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 255 for “"spiking"”.
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Oscillatory Spiking Circuits: A biologically plausible architecture for fast and reliable spiking computations
… fashion, and can do so rapidly. How to construct Spiking Neural Networks (SNNs) that achieve similar performance is still unknown. Here, we present an architecture for spiking neural networks that operate efficiently (with a low number of spikes), rapidly, and robustly, using biologically …
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Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications
<p>Spiking neural networks have been used to investigate the mechanisms of processing</p><p>in biological neural circuits or to propose hypotheses that can be tested in exper-</p><p>iments. Because of their biological plausibility and event-based information trans-</p><p>mission, Spiking Neural …
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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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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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Analysis framework for adaptive spiking neural networks
… understanding how mental states might arise from spiking activity. Cortical modeling has traditionally focused on the mechanisms and behaviors at the cellular level. However, developments with respect to group or population level phenomena indicate that a shift in focus is necessary to understand …
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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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Learning and memory in chaotic spiking neural models
… This has been done in the context of chaotic spiking neural networks (CSNNs). Chaos provides many interesting properties that can be used to achieve computational tasks. Such properties are sensitivity to initial conditions, space filling, control and synchronization. Biological research …
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Identification of Dendritic Processing in Spiking Neural Circuits
… processing directly from spike times produced by spiking neurons. The problem setting of spiking neurons is necessary since such neurons make up the majority of electrically excitable cells in most nervous systems and it is often hard or even impossible to directly monitor the activity within …
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Constructive spiking neural networks for simulations of neuroplasticity
… theory on algorithms for constructing neurons in spiking neural networks and simulations of neuroplasticity. This theory is applied in the development of a constructive algorithm based on spike-timing- dependent plasticity (STDP) that achieves continual one-shot learning of hidden spike patterns …
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Biologically motivated reinforcement learning in spiking neural networks
I consider the problem of Reinforcement Learning (RL) in a biologically feasible neural network model, as a proxy for investigating RL in the brain itself. Recent research has demonstrated that synaptic plasticity in the higher regions of the brain (such as the cortex and striatum) depends on …
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Spiking Neural Networks for Low-Power Medical Applications
… series of "spikes" instead of continuous values, spiking neural networks (SNN) may be the right model architecture to address these concerns. This work investigates the proposed advantages of SNNs compared to more traditional architectures when tested on various medical datasets. We compare the …
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Building cell assembly based associative memory with spiking neurons
… 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 question-answering system with CA-based …
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Mesoscale activated states gate spiking in the awake brain
… neurons over the few milliseconds preceding spiking, but it's not known whether these represent just the extremes of a continuum. Combining a virtual reality environment with an optimized robotic system for intracellular recordings we therefore analyzed the subthreshold dynamics leading to …
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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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Characterization of a Spiking Neuron Model via a Linear Approach
<p>In the past decade, characterizing spiking neuron models has been extensively researched as an essential issue in computational neuroscience. In this thesis, we examine the estimation problem of two different neuron models. In Chapter 2, We propose a modified Izhikevich model with an adaptive …
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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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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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Neuromorphic audio processing through real-time embedded spiking neural networks.
… and audio processing systems based on a spiking artificial cochlea and neural networks are proposed and implemented. First, the biological behavior of the animal’s auditory system is analyzed and studied, along with the classical mechanisms of audio signal processing for sound …
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