Network dynamics are thought to be the substrate of brain information processing and of mental representations. Moreover, network-wide dysfunctions are recognized to be at the core of several psychiatric and neurodegenerative disorders. Yet, our ability to target specific networks for functional or genetic manipulations remains limited. The development of monosynaptically-restricted Rabies virus, G-deleted Rabies virus (ΔG-Rabies), has greatly facilitated the anatomical investigation of neural circuits, revealing the network synaptic structure upstream of defined neuronal populations. However, the inherent cytotoxicity of the Rabies virus largely restrains its use to the mere structural characterisation of neural networks. To overcome this limitation, I generated novel tools that allow the manipulation of neural networks for the entire life of the animal, without affecting neuronal and circuit properties. I first developed a viral system obtained by engineering the Rabies virus genome to eliminate its cytotoxicity. This led to the generation of a Self-inactivating Rabies virus (SiR) that transcriptionally disappears from the infected neurons while leaving permanent genetic access to the traced network. I showed that SiR provides a virtually unlimited temporal window for the functional manipulation of neural circuits in vivo without adverse effects on neuronal physiology. To further expand our ways of intervening on neural networks function I then developed a completely virus-free system, named Genetically-Encoded TransSynaptic Shuttle (GETSS), which is the only specific genetically-encoded transsynaptic tracer to date. In this thesis, I established novel approaches that provide, for the first time, the functional and genetic access to traced network elements in vivo for the lifetime of the animal, with no cytotoxic effects, no changes in the electrophysiological properties of the traced neurons and no adverse effects on network function. This opens new horizons in the functional investigation of neural circuits and potentially represent the first approaches to experimentally monitor neural circuit remodelling in vivo.

Computational intelligence techniques, such as artificial neural networks (ANNs), have been widely used to improve the performance of power system monitoring and control. Although inspired by the neurons in the brain, ANNs are largely different from living neuron networks (LNNs) in many aspects. Due to the oversimplification, the huge computational potential of LNNs cannot be realized by ANNs. Therefore, a more brain-like artificial neural network is highly desired to bridge the gap between ANNs and LNNs. The focus of this research is to develop a biologically inspired artificial neural network (BIANN), which is not only biologically meaningful, but also computationally powerful. The BIANN can serve as a novel computational intelligence tool in monitoring, modeling and control of the power systems. A comprehensive survey of ANNs applications in power system is presented. It is shown that novel types of reservoir-computing-based ANNs, such as echo state networks (ESNs) and liquid state machines (LSMs), have stronger modeling capability than conventional ANNs. The feasibility of using ESNs as modeling and control tools is further investigated in two specific power system applications, namely, power system nonlinear load modeling for true load harmonic prediction and the closed-loop control of active filters for power quality assessment and enhancement. It is shown that in both applications, ESNs are capable of providing satisfactory performances with low computational requirements. A novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. A comprehensive survey of the spiking models of living neurons as well as the coding approaches is presented to review the state-of-the-art in BIANN research. The proposed BIANNs are based on spiking models of living neurons with adoption of reservoir-computing approaches. It is shown that the proposed BIANNs have strong modeling capability and low computational requirements, which makes it a perfect candidate for online monitoring and control applications in power systems. BIANN-based modeling and control techniques are also proposed for power system applications. The proposed modeling and control schemes are validated for the modeling and control of a generator in a single-machine infinite-bus system under various operating conditions and disturbances. It is shown that the proposed BIANN-based technique can provide better control of the power system to enhance its reliability and tolerance to disturbances. To sum up, a novel, more brain-like artificial neural network, i.e. biologically inspired artificial neural network (BIANN), is proposed in this dissertation to bridge the gap between ANNs and LNNs and provide a novel tool for monitoring and control in power systems. It is clearly shown that the proposed BIANN-based modeling and control schemes can provide faster and more accurate control for power system applications. The conclusions, the recommendations for future research, as well as the major contributions of this research are presented at the end.

In this thesis, an intelligent controller for gun and/or sight stabilization of turret subsystems is developed using artificial neural networks. A classical proportional, integral and derivative (PID) controller equipped with a non-linear unbalance compensation algorithm is used as the low-level controller. The gains of this PID controller are tuned using a multilayered back-propagation neural network. These gains are modeled as a function of the error between the command and feedback signals and this model is generated by the function fitting property of neural networks as an estimate. The network is called as the &ldquo / Neural PID Tuner&rdquo / and it takes the current and previous errors as inputs and outputs the PID gains of the controller. Columb friction is the most important non-linearity in turret subsystems that heavily lower the efficiency of the controller. Another multilayered back-propagation neural network is used in order to increase the performance of the PID controller by identifying and compensating this Columb friction. This network utilizes the error between the output of the PID controller driving the physical system with Columb friction and the output of the identical PID controller driving a virtual equivalent linear system without Columb friction. The linear dynamics of the physical system is identified using a single layer linear neural network with pure linear activation function and the equivalent virtual linear system is emulated using this identification. The proposed methods are applied to both computer simulations and hardware experimental setup. In addition, sensitivity and performance analysis are performed both by using the mathematical model and hardware experimental setup.

The work we present in this thesis is a series of studies of how heterogeneities in coupling affect the synchronization of coupled neural oscillators. We begin by examining how heterogeneity in coupling strength affects the equilibrium phase difference of a pair of coupled, spiking neurons when compared to the case of identical coupling. This study is performed using pairs of Hodgkin-Huxley and Wang-Buzsaki neurons. We find that heterogeneity in coupling strength breaks the symmetry of the bifurcation diagrams of equilibrium phase difference versus the synaptic rate constant for weakly coupled pairs of neurons. We observe important qualitative changes such as the loss of the ubiquitous in-phase and anti-phase solutions found when the coupling is identical and regions of parameter space where no phase locked solution exists. Another type of heterogeneity can be found by having different types of coupling between oscillators. Synaptic coupling between neurons can either be exciting or inhibiting. We examine the synchronization dynamics when a pair of neurons is coupled with one excitatory and one inhibitory synapse. We also use coupled pairs of Hodgkin-Huxley neurons and Wang-Buzsaki neurons for this work. We then explore the existance of 1:n coupled states for a coupled pair of theta neurons. We do this in order to reproduce an observed effect called quantal slowing. Quantal slowing is the phenomena where jumping between different $1:n$ coupled states is observed instead of gradual changes in period as a parameter in the system is varied. All of these topics fall under the general heading of coupled, non-linear oscillators and specifically weakly coupled, neural oscillators. The audience for this thesis is most likely going to be a mixed crowd as the research reported herein is interdisciplinary. Choosing the content for the introduction proved far more challenging than expected. It might be impossible to write a maximally useful introductory portion of a thesis when it could be read by a physicist, mathematician, engineer or biologist. Undoubtedly readers will find some portion of this introduction elementary. At the risk of boring some or all of my readers we decided it was best to proceed so that enough of the mathematical (biological) background is explained in the introduction so that a biologist (mathematician) is able to appreciate the motivations for the research and the results presented. We begin with a introduction in nonlinear dynamics explaining the mathematical tools we use to characterize the excitability of individual neurons, as well as oscillations and synchrony in neural networks. The next part of the introductory material is an overview of the biology of neurons. We then describe the neuron models used in this work and finally describe the techniques we employ to study coupled neurons.

Este trabalho propõe uma abordagem baseada em redes neurais artificiais para estimar a velocidade do motor de indução aplicado no controle escalar a laço fechado. Os motores de indução têm grande importância nos mais diversos setores industriais por sua robustez e baixo custo. Assim, quando a carga acoplada ao eixo necessita do controle de velocidade, parte das estratégias de controle e acionamento são baseadas na estimativa de velocidade do eixo do motor. A medida direta da velocidade diminui a robustez comprometendo o sistema de acionamento e controle bem como o aumento do custo de implementação. A proposta deste trabalho consiste em apresentar uma metodologia alternativa às tradicionais para estimativa de velocidade do motor de indução trifásico acionado por um inversor fonte de tensão utilizando modulação espacial vetorial na estratégia de controle escalar. Resultados de simulação e experimentais são apresentados para validar o método proposto com o motor submetido a variações de velocidade e torque de carga, os quais demostraram ser bem promissores. / This work proposes an artificial neural network approach to estimate the induction motor speed applied in a closed-loop scalar control. The induction motor has a great importance in many industrial sectors for the robustness and low cost. Thus, when the load coupled to the axis needs speed control, some of the drive and control strategies are based on the estimated axis speed of the motor. The direct measurement of this quantity reduces its robustness, compromises the driver system and control as well as it increases the implementation cost. The propose of this work is to present an alternative methodology for speed estimate of three phase induction motor driven by a voltage source inverter using space vector modulation in the scalar control strategy. Simulation and experimental results are presented to validate the performance of the proposed method under motor load torque and speed reference set point variations, which show very promising.

No description available.

Indiana University-Purdue University Indianapolis (IUPUI) / Many complex processes can be viewed as sequential events on a network. In this thesis, we study the interplay between a network and the event sequences on it. We first focus on predicting events on a known network. Examples of such include: modeling retweet cascades, forecasting earthquakes, and tracing the source of a pandemic. In specific, given the network structure, we solve two types of problems - (1) forecasting future events based on the historical events, and (2) identifying the initial event(s) based on some later observations of the dynamics. The inverse problem of inferring the unknown network topology or links, based on the events, is also of great important. Examples along this line include: constructing influence networks among Twitter users from their tweets, soliciting new members to join an event based on their participation history, and recommending positions for job seekers according to their work experience. Following this direction, we study two types of problems - (1) recovering influence networks, and (2) predicting links between a node and a group of nodes, from event sequences.

The thesis deals with design of novel circuit structure suitable for hardware implementations of feedforward neural networks. The structure utilizes innovative data bus structure. The main contribution of the structure is in optimization of the utilization of implemented computing units. Proposed architecture is flexible and suitable for implementations of variety of feedforward neural network structures.

Description of the cell cycle features of neural progenitors during late stages of neurogenesis in a gyrencephalic mammal, the ferret.

Bolagsverket, a Swedish government agency receives cases both in paper form via mail, document form via e-mail and also digital forms. These cases may be about registering people in a company, changing the share capital, etc. However, handling and confirming all these papers can be time consuming, and it would be beneficial for Bolagsverket if this process could be automated with as little human input as possible. This thesis investigates if it is possible to identify whether a paper contains a signature or not by using artificial intelligence (AI) and convolutional neural networks (CNN), and also if it is possible to determine how many signatures a given paper has. If these problems prove to be solvable, it could potentially lead to a great benefit for Bolagsverket. In this paper, a residual neural network (ResNet) was implemented which later was trained on sample data provided by Bolagsverket. The results demonstrate that it is possible to determine whether a paper has a signature or not with a 99% accuracy, which was tested on 1000 images where the model was trained on 8787 images. A second ResNet architecture was implemented to identify the number of signatures, and the result shows that this was possible with an accuracy score of 94.6%.