Computational Neuroscience Research

Computational neuroscience uses mathematical and computational models to understand brain function and dysfunction across spatial and temporal scales. We integrate brain imaging and electrophysiological data (EEG, SEEG) to identify brain network mechanisms that underpin the functional organisation of the brain in disease conditions, e.g., epileptic seizure generation and propagation.

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Research projects

Identifying network mechanisms of neuronal population responses to in-vitro electrical stimulation: A computational modelling approach.

This project aims to develop a computational network model of a complex biological system. It will examine how different node interconnectivity patterns influence the behaviour of systems such as induced pluripotent stem cell-derived neuronal populations, both at rest and under electrical stimulation. The objective is to identify network mechanisms underlying neuronal responses to stimulation. Three neuronal network models will be numerically solved to infer spatial and temporal dynamics at the mesoscopic scale. Find out more

Cracking one of the brain codes: Mathematical-computational model of multiscale brain network dynamics in patients with epilepsy.

This study will develop a mathematical-computational model of brain network dynamics to investigate how epileptic seizures emerge and spread across different brain regions and life stages. The model will capture neuronal activity at the mesoscopic and macroscopic scales, integrating biologically realistic networks, stochastic population models, and stereo-EEG (SEEG) data. A multi-modal approach will combine MRI-based brain models, non-linear dynamics (e.g., chaotic oscillators), SEEG analysis, network science, and signal processing. The goal is to uncover the mechanisms underpinning seizure onset, propagation and termination, and understand the role of brain connectivity in large-scale seizure activity. Find out more

Localising the epileptogenic-zone in the brain: A dynamic network connectivity approach.

The study focuses on developing a computational analysis pipeline to localise the epileptogenic zone (EZ) in the brain in patients with epilepsy. It integrates scalp EEG, stereo-EEG (SEEG), and MRI to identify brain network dynamics at the mesoscopic and macroscopic scales. Dynamic connectivity patterns are assessed by algebraic and statistical multivariate signal processing techniques, enabling spatial mapping of seizure onset regions. The findings also support theoretical modelling efforts aimed at simulating epileptic neuronal population dynamics with greater accuracy. Find out more

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Get in touch: Research and Study Opportunities

To learn more about our research or opportunities to study with us, please contact Dr Steve Mehrkanoon at steve.mehrkanoon@monash.edu