Augustinas is a first year Quantitative Biology PhD student at the University of Edinburgh. He graduated from the University of Edinburgh with an Integrated Master’s degree in Computational Physics. Over the course of his studies Augustinas undertook various research projects which involved electronic structure calculations, applying machine learning to astronomical data, and investigating the black-box behaviour of artificial neural networks. He developed a keen interest in neuroscience, systems biology, statistical physics and machine learning—fields he is excited to delve deeper into during his time at the Turing.
The firing of neurons subject to input stimuli is highly variable so that random fluctuations in the system must be considered when modelling biologically realistic neurons. Augustinas studies the properties of such stochastic neuron models, hoping to shed light upon neuronal dynamics and brain function.
By adapting and extending mathematical approximation techniques previously developed in the context of modelling biochemical cellular processes, he aims to further investigate both single neuron models and population models involving interactions between many neurons. He will also develop parameter estimation methods based on Bayesian inference in order to build a statistically rigorous quantitative description of the studied neural systems.