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 28 for “"Spiking Neurons"”.
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Building cell assembly based associative memory with spiking neurons
… in synaptic conditions and strength between neurons. This thesis explores networks of spiking neurons to implement CAs and to simulate cognitive functions. The Stroop test, a prominent cognitive interference task, is replicated in a task-completion simulation using binary CAs. Additionally, a …
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Modeling the hippocampus : finely controlled memory storage using spiking neurons
The hippocampus, an area in the temporal lobe of the mammalian brain, participates in the storage of personal memories and life events. As such traumatic memories and the consequent symptoms of post-traumatic stress are thought to be stored or at least processedin the hippocampus. While a …
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Pattern Recognition Using Spiking Neural Networks
… 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 the information. The complexity and dynamic of these neurons allow them to perform complex computational tasks. However, …
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Energy Efficient Deep Spiking Recurrent Neural Networks: A Reservoir Computing-Based Approach
… efficiency of our model, we propose to adopt spiking neurons as the information processing unit of DFR. Spiking neural networks (SNNs) are the most biologically plausible and energy efficient class of artificial neural networks (ANNs). The traditional analog ANNs have marginal similarity with …
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Spike-Based Classification of UCI Datasets with Multi-Layer Resume-Like Tempotron
Spiking neurons are a class of neuron models that represent information in timed sequences called ``spikes.'' Though predominantly used in neuro-scientific investigations, spiking neural networks (SNN) can be applied to machine learning problems such as classification and regression. SNN are …
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Dynamics and learning in recurrent neural networks
… out through a network of a large number of neurons. With massive feedback connections among these neurons, a study of its dynamics is necessary in order to understand the network's function. In this thesis, I aim at studying several recurrent network models and relating the dynamics with the …
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Towards a Neural Measure of Value and the Modelling of Choice in Strategic Games
… value and the noise inherent in networks of spiking neurons. Our neural model generates any ratio of choices through the specification of action value, including the equilibrium ratio, and provides an explanation for why we observe equilibrium behaviour in some contexts and not others. The …
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The role of short term synamptic plasticity in temporal coding of neuronal networks
… in enhancing coincidence detecting ability of neurons in the avian auditory brainstem. Coincidence detection means a target neuron has a higher firing rate when it receives simultaneous inputs from different neurons as opposed to inputs with large phase delays. This property is used by birds in …
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Spiking neural networks and their applications
… and the third is now under development. Spiking neural networks (SNNs) seek to improve on previous generations in two ways: by using a more biologically-inspired neuron, they are shown to be capable of more complex calculations; incorporating polychronous properties of highly-recurrent …
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Scalable Bundle Design for Massively Parallel Neuronal Recordings <i>In Vivo</i>
… consists of precise interactions between related neurons. New techniques are needed to measure the time sensitive interactions within entire neural networks to understand how the brain functions. Extracellular recording is the oldest method of measuring neural activity and can sample at a temporal …
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Advanced technologies for spatio-temporal control of neural circuits using optogenetics
… approaches aiming at obtaining information on spiking activity, morphology and genetic identity of the many constituents of neural networks.;This PhD research project focussed on the development of an experimental tool to exploit this possibility combining micro-electrode array (MEA) …
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Identification of Dendritic Processing in Spiking Neural Circuits
… of dendritic trees and dendritic branches of neurons. This evidence suggests that, in addition to inferring the connectivity between neurons, identifying analog dendritic processing in individual cells is fundamentally important to understanding the underlying principles of neural computation. …
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Massively parallel neural 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 real challenge lies in modelling …
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Design and Optimization of Temporal Encoders using Integrate-and-Fire and Leaky Integrate-and-Fire Neurons
… directly using analog temporal encoders from Spiking Neural Networks (SNNs). These encoders receive an analog signal as an input and generate a spike or spike trains as their output. The proposed temporal encoders use latency and Inter-Spike Interval (ISI) encoding and are expected to produce …
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Achieving baseline states in sparsely connected spiking-neural networks: stochastic and dynamic approaches in mathematical neuroscience
Networks of simple spiking neurons provide abstract models for studying the dynamics of biological neural tissue. At the expense of cellular-level complexity, they are a frame-work in which we can gain a clearer understanding of network-level dynamics. Substantial insight can be gained …
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Inference and Learning in Spiking Neural Networks for Neuromorphic Systems
… (IOT), and cyber physical systems (CPS). Spiking neural network (SNN) is often studied together with neuromorphic computing as the underlying computational model . Similar to the biological neural system, SNN is an inherently dynamic and stateful network. The state and output of SNN do not …
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Inference And Learning In Spiking Neural Networks For Neuromorphic Systems
… (IOT), and cyber physical systems (CPS). Spiking neural network (SNN) is often studied together with neuromorphic computing as the underlying computational model . Similar to the biological neural system, SNN is an inherently dynamic and stateful network. The state and output of SNN do not …
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Dynamics and precursor signs for phase transitions in neural systems
… We use theoretical neural modelling (single spiking neurons, a network of these, and a mean-field continuum limit) and in vitro experiments to address this question. Dynamically realistic equations of motion for thalamic relay neuron, reticular nuclei, cortical pyramidal and cortical …
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Network Structures Arising from Spike-Timing Dependent Plasticity
… network. By analyzing pairwise interactions of neurons through STDP and also numerical simulations of a large network, I show that conventional pair-based STDP functions as a loop-eliminating mechanism in a network of spiking neurons and organizes neurons into in- and out-hubs. Loop-elimination …
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Efficient Processing of Spiking Neural Networks: A Memory-Based Approach
… of functionality and processing efficiency. Spiking neural networks (SNNs) are widely used as the computing model for the neuromorphic system. The neurons in the SNN communicate using spikes, which allows the SNN to be efficiently implemented on hardware. In addition, the SNNs are based on …
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