Lachlan HW Hamilton

Lachlan HW Hamilton

PhD Candidate at QIMR Berghofer and UQ, Computational Neuroscience

Talks and presentations

COVID Modelling: Mission Impossible

November 03, 2021

Talk, QIMR Berghofer Clinical Brain Network & Brain Modelling Group Lab Meeting, Brisbane, Queensland, Australia

The COVID pandemic has kept the borders to Queensland shut for almost 2 years. The QLD government commissioned the QIMR Berghofer COVID Modelling Team to investigate when the boarders should open. With the agent-based-modelling software, “covasim”, we simulated a range of scenarios focusing on the question: “what will happen to Queensland if the borders open?”

First and Second Order Phase Transitions: Criticality, Continuity and Confusion

September 03, 2021

Talk, QIMR Berghofer Clinical Brain Network & Brain Modelling Group Teaching Session, Brisbane, Queensland, Australia

Phase changes are ubiquitous in the world around us, from the obvious freezing and melting point of water, to the Curie temperature determining if a ferromagnetic material has a magnetic field. From Landau original description of second order phase transitions in 1937, the analysis of systems at criticality has been seen in an incredible number of fields as diverse as geoscience and neuroscience. There are many underlying assumptions to measurements of a system at criticality, and understanding these are essential to using these metrics.

Eigenmodes in the brain explain how local perturbations evolve into long-range effects

April 21, 2021

Talk, Australian Brain and Psychological Sciences Meeting, Brisbane, Queensland, Australia

The brain is a complex system consisting of 10^11 neurons and over 10^14 connections. Theories of criticality from statistical physics can provide insight into systems of this size, and ask whether the brain is in fact critical. I outline my work where we developed numerical simulations of ideal models of criticality with the random field Ising model, and compared the observed dynamics to single-cell resolution calcium imaging data of the zebrafish brain. We investigated how sub-sampling and finite sized systems change self-organized criticality metrics such as power laws and universal scale.

Statistical Physics Models for Zebrafish Neural Dynamics

April 21, 2021

Talk, QIMR Berghofer Clinical Brain Network & Brain Modelling Group Lab Meeting, Brisbane, Queensland, Australia

The brain is a complex system consisting of 10^11 neurons and over 10^14 connections. Theories of criticality from statistical physics can provide insight into systems of this size, and ask whether the brain is in fact critical. I outline my work where we developed numerical simulations of ideal models of criticality with the random field Ising model, and compared the observed dynamics to single-cell resolution calcium imaging data of the zebrafish brain. We investigated how sub-sampling and finite sized systems change self-organized criticality metrics such as power laws and universal scale.

Predicting particle properties in optical traps with machine learning

September 17, 2020

Talk, SPIE Nanoscience + Engineering, 2020, San Diego, California, United States

Identifying a particle in an optical trap can be a difficult task, especially for biological samples with low contrast. The relationship of radius and refractive index to the stiffness of optical traps is non-intuitive, motivating a machine learning approach. We demonstrate methods for real-time estimates of the radius and refractive index of particles trapped by optical tweezers. This is achieved by analyzing the particle’s position and force with artificial neural networks. Our network achieved binary classification of experimental particles by sampling only milliseconds of force and position values. This demonstrates that real-time particle recognition is achievable with machine learning systems.