819 research outputs found
Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to continuous learning of new knowledge without disturbing the existing knowledge base and without re-iterating through the training samples. The Adaptive Resonance Theory (ART) and Generalized Regression Neural Network (GRNN) models are employed as the backbone in this
research
Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review
First published: 25 April 2020Neurofeedback training using real-time functional magnetic resonance imaging
(rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity.
It has sparked increased interest as a promising non-invasive treatment option in
neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance
are yet to be determined. In this work, we present the first extensive review
of acquisition, processing and quality control methods available to improve the quality
of the neurofeedback signal. Furthermore, we investigate the state of denoising
and quality control practices in 128 recently published rtfMRI-NF studies. We found:
(a) that less than a third of the studies reported implementing standard real-time
fMRI denoising steps, (b) significant room for improvement with regards to methods
reporting and (c) the need for methodological studies quantifying and comparing the
contribution of denoising steps to the neurofeedback signal quality. Advances in
rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic
effort is needed to build up evidence that disentangles the various mechanisms
influencing neurofeedback effects. To this end, we recommend that future
rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising
steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/),
(b) ensure the quality of the neurofeedback signal by calculating and reporting
community-informed quality metrics and applying offline control checks and (c) strive
to adopt transparent principles in the form of methods and data sharing and support
of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an
interactive environment to explore the study data, can be accessed at https://github.
com/jsheunis/quality-and-denoising-in-rtfmri-nf.LSH‐TKI, Grant/Award Number: LSHM16053‐SGF; Philips Researc
A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
Confined space fires are common emergencies in our society. Enclosure size, ventilation, or type and quantity of fuel involved are factors that determine the fire evolution in these situations. In some cases, favourable conditions may give rise to a flashover phenomenon. However, the difficulty of handling this complicated emergency through fire services can have fatal consequences for their staff. Therefore, there is a huge demand for new methods and technologies to tackle this life-threatening emergency. Modelling and simulation techniques have been adopted to conduct research due to the complexity of obtaining a real cases database related to this phenomenon. In this paper, a review of the literature related to the modelling and simulation of enclosure fires with respect to the flashover phenomenon is carried out. Furthermore, the related literature for comparing images from thermal cameras with computed images is reviewed. Finally, the suitability of artificial intelligence (AI) techniques for flashover prediction in enclosed spaces is also surveyed.This work has been partially funded by the Spanish Government TIN2017-89069-R grant supported with Feder funds. This work was supported in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under Grant RTI2018-094283-B-C32 and the Lucentia AGI Grant
Parameter estimation of neuron models using <i>in-vitro </i>and<i> in-vivo </i>electrophysiological data
Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model
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