4 citations · 8 across the 11 of their papers we have counts for
5 papers · 1 filter
Epileptic seizure forecasting with long short-term memory (LSTM) neural networks
Daniel E. Payne, Jordan D. Chambers, Anthony Burkitt +4
Objective: Forecasting epileptic seizures can reduce uncertainty for patients and allow preventative actions. While many models can predict the occurrence of seizures from features…
Path Signatures for Seizure Forecasting
Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand +4
Predicting future system behaviour from past observed behaviour (time series) is fundamental to science and engineering. In computational neuroscience, the prediction of future epi…
Understanding visual processing of motion: Completing the picture using experimentally driven computational models of MT
Parvin Zarei Eskikand, David B Grayden, Tatiana Kameneva +2
Computational modeling helps neuroscientists to integrate and explain experimental data obtained through neurophysiological and anatomical studies, thus providing a mechanism by wh…
Autoregressive models for biomedical signal processing
Jonas F. Haderlein, Andre D. H. Peterson, Anthony N. Burkitt +2
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,…
On the benefit of overparameterisation in state reconstruction: An empirical study of the nonlinear case
Jonas F. Haderlein, Andre D. H. Peterson, Parvin Zarei Eskikand +3
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…