred graphene. Moreover, flexible and ultra-thin epicortical and intracortical neural probes, containing arrays of gSGFETs, have been successfully fabricated and used during in vivo microelectrocorticography recordings in anaesthesized and awake rodents. Flexible intracortical devices were inserted into the brain using a back-coating stiffening protocol with bioresobable silk fibroin protein, developed during this PhD thesis. The results presented in this PhD demonstrate the superior spatio-temporal resolution of gSGFETs compared to standard microelectordes technology; particularly the ability to map with high fidelity, infraslow activity (ISA, < 0.1 Hz) together with signals in the typical local field potential bandwidth. Today it is known that infraslow brain activity, including spreading depolarisations, contribute to the pathophysiology of several neurological disorders such as stroke, traumatic brain injury, migraine and epilepsy. However, this activity is seldom recorded due to intrinsic technical limitations of conventional AC-coupled electrodes. To demonstrate the usefulness of the developed flexible gSGFET arrays technology, recordings have been obtained with multichannel flexible graphene depth neural probes (gDNP) in relevant awake animal models of seizures and established epilepsy. ISA was detected and mapped through different cortical layers and subcortical regions, whilst simultaneously recording epileptiform activity in more conventional frequency bands (1-600Hz). Furthermore, the assessment of the long term recording stability and functionality, as well as biocompatibility of the gDNP has also been demonstrated as part of this thesis. The graphene based bioelectronic technology here described has the potential to become a gold standard tool for full bandwidth electrophysiology. This technology is envisioned to have a great impact on a broad and multidisciplinary community including neurotechnology researchers, biomedical engineers, neuroscientists studying wide-band cortical dynamics associated with spontaneous behaviour and/or brain states, as well as clinical researchers interested in the role of infraslow activity in epilepsy, stroke and migraine.

Current theories and mechanisms of nerve impulse propagation inside neurons do not give a satisfactory explanation to the nanoscale processes that occur inside the membrane. Propagation at the dendritic zones, and at the myelinated and unmyelinated axons, presents effects and events that, despite numerical calculations of current models fit with biological results obtained with macroscopic measurements, are far from giving an explanation to the real operation of biological mechanisms and to the effects and behaviors that the involved biological elements support. Current biological knowledge on the structure and behavior of biological membranes, as well as on the structure and function of voltage-regulated ion channels, allows us to consider alternatives to existing propagation mechanisms, giving explanation to the effects and behavior from both nanoscale and macroscopic level. This theoretical study proposes intra-membrane electric field propagation as a feasible propagation mechanism capable to explain both macroscopic and nanometric effects. With current theories and mechanisms, generation, propagation and regeneration of the nerve impulse is based on the action potential propagation effect. The action potential can be defined as the temporal imbalance that affects the membrane potential during nerve impulse propagation, as a consequence of ion channel opening. Progress on the neural membrane biological structure knowledge has allowed to propose a new hypothesis on the action potential operation. Besides the membrane potential variation, the electric charge that crosses the membrane generates an electric field that propagates inside the cell membrane, activating the closer ionic channels. This mechanism gives an explanation compatible with existing biological components and with other effects hard to explain with the standard current mechanisms. In addition, the proposed intra-membrane electric field propagation mechanism allows for a different explanation of the action potential evolution, as well as ion traffic level more in line with the capabilities of the biological reality, both on the ion traffic level and energy consumption. The proposed mechanism allows to explain the so-called saltatory action potential propagation on myelinated axons, and to apply the saltatory propagation to unmyelinated axons. In the latter, as short-distance steps; in the former, as long-distance jumps, also giving a coherent answer to the constant-time propagation independent of the distance between nodes of Ranvier. The proposed mechanism suggests that the effects the action potential presents are more the result of a charge displacement inside sodium and potassium channels than a current that crosses ion channels. An important factor to consider in this mechanism is the energy consumption minimization, a fundamental biological premise for maximum optimization of process operation.
Les actuals teories i mecanismes de propagació de l'impuls nerviós per l'interior de les neurones no donen una explicació suficientment satisfactòria ni als principis físics als quals estan subjectes, ni als processos nanomètrics que es produeixen al seu interior. La propagació a les zones dendrítiques i als axons mielinitzats i no mielinitzats presenten efectes i esdeveniments que si bé el càlcul numèric dels actuals models concorda amb els resultats biològics obtinguts amb mesures macroscòpiques, estan lluny de donar una explicació al funcionament real dels mecanismes biològics i als efectes i comportaments que presenten la resta d'elements biològics participants que hi donen suport. Els actuals coneixements sobre l'estructura i comportament de les membranes biològiques, així com el coneixement sobre l'estructura i el funcionament dels canals iònics regulats per voltatge, ens permet plantejar una opció alternativa als mecanismes existents de propagació, donant explicació als efectes i comportaments tant des d'una escala nanomètrica com des d'una escala macroscòpica. En aquest treball teòric es proposa la propagació de camps elèctrics intra-membrana com un mecanisme possible de propagació amb la capacitat de donar explicació tant als efectes macroscòpics com als nanomètrics. Amb les teories i mecanismes actuals, la generació, la propagació i la reemissió de l'impuls nerviós es fonamenta en un efecte anomenat potencial d'acció. El potencial d'acció el podem definir com el desequilibri temporal que pateix el potencial de membrana, durant la propagació de l'impuls nerviós, i que és conseqüència de l'obertura dels canals iònics. El progrés en el coneixement sobre les estructures biològiques existents a les membranes neuronals ha permès plantejar una nova hipòtesi sobre el funcionament del potencial d'acció. A més de la variació del potencial de membrana, la càrrega elèctrica que travessa la membrana genera un camp elèctric que es propaga per l'interior de la membrana cel·lular, activant els canals iònics propers. Aquest mecanisme aporta una explicació consistent, compatible amb els components biològics existents i amb altres efectes difícilment explicables amb els mecanismes estàndard actuals. A més, el mecanisme de propagació per camp elèctric intra-membrana proposat permet donar una explicació diferent de l'evolució dels potencials d'acció, així com un nivell de tràfic iònic més en consonància amb les capacitats de la realitat biològica, tant en el nivell de tràfic existent com en el consum energètic. El mecanisme proposat permet explicar l'anomenada propagació saltatòria del potencial d'acció dels axons mielinitzats, i també aplicar la propagació saltatòria als axons no mielinitzats. En aquest cas com una propagació saltatòria de salts curts; per als axons mielinitzats, com una propagació saltatòria de salts llargs, donant també una resposta coherent a la propagació en temps constant independent de la distància entre nodes de Ranvier. El mecanisme proposat permet plantejar que els efectes que presenta el potencial d'acció són més el resultat d'un desplaçament de càrrega a l'interior dels canals de sodi i de potassi, que d'un corrent que realment travessa els canals iònics. Un factor important a tenir en compte en el mecanisme proposat és la minimització del cost energètic, premissa fonamental de la biologia per a la màxima optimització en el funcionament de processos.

A computationally efficient, biophysically-based model of single-neuron dynamics is studied. The model equations are shown to provide a wide array of physiological dynamics in terms of spiking patterns, bursting, sub-threshold oscillations, and chaotic firing. Despite its simplicity, the model is capable of simulating an extensive range of spiking patterns. This simple model is suitable for use in neural field theory . A variant of the single-neuron model is incorporated into a physiologically-based corticothalamic neural field model to study slow wave oscillations including cortical UP and DOWN states in deep sleep. EEG spectral features in wake and sleep are reproduced. Furthermore, it is found that there is a continuous cycle between the UP and DOWN regimes, rather than a flip-flop between discrete states. The mechanisms underlying generalized seizures are explored with the corticothalamic neural field model. Changes leading to pathological seizure states are studied and it is found that absence seizures arise from instabilities in the system which replicate experimental studies in animal models and clinical studies. A neural field model for the isolated cortex that consists of three neural populations, excitatory, inhibitory, and excitatory bursting is studied. Mechanisms by which an isolated cortex gives rise to seizure-like waveforms are investigated. These mechanisms have been studied experimentally and are thought to underly pathological EEG waveforms on the cortex independent of the thalamus. It is shown that similar athalamic ictal discharges arise in the model.