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 30 for “"neuron model"”.
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A Biologically Plausible Neuron Model of Mental Rotation
… engenders a continuous, whole-unit rotation. The model is comprised of 43,000 simulated neurons spread across a variety of neuron ensembles. These ensembles work together to form a neuronal representation of the spatial map entailed by the stimuli, then rotates that spatial map into a series of …
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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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EPIGENETIC REGULATION IN AN IPSC-NEURON MODEL OF FAMILIAL ALZHEIMER’S DISEASE
… controls. In this thesis, I use iPSC-neurons derived from familial AD patients with an APP duplication as a model to study the functional effect of H3K27ac reduction. I found that in iPSC-neurons derived from the AD patients, homeostatic amyloid-reducing genes were upregulated compared …
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ESTIMATING PARAMETERS OF A MULTI-CLASS IZHIKEVICH NEURON MODEL TO INVESTIGATE THE MECHANISMS OF DEEP BRAIN STIMULATION
… a computationally efficient neural network model for the study of deep brain stimulation efficacy in the treatment of Parkinson's disease. An Izhikevich neuron model was used to accomplish this task and four classes of neurons were modeled. The parameters of each class were estimated using a …
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Temporal Neural Networks and Transient Analysis of Complex Engineering Systems
The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a …
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Towards the neurocomputer: an investigation of VHDL neuron models
The investigation of neuron structures is an incredibly difficult and complex task that yields relatively low rewards in terms of information from biological forms (either animals or tissue). The structures and connectivity of even the simplest invertebrates are almost impossible to establish with …
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Dynamics and precursor signs for phase transitions in neural systems
… to state transition. 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 …
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A generalised feedforward neural network architecture and its applications to classification and regression
… systems. It has been extensively used to model some important visual and cognitive functions. It equips neurons with a gain control mechanism that allows them to operate as adaptive non-linear filters. Shunting Inhibitory Artificial Neural Networks (SIANNs) are biologically inspired …
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Roles of gap junctions in neuronal networks
… the roles of gap junctions in the dynamics of neuronal networks in three distinct problems. First, we study the circumstances under which a network of excitable cells coupled by gap junctions exhibits sustained activity. We investigate how network connectivity and refractory length affect the …
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Learning temporal representations in cortical networks through reward dependent expression of synaptic plasticity
… is long-term synaptic potentiation between neurons in a recurrent network. Analytical and numerical techniques are used to demonstrate that the model is sufficient to allow näive networks of both linear and non-linear neurons to encode and reliably represent durations specified by external …
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Neuromorphic deep convolutional neural network learning systems for FPGA in real time
… the behavior and properties of biological neurons. Neuromorphic engineering tries to give an answer to how our brain is capable to learn and perform complex tasks with high efficiency under the paradigm of spike-based computation. This thesis explores both frame-based and spike-based …
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Efficient Implementation of Stochastic Inference on Heterogeneous Clusters and Spiking Neural Networks
… and efficiently implement neuromorphic inference model using heterogeneous clusters to address the problem using traditional Von Neumann architectures and by developing spiking neural networks (SNN) for native and ultra-low power implementation. In this regard, an extendable high-performance …
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A Novel Dual Modeling Method for Characterizing Human Nerve Fiber Activation
… and successful illustration of a coupled model of the human nerve fiber. SPICE netlist code was utilized to describe the electrical properties of the human nervous membrane in tandem with COMSOL Multiphysics, a finite element analysis software tool. The initial research concentrated on the …
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Biosignal Recording with Integrated Circuits
… an improved quadratic-integrate-and-fire neuron fabricated in CMOS technology, capable of mimicking various biologically inspired spike patterns. This neuron, in combination with a piezo-FET tactile sensor, shows promising results for future applications in biologically plausible spiking …
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Models and analysis of a stochastic neural source coder
Information transfer in neurons takes place through action potentials (spikes) which are metabolically expensive. A neural coding approach was developed by Johnson et al. (2016) that is optimal, high-fidelity, energy-efficient and well matches the experimental spiking behavior of real neurons. This …
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The Mechanisms And Roles Of Feedback Loops For Visual Processing
… isthmi: Imc, Ipc and SLu. The tectal layer 10 neurons project to ipsilateral Imc, Ipc and SLu in a topographic way. In turn Ipc and SLu send back topographical: local) cholinergic terminals to the OT, whereas Imc sends non-topographical: global) GABAergic projections to the OT, and also to the …
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Massively parallel neural computation
… 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 neural …
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Spiking Neural Network Framework for Brain Computer Interfaces
… (SNN) communicates via spikes and is a suitable model to study the spiking aspect of EEG-based BCI systems. Electroencephalography recordings are often time consuming, and artifacts cause further rejection of these recorded samples. To overcome this, a few studies have developed generative models …
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Visualization and Analysis Tools for Neuronal Tissue
The complex nature of neuronal cellular and circuit structure poses challenges for understanding tissue organization. New techniques in electron microscopy allow for large datasets to be acquired from serial sections of neuronal tissue. These techniques reveal all cells in an unbiased fashion, so …
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Polyethylene glycol copolymer nanocarriers: Biocompatibility, uptake and intracellular trafficking in neurons
Spinal cord injury (SCI) causes neuronal death and leads to persistent loss of motor and sensory functions. Treatment of SCI is challenging as axon regeneration from damaged neurons is largely inhibited in the central nervous system (CNS). Moreover, targeting therapeutics to damaged CNS neurons is …
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