51 citations · 73 across the 8 of their papers we have counts for
8 papers
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…
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…
Neural Field Models: A mathematical overview and unifying framework
Blake J. Cook, Andre D. H. Peterson, Wessel Woldman +1
Mathematical modelling of the macroscopic electrical activity of the brain is highly non-trivial and requires a detailed understanding of not only the associated mathematical techn…
Consequences of Dale's law on the stability-complexity relationship of random neural networks
J. R. Ipsen, A. D. H. Peterson
In the study of randomly connected neural network dynamics there is a phase transition from a `simple' state with few equilibria to a `complex' state characterised by the number of…
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…