Brendan Harris
@brendanjohnh
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Postgrad student in complex systems @sydney_physics
Joined July 2021
RT @bendfulcher: New paper!. We introduce an efficient set of statistical features for fMRI time series (calibrated on mouse manipulation e….
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RT @AnnieGBryant: x-less Chris Whyte and I introduce a novel data-driven framework to evaluate >200 connectivity-based neural correlates of….
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Can't wait for #cosyne2025, starting tomorrow!. Our paper (with Leonardo Gollo and @bendfulcher) will be on display at poster [1-117]. Stop by 8pm to 11pm tomorrow and hear about our new method for detecting criticality in noisy systems like the brain!
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Going to #cosyne2025? Wondering whether higher cortical regions are closer to a critical point? Stop by poster [1-117] on the 27th March to chat about detecting #criticality in noisy systems like the #brain!. Poster and related links are up now at:
github.com
Poster file for Cosyne 2025. Contribute to brendanjohnharris/Cosyne_2025 development by creating an account on GitHub.
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RT @AnnieGBryant: Excited to share work co-led with @aditi_jh developing a data-driven selection technique for overlapping community detec….
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RT @Enri_amico: Very happy to see that our paper on "Higher order connectomics of human brain function" is now out in @NatureComms . Lots o….
nature.com
Nature Communications - Here, the authors perform a higher-order analysis of fMRI data, revealing that accounting for group interactions greatly enhances task decoding, brain fingerprinting, and...
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RT @bendfulcher: Our review/perspective on tracking non-stationarity of an unknown process is now published in Chaos 🦋.Congratulations @kie….
pubs.aip.org
Non-stationary systems are found throughout the world, from climate patterns under the influence of variation in carbon dioxide concentration to brain dynamics
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RT @JoshTan19: So excited for this work to be finally out. Thanks to all the co-authors for putting up with me and letting me talk about te….
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Better believe it, there are now TWO #timeseries feature sets available in #julialang. The new CatchaMouse16.jl package joins Catch22.jl, bringing 16 more features tailored to (mouse) fMRI data: Check out the CatchaMouse16 paper below.
github.com
Evaluate catchaMouse16 features in Julia. Contribute to brendanjohnharris/CatchaMouse16.jl development by creating an account on GitHub.
New preprint w/ Imran Alam, Patrick Cahill @Valerio_Zerbi @m_markicevic @brendanjohnh @olivercliff . "Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI". Code: Short summary 👇
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RT @bendfulcher: Our work by @brendanjohnh (w Leo Gollo) on tracking the distance to criticality in noisy systems is now out in @PhysRevX 🙂….
time-series-features.gitbook.io
The Rescaled Auto-Density (RAD) is a noise-robust metric for inferring the distance to criticality (the DTC). It aims to perform well in settings where the noise level varies between time series.
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RT @phan_cameron: Direct your gaze at this or put it into your periphery, really depends if you are walking. We found that the oscillation….
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RT @bendfulcher: New results now live! Most exciting is @brendanjohnh mouse #neuropixels application. We find that brain areas higher in t….
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RT @bendfulcher: New pre-print by @aria_mt_nguyen w @jlizier:."A feature-based information-theoretic approach for detecting interpretable,….
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RT @bendfulcher: New #ComplexSystems preprint:. "Tracking the distance to criticality in systems with unknown noise". By @brendanjohnh w/ L….
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All inspired by the normalizations in @bendfulcher's MATLAB time-series analysis toolbox 𝘩𝘤𝘵𝘴𝘢: (.
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Outlier-robust versions of each normalization that use the median and interquartile range, as well as mixed normalizations that are robust unless the IQR is 0.
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Five base normalization methods.(more to come, open to suggestions)
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Even simpler syntax for reversing/inverting a normalization!
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Simple syntax for normalizing any-dimensional slices of an array!
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