4 citations · 8 across the 11 of their papers we have counts for
11 papers
Decoding Saccadic Eye Movements from Brain Signals Using an Endovascular Neural Interface
Suleman Rasheed, James Bennett, Peter E. Yoo +2
An Oculomotor Brain-Computer Interface (BCI) records neural activity from regions of the brain involved in planning eye movements and translates this activity into control commands…
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…
Eigenvalue spectral properties of sparse random matrices obeying Dale's law
Isabelle D Harris, Hamish Meffin, Anthony N Burkitt +1
This paper examines the relationship between sparse random network architectures and neural network stability by examining the eigenvalue spectral distribution. Specifically, we ge…
On the benefit of overparameterization in state reconstruction
Jonas F. Haderlein, Iven M. Y. Mareels, Andre Peterson +3
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…
Impact of axonal delay on structure development in a multi-layered network
Catherine E Davey, David B Grayden, Anthony N Burkitt
The mechanisms underlying how activity in the visual pathway may give rise through neural plasticity to many of the features observed experimentally in the early stages of visual p…
Emergence of radial orientation selectivity: Effect of cell density changes and eccentricity in a layered network
Catherine E. Davey, David B. Grayden, Anthony N. Burkitt
Previous work by Linsker revealed how simple cells can emerge in the absence of structured environmental input, via a self-organisation learning process. He empirically showed the…
A homotopic mapping between current-based and conductance-based synapses in a mesoscopic neural model of epilepsy
Andre D. H. Peterson, Hamish Meffin, Mark J. Cook +3
Changes in brain states, as found in many neurological diseases such as epilepsy, are often described as bifurcations in mesoscopic neural models. Nearly all of these models rely o…