Synaptic plasticity in biological neural networks
Mr Matthieu Gilson
PhD Candidate
Dr David Grayden
Special Research
Fellow
The University of Melbourne
Prof Tony Burkitt
Special Research
Fellow
The University of Melbourne
The brain’s unparalleled ability to learn and adapt is believed to rely largely on mechanisms of synaptic plasticity. Spike-timing-dependent plasticity (STDP) is one well-known mechanism that strengthens or weakens synapses depending on the firing rate and correlation structures of the connected neurons. We use a mathematical model of spiking neurons, the linear Poisson neuron, to investigate the impact of STDP in recurrently connected networks.
Mathematical techniques from stochastic analysis and dynamical systems allow us to determine the asymptotic activity state of our networks depending on the input characteristics and the learning parameters. We verify our analytical predictions using numerical simulation with simulation software we have developed (written in C++); a parallelised version to be used on a supercomputer is being developed.
Our results will lead to a better understanding of the information processing which takes place in the central nervous system. Such an understanding may allow us, for example, to fine tune the electric signals sent to the brain by electrodes, so as to make use of the natural plasticity of the brain.
Funding
ARC Discovery Project Grant DP 0453205.
This project is a collaboration between the BEI and the School of Engineering at The University of Melbourne