Venue: The University of Tokyo, Komaba I Campus, KOMCEE east (Building 39a in this map), room K 211
Date: 19th October, 2022
Time: 14:00-17:00 (JST)
Schedule:
14:00-15:00 Talk 1 (45 min presentation + 15 min Q&A)
15:00-15:30 Break
15:30-16:30 Talk 2 (45 min presentation + 15 min Q&A)
16:30-17:00 Discussions
Short Bio:
Dr Ben Fulcher is a Senior Lecturer in The School of Physics at The University of Sydney. His research uses physical modeling, dynamical systems, genetics, and statistical machine-learning approaches to understand organizational principles of complex systems, including the brain.
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Talk 1: Quantifying complex dynamical systems
Many systems in the world around us evolve through time and can be measured in the form of multivariate time series. In this talk I will introduce new methods that we’ve developed for quantifying the dynamical properties of individual components of the system and their interactions. We describe our approach as ‘highly comparative’, as large numbers of possible analysis methods (e.g., >7000 time-series features implemented in our hctsa software package, and ~150 pairwise dependence measures in pyspi) are compared. Our approach enables new systematic ways of analyzing time-series data, leveraging an interdisciplinary literature in a way that provides understanding. I will highlight new open tools that we’ve developed to enable these analyses and discuss recent applications to applications in time-series data in neuroscience and astrophysics.
Ref 1: Cliff et al. (2022). Unifying Pairwise Interactions in Complex Dynamics. arXiv:
Ref 2: Fulcher & Jones (2017). hctsa: A computational framework for automated time-series phenotyping using massive feature extraction. Cell Systems.
Talk 2: Opportunities for Incorporating Brain Atlas Datasets into Whole-Brain Models
In this talk I will discuss the opportunities for developing physiologically based brain models constrained by recent brain-atlas datasets with high spatial resolution and whole-brain coverage. I’ll talk both about some recent statistical findings from such brain-mapping experiments (that combine spatial variation of gene expression, cell densities, and MRI measurements), and also some of our work developing and understanding neural-mass models constrained by these data. Our work demonstrates methods for visualizing and interpreting model performance in terms of underlying dynamical mechanisms, an approach that is crucial for building explanatory and physiologically grounded models of the dynamical principles that underpin large-scale brain activity.
Ref 1: Fulcher et al. (2019). Multimodal gradients across mouse cortex. PNAS
Ref 2: Siu et al. (2022). Extracting Dynamical Understanding From Neural-Mass Models of Mouse Cortex. Frontiers in Computational Neuroscience.
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Host: Masafumi Oizumi (The University of Tokyo)