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 37 for “"spiking neural network"”.
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Spiking Neural Network Framework for Brain Computer Interfaces
… However, with EEG being an accumulation of spiking neural activity, reverse engineering it to a spiking version could establish new directions in improving BCI systems, which is the focus of this thesis. Spiking neural network (SNN) communicates via spikes and is a suitable model to study …
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Fast flux botnet detection based on adaptive dynamic evolving spiking neural network
… System (DNS) method known as Fast-Flux Service Network (FFSN) is a special type of botnet that has been engaged by bot herders to cover malicious botnet activities, and increase the lifetime of malicious servers by quickly changing the IP addresses of the domain name over time. Although several …
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Spiking Neural Network with Memristive Based Computing-In-Memory Circuits and Architecture
… There are two main types of Artificial Neural Networks (ANNs), Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). In this thesis we first study the types of RNNs and then move on to Spiking Neural Networks (SNNs). SNNs are an improved version of ANNs that mimic …
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Analog Spiking Neural Network Implementing Spike Timing-Dependent Plasticity on 65 nm CMOS
… and well-known algorithm is the artificial neural network (ANN). While ANNs demonstrate impressive reinforcement learning behaviors, they require large power consumption to operate. Therefore, an analog spiking neural network (SNN) implementing spike timing-dependent plasticity is proposed, …
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A Back Propagation Based Spiking Neural Network Approach for Intelligent Link Decisions In Satellite Communication
A Spiking Neural Network (SNN) with neuromorphic architecture for optimal link decisions is put forward in this paper. SNNs can adapt to the various changes in the working environment quickly, for maintenance or advancement of the selected performance metrics. Such results can be appealing for …
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Energy-efficient spiking neural network-based visual place recognition and reinforcement learning in adversarial scenarios
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2027-05-01
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Spatial-temporal data modelling and processing for personalised decision support
… modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into …
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Binaural sound source localization using machine learning with spiking neural networks features extraction
… are used to acquire binaural signals and a spiking neural network is used to compare signals from the two sensors. The firing rates of coincidence-neurons in the spiking neural network model provide information as to the location of a sound source. Previous methods have used a …
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Neural Networks for Control of Artificial Life Form
… An artificial life form under the control of a spiking neural network has been created in a chessboard environment which consists of 60*60 grids using Matlab GUI. The spiking neural network consists of 8 neurons simulated using Izhikevich model which combines the property of both biological …
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Polychronization as a mechanism for language acquisition in spiking neural networks
… understanding how mental states might arise from spiking activity. In particular, we focus on the phenomenon of polychronization, which may be described as the self-organization of a spiking neural network as a result of the interplay between network structure, spiking activity, and synaptic …
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Energy-efficient Neuromorphic Computing for Resource-constrained Internet of Things Devices
… for on-device processing are driven by network bandwidth limitations and consumer anticipations concerning data privacy and user experience. In the realm of computing, there is a growing interest in exploring novel technologies that can facilitate ongoing advancements in performance. Of …
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Pattern Recognition Using Spiking Neural Networks
… pattern recognition in comparison to traditional neural networks convinced neuroscientists to introduce a biologically plausible model of the neuron, which is known as spiking neurons. In opposition to conventional neuron, spiking neurons use a short electrical pulse known as a spike to transfer …
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An Exploration of Spiking Neural Networks and their use on Reinforcement Learning Tasks
Artificial neural networks have recently been the prominent architecture for reinforcement learning tasks. However, there is emerging evidence that spiking neural networks can perform just as well and can retain this performance across similar environments. Spiking neural networks are experiencing …
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Voluntary Action and Conscious Intention
… volition. In the first study, I developed a spiking neural network model of self-initiated action that explains movement-related signals at multiple spatiotemporal scales. In the second study, I investigated what kind of process underlies spontaneous action initiation and how that process …
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Integrating Biological and Artificial Neural Networks Processing on FPGAs
Neural interfaces are rapidly gaining momentum in the current landscape of neuroscience and bioengineering. This is due to a) unprecedented technology capable of sensing biological neural network electrical activity b) increasingly accurate analytical models usable to represent and understand …
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Large-scale neuronal networks: from simulation technology to the study of plasticity-driven cognitive processes
… on neuronal dynamics, mean-field models and spiking neural networks are two of the most relevant, and are introduced in Part I of this doctoral thesis. In particular, spiking neural network models have become an effective tool to study brain functions as they capture several aspects of …
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Neuromodulation Based Control of Autonomous Robots on a Cloud Computing Platform
… neurobiologically plausible models and computer networking has resulted in new ways of implementing control systems on robotic platforms. The work presents a control approach based on vertebrate neuromodulation and its implementation on autonomous robots in the open-source, open-access …
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Active Pre-Touch Sensing: From Biology to Neuromorphic Biomimetic Artifacts
… the carrier wave itself is subject to noise. Neural structures associated with the electric sense of mormyrid weakly electric fish are evolutionary optimized to cope which such problems and hence provide a convenient biological model in which processing of electrosensory information can be …
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