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 21 for “"Neuron models"”.
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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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From Neuron Models to Edge Intelligence: A Multi-Level Exploration of Neuromorphic Computing
L'abstract è presente nell'allegato / the abstract is in the attachment
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ASSESSMENT OF CELL AND METABOLIC IMPACT OF MICROTUBULE TARGETING AGENTS (MTA) IN NEURODEGENERATION AND NEURON MODELS.
… data integration using Genome-Scale Metabolic Models (GSMM) we report that, in PC12 cell model, neuronal differentiation is accompanied by switching towards a more glycolytic metabolism, with the glucose catabolism that tends towards lactate production, rather than the aerobic oxidative …
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Implementation of a line attractor-based model of the gaze holding integrator using nonlinear spiking neuron models
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.
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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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Modelling the synaptic plasticity underlying habituation, sensitization and classical conditioning of the Aplysia Californica Gill Siphon withdrawal reflex
… description of the functioning of real neurons, as well as an introduction to several of the well known neuron models, including the Hodgkin Huxley equations and the Leaky Integrate-and-Fire model. Also included is a brief description of some neural coding schemes. The next chapter …
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Voltage-source inverter output waveform compensation using adaptive intelligent control
… and outputs through multiple linear or nonlinear neuron models, and processes these input/output data associations in a parallel distributed manner. Network inputs in the form of UPS load voltage commands and load current feedback are propagated forward in the network each controller sampling …
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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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A Novel Dual Modeling Method for Characterizing Human Nerve Fiber Activation
… through this process that the correct usage of neuron models within a two dimensional conductive space did allow for the approximate modeling of human neural electrical characteristics.</p>
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Multi-Scale Modeling for Analysis and Design of Transcranial Electric and Magnetic Brain Stimulation
… dissertation presents multiscale computational models that predict the neural response to TMS and tES at the single-cell and population levels for analysis and rational design of transcranial brain stimulation.We adapted biophysically-realistic models of cortical neurons from the Blue Brain …
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Enabling Energy-Efficient Hybrid CMOS and Embedded Memory Accelerators for Neuromorphic Computing at the Edge
… schemes and Leaky Integrate-and-Fire (LIF) neuron models are developed to optimize temporal information processing. We then explore reservoir computing (RC) architecture to reduce the training complexity of deep models on ASICs. By training only the output layer while preserving …
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Quantifying the pathways modulated by deep brain stimulation for essential tremor using computational modeling
… We developed patient-specific computational neuron models from three ET patients implanted with a Medtronic 3389 DBS lead. Multi-compartment models of Vim / Vc thalamocortical neurons and cerebellothalamic / medial lemniscal axonal afferents were simulated in the context of patient-specific …
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Integrating Biological and Artificial Neural Networks Processing on FPGAs
… activity b) increasingly accurate analytical models usable to represent and understand dynamics and behavior in neural networks c) novel and improved artificial intelligence methods usable to extract information from recorded neural activity. Nevertheless, all these instruments pose …
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Investigating Parkinson’s Disease human iPSC-derived models through single-cell gene expression
… protocols of iPSC to midbrain dopaminergic neurons enables the study human midbrain Dopamine(DA) neurons, the primary neuronal group affected in Parkinson's Disease(PD), in-vitro. Despite their increasing adoption and disease modeling potential in neuroscience research, establishing …
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The functionality of spatial and time domain artificial neural models.
… of the research and their units, Artificial Neurons, are first compared with alternative models. This initial work is mainly in the spatial-domain and introduces a new neural model, termed a Taylor Series neuron. This is designed to be flexible enough to assume most mathematical functions. …
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Efficient Implementation of Stochastic Inference on Heterogeneous Clusters and Spiking Neural Networks
… significant progress in this direction, spiking neuron models capable of distributed online learning are proposed. A high performance SNN simulator (SpNSim) is developed for simulation of large scale mixed neuron model networks. An accompanying digital hardware neuron RTL is also proposed for …
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Speech enhancement using multisensory cooperative computing
… processing of two-point layer five pyramidal neurons (L5PCs). Unlike conventional point neuron models that indiscriminately process inputs, MCC adaptively filters and amplifies only contextually salient signals via a dendritic gating mechanism. Implemented on Xilinx Ultra- Scale+ MPSoC …
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An Exploration of Neural Heterogeneity and its Consequences on Network Dynamics and Neuromodulation
… distribution to cell types and patterns of neuron connectivity. This heterogeneity has been linked to stable, persistent behaviour, increased information transmission, and effective neural encoding. Importantly, although neural heterogeneity is often regarded as a static property and …
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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 …
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Minimum-error, energy-constrained source coding by sensory neurons
Neural coding, the process by which neurons represent, transmit, and manipulate physical signals, is critical to the function of the nervous system. Despite years of study, neural coding is still not fully understood. Efforts to model neural coding could improve both the understanding of the …
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