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Spiking neural networks are a new generation of neural networks that use neuronal models that are more biologically plausible than the typically used perceptron model. They do not use analog values to perform computations, as is the case in regular neural networks, but rely on spatio-temporal information encoded into sequences of delta-functions known as spike trains. Spiking neural networks are highly energy efficient compared to regular neural networks which makes them highly attractive in certain applications. This thesis implements two approaches for training spiking neural networks. The first approach uses surrogate gradient descent to deal with the issues of non-differentiability that arise with training spiking neural networks. The second approach is based on Bayesian probability theory and uses variational inference for parameter estimation and leads to a Bayesian spiking neural network. The two methods are tested on two datasets from the spiking neural network literature and limited hyperparameter studies are performed. The results indicate that both training methods work on the two datasets but that the Bayesian implementation yields a lower accuracy on test data. Moreover, the Bayesian implementation appear to be robust to the choice of prior parameter distribution. / <p>Sekretess</p>

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This thesis focuses on an adaptive quadratic spiking model of a motoneuron that is both versatile in its ability to represent a range of experimentally observed neuronal firing patterns as well as computationally efficient for large network simulation. The objective of research is to fit membrane voltage data to the model using a parameter estimation approach involving simulated annealing. By manipulating the system dynamics of the model, a realizable model with linear parameterization (LP) can be obtained to simplify the estimation process. With a persistently excited current input applied to the model, simulated annealing is used to efficiently determine the best model parameters that minimize the square error function between the membrane voltage reference data and data generated by the LP model. Results obtained through simulation of this approach show feasibility to predict a range of different neuron firing patterns.

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<p>This Thesis details the design, fabrication and characterization of organic semiconductor field effect transistors with silicon oxide-nitride-oxide-semiconductor (SONOS) gates for use in spiking analog neural circuits. The results are divided into two main sections. First, the SONOS structures, parallel plate capacitors and field effect transistors, were designed, fabricated and characterized. Second, these results are used to model spiking analog neural circuits. The modeling is achieved using PSPICE based software.</p> <p>The initial design work begins with an analysis of the basic SONOS structure. The existence of the ultrathin layers of the SONOS structure is confirmed with the use of Transmission Electron Microscopy (TEM) and Energy Dispersive Spectroscopy (EDS) scans of device stacks. Parallel plate capacitors were fabricated prior to complete transistors due to the significantly less processing required. The structure and behaviour of these capacitors is similar to that of the transistor gates which allows for the optimization of the structures prior to the fabrication of the transistors. These capacitors were fabricated using the semiconductor materials of; crystalline silicon, amorphous silicon, Zinc Oxide, copper phthalocyanine (CuPc) and tris 8-hydroxyquinolinato aluminium (AlQ3). These devices are then subjected to standard capacitance voltage (C-V) analysis. The results of this analysis demonstrate that the inclusion of SONOS structures in the capacitors (and transistors) result in a hysteresis which is the result of charge accumulation in the nitride layer of the SONOS structure. This effect can be utilized as an imbedded memory. Standard control devices were fabricated and analysed and no significant hysteresis effect was observed. The hysteresis effect is only observed after the SONOS devices are subject to high voltages (approximately 14 volts) which allows tunneling through a thin oxide layer into traps in the silicon nitride layer. This analysis was conducted to confirm that the SONOS structure causes the memory effect, not the existence of interface states that can be charged and discharged.</p> <p>The next step was to design and fabricate amorphous semiconductor field effect transistors with and without the SONOS structure. First FETs without the SONOS gates were fabricated using amorphous semiconductor materials; Zinc Oxide, CuPc and AlQ3 and then the devices were characterized. This initial step confirmed the functionality of these basic devices and the ability to fabricate working control samples. Next, SONOS gate TFTs were fabricated using CuPc as the semiconductor material. The characterization of these devices confirmed the ability to shift the transfer characteristics of the devices through a read and write mechanism similar to that used to shift the C-V characteristics of the parallel plate capacitors. Split gate FETs were also produced to examine the feasibility of individual transistors with multiple gates.</p> <p>The results of these characterizations were used to model spiking analog neural circuits. This modeling was carried out in four parts. First, representative transfer and output characteristics were used to replicate analog spiking neural circuits. This was carried out using standard PSPICE software with the modification of the discrete TFT device characteristics to represent the amorphous CuPc organic transistors. The results were found to be comparable to circuits using crystalline silicon transistors. Second, the SONOS structures were modeled closely matching the characterized results for charge and voltage shift. Third, a simple Hebbian learning circuit was designed and modeled, demonstrating the potential for imbedded memories. Lastly, split gate devices were modeled using the device characterizations.</p> / Doctor of Philosophy (PhD)

This research provides a proof of concept and background theory for the physics behind the state-of-the-art ultra-fast plasmonic spiking neurons (PSN), which can serve as a primary synaptic device for developing a platform for fast neural computing. Such a plasmonic-powered computing system allows localized AI with ultra-fast operation speed. The designed architecture for a plasmonic spiking neuron (PSN) presented in this thesis is a photonic integrated nanodevice consisting of two electro-optic and optoelectronic active components and works based on their coupling. The electro-optic active structure incorporated a periodic array of seeded quantum nanorods sandwiched between two electrodes and positioned at a near-field distance from the topmost metal layer of a sub-wavelength metal-oxide multilayer metamaterial. Three of the metal layers of the metamaterials form the active optoelectronic component. The device operates based on the coupling of the two active components through optical complex modes supported by the multilayer and switching between two of them. Both action and resting potentials occur through subsequent quantum and extraordinary photonics phenomena. These phenomena include the generation of plasmonic high-k complex modes, switching between the modes by enhanced quantum-confined stark effect, decay of the plasmonic excitations in each metal layer into hot-electrons, and collecting hot-electrons by the optoelectronic component. The underlying principles and functionality of the plasmonic spiking neuron are illustrated using computer simulation.

Numerous bats species have evolved biosonar to obtain information from their habitats with dense vegetation. Different from man-made sensors, such as stereo cameras and LiDAR, bats' biosonar has much lower spatial resolution and sampling rates. Their biosonar is capable of reliably finding narrow gaps in foliage to serve as a passageway to fly through. To investigate the sensory information under such capability, we have used a biomimetic sonar robot to collect the narrow gap echoes from an artificial hedge in a laboratory setup and from the natural foliage in outdoor environments respectively. The work in this dissertation presents the performance of a conventional energy approach and a deep-learning approach in the classification of echoes from foliage and gap. The deep-learning approach has better foliage versus passageway classification accuracy than the energy approach in both experiments, and it also shows good robustness than the latter one when dealing with data with great varieties in the outdoor experiments. A class activation mapping approach indicates that the initial rising flank inside the echo spectrogram contains critical information. This result corresponds to the neuromorphic spiking model which could be simplified as times where the echo amplitude crosses a certain threshold in a certain frequency range. With these findings, it could be demonstrated that the sensory information in clutter echoes plays an important role in detecting passageways in foliage regardless of the wider beamwith than the passageway geometry. / Doctor of Philosophy / Many bats species are able to navigate and hunt in habitats with dense vegetation based on trains of biosonar echoes as their primary sources for sensory information on the environment. Drones equipped with man-made sensory systems such as optical, thermal, or LiDAR sensors, still face challenges when navigating in dense foliage. Bats are not only able to achieve higher reliability in detecting narrow gaps but accomplish this with much lower spatial resolutions and data rates than those of man-made sensors. To study which sensory information is accessible to bat biosonar for detecting passageways in foliage, a robot consisting of a biomimetic sonar and a camera system has been used to collect a large number of echoes and corresponding images (∼130k samples) from an artificial hedge constructed in the laboratory and various natural foliage targets found outdoors. We have applied a conventional energy approach which is widely used in engineered sonar but is limited by the biosonar's wide beamwidth and only achieves a foliage-versus-passageway classification accuracy of ∼70%. To deal with this situation, a deep-learning approach has been used to improve performance. Besides that, a transparent AI approach has been applied to overcome the black-box property and highlight the region of interest of the deep-learning classifier. The results achieved in detecting passageways were closely matched between the artificial hedge in the laboratory setup and the field data. With the best classification accuracy of 97.13% (artificial hedge) and 96.64% (field data) by the deep-learning approach, this work indicates the potential of exploring sensory information based on clutter echoes from complex environments for detecting passageways in foliage.

Proprioception is the ability to sense body position necessary for coordinate precise movements. Despite the low complexity of insect neuronal systems, scientists are studying their motor control system. Researchers performed experiments in desert locusts by stimulating their apodeme and recording the neuronal response. Previous studies reported variations in neuronal spiking rates related to acceleration, velocity and position sensitivity. Their results led us to the assumption that either there are different kinds of sensory neurons, or there is only one type of neuron responding to various Physical quantities. Therefore, this research intends to investigate the different spiking rates. We also want to study the influence of apodemes excitations in sensory neurons with information theoretical measures. However, the way signals were recorded does not allow the calculation of delayed transfer entropy (DTE) between sensory neurons. To solve that problem we propose a method to estimate parameters of connections in such scenarios. Our analysis will model the time spent between spikes with survival functions. The influence of excitation in the neuronal response will be analyzed with DTE, which will also be used to validate the methods of simulation. Results show that there is evidence to support the assumption of different spiking rates among sensory neurons. DTE suggests the existence of intermediate processing nodes between excitation and some sensory neurons. A further simulation joining the methods proposed and neuronal signals showed that models considering intermediate pathways present a good fit to the data. We suggest that the different responses of sensory neurons are not due to various types of neurons, but to a preprocessing layer. / Propriocepção é a capacidade de monitorar a posição do corpo necessária para coordenar movimentos precisos. Apesar da baixa complexidade dos sistemas neuronais de insetos, cientistas têm estudado seu controle motor. Pesquisadores realizaram experimentos em gafanhotos estimulando mecanicamente seu apódema e registrando a resposta neuronal. Estudos anteriores relatam variações nas taxas de spiking, e as relacionam com sensibilidades à aceleração, à velocidade e à posição. Seus resultados nos levaram às suposições de que ou existem diferentes tipos de neurônios sensores ou há apenas um tipo de neurônio sensível à diferentes grandezas físicas. Portanto, esta pesquisa pretende investigar as diferentes taxas de spiking e estudar a influência das excitações do apódema em neurônios sensores com medidas de teoria da informação. No entanto, a forma como os sinais foram gravados não permite calcular-se a transferência de entropia atrasada (DTE) entre neurônios sensores. Para tanto, propôs-se um método de estimação de parâmetros para ligações em tais cenários. As análises modelarão o tempo gasto entre spikings com funções de sobrevida. Além disso, a influência da excitação sobre a resposta neuronal será analisada com DTE, a qual também será utilizada para validar os métodos de simulação. Os resultados mostram que há evidências para suportar a hipótese de diferentes taxas de spiking. A DTE sugere a existência de nós intermediários (entre excitação e alguns neurônios sensoriais). Posteriormente, uma simulação juntando os métodos propostos e os sinais neuronais mostrou que modelos considerando caminhos intermediários se ajustam bem aos dados. Por fim, os resultados sugerem que as diferentes respostas de neurônios sensores não acontecem devido a diferentes tipos de neurônios, mas sim à uma camada de pré-processamento.