Learning, Optimization and Data Translation with Deep Neural Networks

Neural networks have been intensively studied as machine learning models and widely applied in various areas. This thesis investigates three problems related to the theory and application of neural networks. First, we analyze a learning scheme for neural networks that uses random weights in the backpropagation training algorithm, which is considered to be more biologically plausible than the standard training procedure. We establish theory that shows the convergence of the loss and the alignment between the forward weights of the network and the random weights used in the backward pass. Second, we study a family of optimization problems where the objective involves a trained generative network, with the goal of inverting the network. We introduce a novel algorithm that takes advantage of a sequential optimization technique to deal with the problem of non-convexity. The third part of this thesis is an application of modern neural network models to certain problems in neuroscience. We analyze data that contains two concurrent imaging modalities of the brain activity in mice, and build translation models to predict one modality the other. Our study is one of the first examples of advanced machine learning models applied to concurrent multi-model brain imaging data and demonstrates the potential of deep neural networks in the emerging area of neuroscience

Emergent organization of receptive fields in networks of excitatory and inhibitory neurons

Local patterns of excitation and inhibition that can generate neural waves are studied as a computational mechanism underlying the organization of neuronal tunings. Sparse coding algorithms based on networks of excitatory and inhibitory neurons are proposed that exhibit topographic maps as the receptive fields are adapted to input stimuli. Motivated by a leaky integrate-and-fire model of neural waves, we propose an activation model that is more typical of artificial neural networks. Computational experiments with the activation model using both natural images and natural language text are presented. In the case of images, familiar "pinwheel" patterns of oriented edge detectors emerge; in the case of text, the resulting topographic maps exhibit a 2-dimensional representation of granular word semantics. Experiments with a synthetic model of somatosensory input are used to investigate how the network dynamics may affect plasticity of neuronal maps under changes to the inputs

A study of electrodischarge machining–pulse electrochemical machining combined machining for holes with high surface quality on superalloy

Noncircular holes on the surface of turbine rotor blades are usually machined by electrodischarge machining. A recast layer containing numerous micropores and microcracks is easily generated during the electrodischarge machining process due to the rapid heating and cooling effects, which restrict the wide applications of noncircular holes in aerospace and aircraft industries. Owing to the outstanding advantages of pulse electrochemical machining, electrodischarge machining–pulse electrochemical machining combined technique is provided to improve the overall quality of electrodischarge machining-drilled holes. The influence of pulse electrochemical machining processing parameters on the surface roughness and the influence of the electrodischarge machining–pulse electrochemical machining method on the surface quality and accuracy of holes have been studied experimentally. The results indicate that the pulse electrochemical machining processing time for complete removal of the recast layer decreases with the increase in the pulse electrochemical machining current. The low pulse electrochemical machining current results in uneven dissolution of the recast layer, while the higher pulse electrochemical machining current induces relatively homogeneous dissolution. The surface roughness is reduced from 4.277 to 0.299 µm, and the hole taper induced by top-down electrodischarge machining process was reduced from 1.04° to 0.17° after pulse electrochemical machining. On account of the advantages of electrodischarge machining and the pulse electrochemical machining, the electrodischarge machining–pulse electrochemical machining combined technique could be applied for machining noncircular holes with high shape accuracy and surface quality

Critical Role of HAX-1 in Promoting Avian Influenza Virus Replication in Lung Epithelial Cells

The PB1-F2 protein of influenza A virus has been considered a virulence factor, but its function in inducing apoptosis may be of disadvantage to viral replication. Host mechanisms to regulate PB1-F2-induced apoptosis remain unknown. We generated a PB1-F2-deficient avian influenza virus (AIV) H9N2 and found that the mutant virus replicated less efficiently in human lung epithelial cells. The PB1-F2-deficient virus produced less apoptotic cells, indicating that PB1-F2 of the H9N2 virus promotes apoptosis, occurring at the early stage of infection, in the lung epithelial cells. To understand how host cells regulate PB1-F2-induced apoptosis, we explored to identify cellular proteins interacting with PB1-F2 and found that HCLS1-associated protein X-1 (HAX-1), located mainly in the mitochondria as an apoptotic inhibitor, interacted with PB1-F2. Increased procaspase-9 activations, induced by PB1-F2, could be suppressed by HAX-1. In HAX-1 knockdown A549 cells, the replication of AIV H9N2 was suppressed in parallel to the activation of caspase-3 activation, which increased at the early stage of infection. We hypothesize that HAX-1 promotes AIV replication by interacting with PB1-F2, resulting in the suppression of apoptosis, prolonged cell survival, and enhancement of viral replication. Our data suggest that HAX-1 may be a promoting factor for AIV H9N2 replication through desensitizing PB1-F2 from its apoptotic induction in human lung epithelial cells