Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 8454 for “"neural"”.
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Neural Sympathy: Towards Interfaces Compatible with Neural Plasticity
… interfaces (BMIs) enable a direct mapping from neural activity to interactions with the world. They have clinically validated applications for patients with severe loss of motor function, but also offer valuable scientific tools for probing the neural mechanisms of how actions are learned, …
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Multi-objective evolutionary neural architecture search for recurrent neural networks
Artificial neural network (ANN) architecture design is a nontrivial and time-consuming task that often requires a high level of human expertise. Neural architecture search (NAS) serves to automate the design of ANN architectures, and has proven to be successful in finding ANN architectures that can …
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Fuzzy neural networks
… neurons are modeled with artificial neural networks (ANNs or NNs). Neural networks, mathematically speaking, are a system of linked parallel equations that are solved simultaneously and iteratively. Initial research can be found in papers by McCulloch-Pitts (1943), Hebb (1949), …
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Neural Diagrammatic Reasoning
… rules? To answer this question, I propose Neural Diagrammatic Reasoning, a new family of diagrammatic reasoning systems which does not have the drawbacks of mechanised reasoning systems. The new systems are based on deep neural networks, a recently popular machine learning method that …
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Neural bus networks
… the best results. This research compares several neural network architectures trained on historical data from Boston buses. Three models are trained: multilayer perceptron, convolutional neural network and recurrent neural network. Recurrent neural networks show the best performance when compared …
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Evolutionary neural networks
To create neural networks that work, one needs to specify a structure and the interconnection weights between each pair of connected computing elements. The structure of a network can be selected by the designer depending on the application, although the selection of interconnection weights is a …
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TWIST1: a subtle modulator of neural differentiation and neural tube formation
… epiblast precursor cells through Neurulation. Neural induction can be studied in its main aspects in vitro. However, the process is poorly understood, especially in regard to when and how a cell becomes specified, and then committed, to be a neural cell. It is, on the other hand, well …
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Approximate Inference in Bayesian Neural Networks and Translation Equivariant Neural Processes
… probabilistic machine learning models: Bayesian neural networks and neural processes. Bayesian neural networks are a classical model that has been the subject of research since the 1990s. They rely on Bayesian inference to represent uncertainty in the weights of a neural network. On the other …
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Longitudinal tracking of neural vascular recovery post microinfarct using multimodal neural platform
… strokes in aged mice to investigate how neural activities respond to such small-scale occlusion. We used ultra-flexible nanoelectrode thread probes, two-photon imaging, and speckle imaging to track neural activities, microvascular structure, and regional cerebral blood flow longitudinally …
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A tailored biocompatible neural interface for long term monitoring in neural networks
Neural interface electrodes that can record from neurons in the brain for long periods of time will be of great importance to unravel how the brain accomplishes its functions. However, current electrodes usually cause significant glia reactions and loss of neurons within the adjacent brain …
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Reductions of ReLU neural networks to linear neural networks and their applications
Deep neural networks are the main subject of interest in the study of theoretical deep learning, which aims to rigorously explain the incredible performance of these function classes in practice. Although a lot are understood about deep linear network (neural network with all linear activations), …
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Evolving Learning Neural Networks
… has long been used to modify the artificial neural network in order to perform classification tasks. However, the standard fully connected layered design is often inadequate when performing such tasks. We show that evolution can be used to design an artificial neural network that learns …
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Neural Representation for robotics
… or incomplete geometric representations. Neural representations have recently demonstrated exceptional capabilities in Novel View Synthesis (NVS), effectively handling many scenarios where conventional depth cameras struggle. Moreover, unlike traditional methods requiring pre-existing CAD …
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Robust Neural Machine Translation
<p>This thesis aims for general robust Neural Machine Translation (NMT) that is agnostic to the test domain. NMT has achieved high quality on benchmarks with closed datasets such as WMT and NIST but can fail when the translation input contains noise due to, for example, mismatched domains or …
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Self-Modeling Neural Systems
… work, we consider whether it is possible for a neural system that obeys certain biological constraints to solve optimal control problems. We exhibit a simple method to train a different kind of internal model, a neural network model of the Jacobian of the plant, and we integrate the internal …
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Salience-affected neural networks
… using an ANN, creating a salience-affected neural network (SANN). We adapt an ANN to embody the capacity to respond to an input salience signal and to produce a reverse salience signal during testing. The input salience signal applied during training to each node has the effect of varying …
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Massively parallel neural computation
… focusses on real-time computation of large neural networks using the Izhikevich spiking neuron model. Neural computation has been described as “embarrassingly parallel” as each neuron can be thought of as an independent system, with behaviour described by a mathematical model. However, the …
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