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Spatiotemporal dynamics of spontaneous activity in neural networks

Primary supervisor

Mehdi Adibi


  • A/Prof Stefano Vassanelli

Spontaneous synchronization is a common phenomenon occurring in diverse contexts, from a group of glowing fireflies at night or chirping crickets in a field to a network of coupled neurons in the brain. The study of synchronization helps to understand how uniform behaviors emerge in populations of heterogeneous neurons. At a macroscale level, the cortex operates in two classically-defined states: “synchronized” state which is characterized by strong low-frequency fluctuations and “desynchronized” state in which low-frequency fluctuations are suppressed. These states determine how information is processed in the cortex. Similarly, at microscale level significant correlations in fluctuations of spontaneous spiking activity across sensory cortex neurons has been observed. The aim of this project is to characterize the non-stationary dynamics of activity at these two levels, and they are linked across different brain regions over space and time. We hypothesise these dynamics reflect the function of neural networks in evoked conditions.
Multi-electrode electrophysiology data from multiple brain areas is available for interested students. Additionally, simulation and computational projects to simulate the behaviour of coupled networks is available. work with animals is not required, however, if interested, there will be a unique opportunity to observe or contribute in animal experiments.

Student cohort

Single Semester
Double Semester

Required knowledge

Matlab or Python, basic knowledge of signal processing.