Zobrazeno 1 - 10
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pro vyhledávání: '"Burkitt, Anthony N"'
Autor:
Haderlein, Jonas F., Peterson, Andre D. H., Eskikand, Parvin Zarei, Cook, Mark J., Burkitt, Anthony N., Mareels, Iven M. Y., Grayden, David B.
Predicting future system behaviour from past observed behaviour (time series) is fundamental to science and engineering. In computational neuroscience, the prediction of future epileptic seizures from brain activity measurements, using EEG data, rema
Externí odkaz:
http://arxiv.org/abs/2308.09312
Autor:
Eskikand, Parvin Zarei, Grayden, David B, Kameneva, Tatiana, Burkitt, Anthony N, Ibbotson, Michael R
Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by which we can better understand and predict the principles of neural comp
Externí odkaz:
http://arxiv.org/abs/2305.09317
Autor:
Haderlein, Jonas F., Peterson, Andre D. H., Eskikand, Parvin Zarei, Burkitt, Anthony N., Mareels, Iven M. Y., Grayden, David B.
The empirical success of machine learning models with many more parameters than measurements has generated an interest in the theory of overparameterisation, i.e., underdetermined models. This paradigm has recently been studied in domains such as dee
Externí odkaz:
http://arxiv.org/abs/2304.08066
Autor:
Haderlein, Jonas F., Peterson, Andre D. H., Burkitt, Anthony N., Mareels, Iven M. Y., Grayden, David B.
Autoregressive models are ubiquitous tools for the analysis of time series in many domains such as computational neuroscience and biomedical engineering. In these domains, data is, for example, collected from measurements of brain activity. Crucially
Externí odkaz:
http://arxiv.org/abs/2304.11070
This paper examines the relationship between sparse random network architectures and neural network stability by examining the eigenvalue spectral distribution. Specifically, we generalise classical eigenspectral results to sparse connectivity matric
Externí odkaz:
http://arxiv.org/abs/2212.01549
Autor:
Zarei Eskikand, Parvin, Soto-Breceda, Artemio, Cook, Mark J., Burkitt, Anthony N., Grayden, David B.
Publikováno v:
In Neural Networks December 2024 180
Autor:
Haderlein, Jonas F., Mareels, Iven M. Y., Peterson, Andre, Eskikand, Parvin Zarei, Burkitt, Anthony N., Grayden, David B.
Publikováno v:
2021 60th IEEE Conference on Decision and Control (CDC)
The identification of states and parameters from noisy measurements of a dynamical system is of great practical significance and has received a lot of attention. Classically, this problem is expressed as optimization over a class of models. This work
Externí odkaz:
http://arxiv.org/abs/2104.05775
Autor:
Zarei Eskikand, Parvin, Soto-Breceda, Artemio, Cook, Mark J., Burkitt, Anthony N., Grayden, David B.
Publikováno v:
In Neural Networks September 2023 166:296-312
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Publikováno v:
In Biomedical Signal Processing and Control January 2023 79 Part 1