Welcome to the Dynamics and Neural Systems Group!
The Dynamics and Neural Systems Group is an interdisciplinary research team in the School of Physics at The University of Sydney led by Ben Fulcher.
Who we are
We are located at The University of Sydney, which has a beautiful campus full of intelligent people and a vibrant interdisciplinary community that includes researchers in physics, maths, statistics, computer science, engineering, psychology, physiology, and biomedical science, all investigating the brain.
We are a part of Complex Systems Physics. Being an interdisciplinary group, we collaborate closely with diverse researchers, including information theory (like Joe Lizier), systems neuroscience (like Mac Shine), and consciousness (like Nao Tsuchiya).
What we do
We do quantitative research focused on understanding the properties of complex dynamical systems by applying (and developing new) physical and statistical methods. We think it's fun study such systems in general, but we also aim to apply these tools through collaborations with other scientists. The most common application is to neuroscience where we aim to gain a quantitative, physically based understanding of how the brain works. Check out our publications page for more details.
Our group aims to search across disciplinary boundaries for new types of problems to tackle, and thus emphasizes training in creative, interdisciplinary thinking, and broad, clear, and accessible communication.
Our values
We foster an environment that nurtures research students to independently produce creative and high-quality research. We believe that researchers thrive when students are free to align their goals with their passions. Our culture embodies these beliefs by balancing traditional academic activities with creative ones, and we prioritize individual growth and team integration.
- We aim to foster and encourage authenticity of expression and an environment in which all types of people, personalities, backgrounds, and beliefs, feel supported.
- We are committed to producing science that is high-quality, makes meaningful contributions, is open, and is clearly, transparently, and honestly communicated.
- We believe that high-quality and creative science is possible when group members have the freedom to follow their interests, and can protect time free for fun, nourishing, and complementary life interests and pursuits.
- We aim to train students in a way that helps them understand themselves and their motivations well and can align them with problems that excite and motivate them.
- We aim to guide students to become independent thinkers, providing them with the skills and opportunities to do so.
- To ensure that we are continue to perform in accodance with these values, and can improve them, we regularly reflect on our values and practices and seek (anonymous) feedback on them from all team members.
Advice
We keep an active website containing general research advice across a range of topics here.
News
New preprint from Trent on statistical comparisons of time-series feature sets on classification tasks is out.
Josh's pyhctsa paper, a Python package for highly comparative time-series analysis, is now published in the Journal of Open Source Software: Paper.
Welcome to three new postdocs: Xiaobo, Brendan, and Nic, who all started recently!
Congratulations to Josh and Hugo for the acceptance of their matrix product states for time-series machine learning paper!
Congratulations Annie on her PhD acceptance! And welcome to new postdoc Nic Barabara :)
New preprint from Annie on unifying concepts in information-theoretic time-series analysis is out.
New preprint on applying overlapping community detection methods to brain network data is out.
Kieran's work on tracking sources of non-stationarity from an unknown process is published as an Editor's Pick in Chaos Article
Brendan's work on predicting critical points from noisy systems is written for a general audience in The Conversation