An automated signal classifier and a semi-automated signal identifier are designed for collecting the dielectrophoretic signatures of cells flowing through a dielectrophoretic cytometer. In past work, the DEP cytometer signals were manually sorted by going through all recorded signals, which is impractical when analyzing 1000’s of cells per day. In the semi-automated method of collection, signals are automatically identified as events and displayed on the user interface to be accepted or rejected by the user. This approach reduced signal collection time by more than half and produced statistics nearly identical to the manual method. The automated signal classifier based on pattern recognition categorizes detection signals as ‘Accept’ or ‘Reject’. Analyzing large volumes of detection signals is possible in much reduced times and may be approaching real time capability.

An automated signal classifier and a semi-automated signal identifier are designed for collecting the dielectrophoretic signatures of cells flowing through a dielectrophoretic cytometer. In past work, the DEP cytometer signals were manually sorted by going through all recorded signals, which is impractical when analyzing 1000’s of cells per day. In the semi-automated method of collection, signals are automatically identified as events and displayed on the user interface to be accepted or rejected by the user. This approach reduced signal collection time by more than half and produced statistics nearly identical to the manual method. The automated signal classifier based on pattern recognition categorizes detection signals as ‘Accept’ or ‘Reject’. Analyzing large volumes of detection signals is possible in much reduced times and may be approaching real time capability.

Deep neural networks are proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices. The recent rapid increase of edge computing devices has led to an active search for techniques to address the above-mentioned limitations of machine learning frameworks. The quantization of artificial neural networks (ANNs), which converts the full-precision synaptic weights into low-bit versions, emerged as one of the solutions. At the same time, spiking neural networks (SNNs) have become an attractive alternative to conventional ANNs due to their temporal information processing capability, energy efficiency, and high biological plausibility. Despite being driven by the same motivation, the simultaneous utilization of both concepts has not been fully studied. Therefore, this thesis work aims to bridge the gap between recent progress in quantized neural networks and SNNs. It presents an extensive study on the performance of the quantization function, represented as a linear combination of sigmoid functions, exploited in low-bit weight quantization in SNNs. The given quantization function demonstrates the state-of-the-art performance on four popular benchmarks, CIFAR10-DVS, DVS128 Gesture, N-Caltech101, and N-MNIST, for binary networks (64.05%, 95.45%, 68.71%, and 99.365 respectively) with small accuracy drops (8.03%, 1.18%, 3.47%, and 0.17% respectively) and up to 32x memory savings, which outperforms the existing methods.

Face recognition is the problem of identifying individuals in images. This thesis evaluates two methods used to determine if pairs of face images belong to the same individual or not. The first method is a combination of principal component analysis and a neural network and the second method is based on state-of-the-art convolutional neural networks. They are trained and evaluated using two different data sets. The first set contains many images with large variations in, for example, illumination and facial expression. The second consists of fewer images with small variations. Principal component analysis allowed the use of smaller networks. The largest network has 1.7 million parameters compared to the 7 million used in the convolutional network. The use of smaller networks lowered the training time and evaluation time significantly. Principal component analysis proved to be well suited for the data set with small variations outperforming the convolutional network which need larger data sets to avoid overfitting. The reduction in data dimensionality, however, led to difficulties classifying the data set with large variations. The generous amount of images in this set allowed the convolutional method to reach higher accuracies than the principal component method.

<p>Through the computing advance and GPU (Graphics Processing Unit) availability for math calculation, the deep learning field becomes more popular and prevalent. Object detection with deep learning, which is the part of image processing, plays an important role in automatic vehicle drive and computer vision. Object detection includes object localization and object classification. Object localization involves that the computer looks through the image and gives the correct coordinates to localize the object. Object classification is that the computer classification targets into different categories. The traditional image object detection pipeline idea is from Fast/Faster R-CNN [32] [58]. The region proposal network generates the contained objects areas and put them into classifier. The first step is the object localization while the second step is the object classification. The time cost for this pipeline function is not efficient. Aiming to address this problem, You Only Look Once (YOLO) [4] network is born. YOLO is the single neural network end-to-end pipeline with the image processing speed being 45 frames per second in real time for network prediction. In this thesis, the convolution neural networks are introduced, including the state of art convolutional neural networks in recently years. YOLO implementation details are illustrated step by step. We adopt the YOLO network for our applications since the YOLO network has the faster convergence rate in training and provides high accuracy and it is the end to end architecture, which makes networks easy to optimize and train. </p>

The Winner-Take-All (WTA) is an essential neural network operation for locating the most active neuron. Such a procedure has been extensively used in larger application areas. The Winner-Take-All circuit selects the maximum of the inputs inhibiting all other nodes. The efficiency of the analog circuits may well be considerably higher than the digital circuits. Also, analog circuits’ design footprint and processing time can be significantly small. A current-based Winner-Take-All circuit for analog neural networks is presented in this research. A compare and pass (CAP) mechanism has been used, where each input pair is compared, and the winner is selected and passed to another level. The inputs are compared by a sense amplifier which generates high and low voltage signals at the output node. The voltage signal of the sense amplifier is used to select the winner and passed to another level using logic gates. Also, each winner follows a sequence of digital bits to be selected. The findings of the SPICE simulation are also presented. The simulation results on the MNIST, Fashion-MNIST, and CIFAR10 datasets for the memristive deep neural network model show the significantly accurate result of the winner class with an average difference of input and selected winner output current of 0.00795uA, 0.01076uA and 0.02364uA respectively. The experimental result with transient noise analysis is also presented.

1. Lymnaea was the subject of an established behavioural conditioning paradigm where pairings of a neutral lip tactile stimulus (CS) and a sucrose food stimulus (US) results in a conditioned feeding response to the CS alone. The current objective was to dissect trained animals and examine electrophysiological changes in the feeding circuitry which may underlie this learning. 2. Naive subjects were used to confirm that US and CS responses in vivo persisted in vitro since this is a pre-requisite for survival of a learned memory trace. This required the development of a novel semi-intact preparation facilitating CS presentation and simultaneous access to the CNS. 3. The nature and function of the CS response was investigated using naive animals. Intracellular recordings revealed that the tactile CS evokes specific, consistent synaptic responses in identified feeding neurons. Extracellular recording techniques and anatomical investigations showed that these responses occurred through a direct pathway linking the lips to the feeding circuitry. A buccal neuron was characterized which showed lip tactile responses and supplied synaptic inputs to feeding neurons indicating that it was a second-order mechanosensory neuron involved in the CS pathway. 4. Animals trained using the behavioural conditioning paradigm were tested for conditioned responses and subsequently dissected~ Intracellular recording from specific identified feeding motoneurons revealed that CS presentation resulted in significant activation of the feeding network compared to control subjects. This activation was combined both with an increase in the amplitude of a specific synaptic input and an elevation in the extracellular spike activity recorded from a feeding-related connective. A neuronal mechanism to account for these findings is presented. 5. The role of motoneurons in the feeding circuit was reassessed. It is demonstrated, contrary to the current model, that muscular motoneurons have an important contribution during feeding rhythms through previously unreported electrotonic CPG connections.

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