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Thematische Bibliographien / Neural computer

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Dissertationen

Auswahl der wissenschaftlichen Literatur zum Thema „Neural computer“

Autor: Grafiati

Veröffentlicht am 30. Juni 2021

Zuletzt aktualisiert am 6. Februar 2022

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Dissertationen zum Thema "Neural computer"

1

Somers, Harriet. "A neural computer." Thesis, University of York, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362021.

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2

Churcher, Stephen. "VLSI neural networks for computer vision." Thesis, University of Edinburgh, 1993. http://hdl.handle.net/1842/13397.

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Recent years have seen the rise to prominence of a powerful new computational paradigm - the so-called artificial neural network. Loosely based on the microstructure of the central nervous system, neural networks are massively parallel arrangements of simple processing elements (<i>neurons</i>) which communicate with each other through variable strength connections (<i>synapses</i>). The simplicity of such a description belies the complexity of calculations which neural networks are able to perform. Allied to this, the emergent properties of noise resistance, fault tolerance, and large data ba

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3

Khan, Altaf Hamid. "Feedforward neural networks with constrained weights." Thesis, University of Warwick, 1996. http://wrap.warwick.ac.uk/4332/.

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The conventional multilayer feedforward network having continuous-weights is expensive to implement in digital hardware. Two new types of networks are proposed which lend themselves to cost-effective implementations in hardware and have a fast forward-pass capability. These two differ from the conventional model in having extra constraints on their weights: the first allows its weights to take integer values in the range [-3,3] only, whereas the second restricts its synapses to the set {-1,0,1} while allowing unrestricted offsets. The benefits of the first configuration are in having weights w

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4

Kulakov, Anton. "Multiprocessing neural network simulator." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/348420/.

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Over the last few years tremendous progress has been made in neuroscience by employing simulation tools for investigating neural network behaviour. Many simulators have been created during last few decades, and their number and set of features continually grows due to persistent interest from groups of researchers and engineers. A simulation software that is able to simulate a large-scale neural network has been developed and presented in this work. Based on a highly abstract integrate-and-fire neuron model a clock-driven sequential simulator has been developed in C++. The created program is a

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5

Durrant, Simon. "Negative correlation in neural systems." Thesis, University of Sussex, 2010. http://sro.sussex.ac.uk/id/eprint/2387/.

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In our attempt to understand neural systems, it is useful to identify statistical principles that may be beneficial in neural information processing, outline how these principles may work in theory, and demonstrate the benefits through computational modelling and simulation. Negative correlation is one such principle, and is the subject of this work. The main body of the work falls into three parts. The first part demonstrates the space filling and accelerated central limit convergence benefits of negative correlation, both generally and in the specific neural context of V1 receptive fields. I

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6

Baker, Thomas Edward. "Implementation limits for artificial neural networks." Full text open access at:, 1990. http://content.ohsu.edu/u?/etd,268.

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7

Lam, Yiu Man. "Self-organized cortical map formation by guiding connections /." View abstract or full-text, 2004. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202005%20LAM.

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8

Adamu, Abdullahi S. "An empirical study towards efficient learning in artificial neural networks by neuronal diversity." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33799/.

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Artificial Neural Networks (ANN) are biologically inspired algorithms, and it is natural that it continues to inspire research in artificial neural networks. From the recent breakthrough of deep learning to the wake-sleep training routine, all have a common source of drawing inspiration: biology. The transfer functions of artificial neural networks play the important role of forming decision boundaries necessary for learning. However, there has been relatively little research on transfer function optimization compared to other aspects of neural network optimization. In this work, neuronal dive

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9

McMichael, Lonny D. (Lonny Dean). "A Neural Network Configuration Compiler Based on the Adaptrode Neuronal Model." Thesis, University of North Texas, 1992. https://digital.library.unt.edu/ark:/67531/metadc501018/.

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A useful compiler has been designed that takes a high level neural network specification and constructs a low level configuration file explicitly specifying all network parameters and connections. The neural network model for which this compiler was designed is the adaptrode neuronal model, and the configuration file created can be used by the Adnet simulation engine to perform network experiments. The specification language is very flexible and provides a general framework from which almost any network wiring configuration may be created. While the compiler was created for the specialized ada

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10

Yang, Horng-Chang. "Multiresolution neural networks for image edge detection and restoration." Thesis, University of Warwick, 1994. http://wrap.warwick.ac.uk/66740/.

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One of the methods for building an automatic visual system is to borrow the properties of the human visual system (HVS). Artificial neural networks are based on this doctrine and they have been applied to image processing and computer vision. This work focused on the plausibility of using a class of Hopfield neural networks for edge detection and image restoration. To this end, a quadratic energy minimization framework is presented. Central to this framework are relaxation operations, which can be implemented using the class of Hopfield neural networks. The role of the uncertainty principle in

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