Ben Fulcher
@bendfulcher.bsky.social
I lead the Dynamics and Neural Systems Group at the School of Physics, the University of Sydney.
We develop time series tools & physical models to understand the dynamics of complex (usually neural) systems.
Also: @bendfulcher@fediscience.org
We develop time series tools & physical models to understand the dynamics of complex (usually neural) systems.
Also: @bendfulcher@fediscience.org
New paper!
We introduce an efficient set of statistical features for fMRI time series (calibrated on mouse manipulation experiments and tested on mouse and human data): catchaMouse16.
Paper: doi.org/10.52294/001...
Code (python/Matlab/C): github.com/DynamicsAndN...
We introduce an efficient set of statistical features for fMRI time series (calibrated on mouse manipulation experiments and tested on mouse and human data): catchaMouse16.
Paper: doi.org/10.52294/001...
Code (python/Matlab/C): github.com/DynamicsAndN...
July 15, 2025 at 11:47 PM
New paper!
We introduce an efficient set of statistical features for fMRI time series (calibrated on mouse manipulation experiments and tested on mouse and human data): catchaMouse16.
Paper: doi.org/10.52294/001...
Code (python/Matlab/C): github.com/DynamicsAndN...
We introduce an efficient set of statistical features for fMRI time series (calibrated on mouse manipulation experiments and tested on mouse and human data): catchaMouse16.
Paper: doi.org/10.52294/001...
Code (python/Matlab/C): github.com/DynamicsAndN...
Long-range connections play a large role in shaping dynamics for nearby spatially targeted inputs, but are less important for spatially diffuse inputs like in spontaneous dynamics.
This might help explain why we see stronger non-local dynamics in targeted stimulation experiments.
This might help explain why we see stronger non-local dynamics in targeted stimulation experiments.
June 25, 2025 at 5:03 AM
Long-range connections play a large role in shaping dynamics for nearby spatially targeted inputs, but are less important for spatially diffuse inputs like in spontaneous dynamics.
This might help explain why we see stronger non-local dynamics in targeted stimulation experiments.
This might help explain why we see stronger non-local dynamics in targeted stimulation experiments.
A few key results:
The role of long-range connections in shaping dynamics is heavily timescale dependent, concentrated on fast dynamics <~ 30 ms, while slow dynamics resemble the geometric model. This could help explain why they are less important for capturing slower rs-fMRI dynamics.
The role of long-range connections in shaping dynamics is heavily timescale dependent, concentrated on fast dynamics <~ 30 ms, while slow dynamics resemble the geometric model. This could help explain why they are less important for capturing slower rs-fMRI dynamics.
June 25, 2025 at 5:03 AM
A few key results:
The role of long-range connections in shaping dynamics is heavily timescale dependent, concentrated on fast dynamics <~ 30 ms, while slow dynamics resemble the geometric model. This could help explain why they are less important for capturing slower rs-fMRI dynamics.
The role of long-range connections in shaping dynamics is heavily timescale dependent, concentrated on fast dynamics <~ 30 ms, while slow dynamics resemble the geometric model. This could help explain why they are less important for capturing slower rs-fMRI dynamics.
So Rishi developed a model that allowed us to analyze long-range projections as a perturbation to geometric dynamics and thereby better understand why they might play different roles in different settings.
The model dynamics combine wave propagation with 'worm-hole' shortcuts 🕳️🐛
The model dynamics combine wave propagation with 'worm-hole' shortcuts 🕳️🐛
June 25, 2025 at 5:03 AM
So Rishi developed a model that allowed us to analyze long-range projections as a perturbation to geometric dynamics and thereby better understand why they might play different roles in different settings.
The model dynamics combine wave propagation with 'worm-hole' shortcuts 🕳️🐛
The model dynamics combine wave propagation with 'worm-hole' shortcuts 🕳️🐛
And yet in many experiments, most compellingly in fMRI, 'smearing out' the connectome into a geometric average yields traveling wave dynamics that are a surprisingly strong approximation for data (cf. www.nature.com/articles/s41...)
June 25, 2025 at 5:03 AM
And yet in many experiments, most compellingly in fMRI, 'smearing out' the connectome into a geometric average yields traveling wave dynamics that are a surprisingly strong approximation for data (cf. www.nature.com/articles/s41...)
Long-range connections facilitate rapid communication between networks of distributed cortical populations, are costly, heritable, and we clearly see their functional role in shaping dynamics experimentally, like the rapid non-local 'wormhole' in this whisker-stimulation experiment
June 25, 2025 at 5:03 AM
Long-range connections facilitate rapid communication between networks of distributed cortical populations, are costly, heritable, and we clearly see their functional role in shaping dynamics experimentally, like the rapid non-local 'wormhole' in this whisker-stimulation experiment
New preprint!
Why are long-range connectomic interactions in the cortex dominant in shaping dynamics in some experiments but apparently negligible in others?
We (w/ R Maran, @elimuller.bsky.social) address this question by studying a new hybrid model of cortical dynamics.
arxiv.org/abs/2506.19800
Why are long-range connectomic interactions in the cortex dominant in shaping dynamics in some experiments but apparently negligible in others?
We (w/ R Maran, @elimuller.bsky.social) address this question by studying a new hybrid model of cortical dynamics.
arxiv.org/abs/2506.19800
June 25, 2025 at 5:03 AM
New preprint!
Why are long-range connectomic interactions in the cortex dominant in shaping dynamics in some experiments but apparently negligible in others?
We (w/ R Maran, @elimuller.bsky.social) address this question by studying a new hybrid model of cortical dynamics.
arxiv.org/abs/2506.19800
Why are long-range connectomic interactions in the cortex dominant in shaping dynamics in some experiments but apparently negligible in others?
We (w/ R Maran, @elimuller.bsky.social) address this question by studying a new hybrid model of cortical dynamics.
arxiv.org/abs/2506.19800
We find that it works: once trained, our MPS approach, "MPSTime", can efficiently learn a time-series model that can be used for time-series classification, imputation of missing data, and synthetic data generation. We demonstrate on synthetic data, medical, industrial, and astronomical data.
May 13, 2025 at 5:15 AM
We find that it works: once trained, our MPS approach, "MPSTime", can efficiently learn a time-series model that can be used for time-series classification, imputation of missing data, and synthetic data generation. We demonstrate on synthetic data, medical, industrial, and astronomical data.
We draw an analogy between 1-dimensional (in space) spin chains and 1-dimensional (in time) sequences of measurements: time series. The main idea is to see if a method developed in quantum mechanics—the matrix product state (MPS)—can encode complex correlation structures in time series
May 13, 2025 at 5:15 AM
We draw an analogy between 1-dimensional (in space) spin chains and 1-dimensional (in time) sequences of measurements: time series. The main idea is to see if a method developed in quantum mechanics—the matrix product state (MPS)—can encode complex correlation structures in time series
New preprint!: "Using matrix-product states for time-series machine learning".
arxiv.org/abs/2412.15826
Quick summary below 👇
arxiv.org/abs/2412.15826
Quick summary below 👇
May 13, 2025 at 5:15 AM
New preprint!: "Using matrix-product states for time-series machine learning".
arxiv.org/abs/2412.15826
Quick summary below 👇
arxiv.org/abs/2412.15826
Quick summary below 👇
New preprint by Rishi Maran, Eli Muller:
"Analyzing the Brain's Dynamic Response to Targeted Stimulation using Generative Modeling"
A review/perspective on why new mechanisms may be found by modeling brain stimulation dynamics 🧠⚡️
arxiv.org/abs/2407.19737
"Analyzing the Brain's Dynamic Response to Targeted Stimulation using Generative Modeling"
A review/perspective on why new mechanisms may be found by modeling brain stimulation dynamics 🧠⚡️
arxiv.org/abs/2407.19737
August 5, 2024 at 12:44 AM
New preprint by Rishi Maran, Eli Muller:
"Analyzing the Brain's Dynamic Response to Targeted Stimulation using Generative Modeling"
A review/perspective on why new mechanisms may be found by modeling brain stimulation dynamics 🧠⚡️
arxiv.org/abs/2407.19737
"Analyzing the Brain's Dynamic Response to Targeted Stimulation using Generative Modeling"
A review/perspective on why new mechanisms may be found by modeling brain stimulation dynamics 🧠⚡️
arxiv.org/abs/2407.19737
New preprint: "Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI"
biorxiv.org/content/10.1...
We found a reduced set of time-series features relevant to fMRI (trained in mouse).
Code: github.com/DynamicsAndN...
biorxiv.org/content/10.1...
We found a reduced set of time-series features relevant to fMRI (trained in mouse).
Code: github.com/DynamicsAndN...
July 18, 2024 at 11:39 PM
New preprint: "Canonical time-series features for characterizing biologically informative dynamical patterns in fMRI"
biorxiv.org/content/10.1...
We found a reduced set of time-series features relevant to fMRI (trained in mouse).
Code: github.com/DynamicsAndN...
biorxiv.org/content/10.1...
We found a reduced set of time-series features relevant to fMRI (trained in mouse).
Code: github.com/DynamicsAndN...
Latest preprint: "Parameter Inference from a Non-stationary Unknown Process" (PINUP)
We unify a previously disjoint literature on algorithms for this important problem and introduce new benchmarking results.
arxiv.org/abs/2407.089...
#timeseriesanalysis #complexsystems
We unify a previously disjoint literature on algorithms for this important problem and introduce new benchmarking results.
arxiv.org/abs/2407.089...
#timeseriesanalysis #complexsystems
July 15, 2024 at 6:44 AM
Latest preprint: "Parameter Inference from a Non-stationary Unknown Process" (PINUP)
We unify a previously disjoint literature on algorithms for this important problem and introduce new benchmarking results.
arxiv.org/abs/2407.089...
#timeseriesanalysis #complexsystems
We unify a previously disjoint literature on algorithms for this important problem and introduce new benchmarking results.
arxiv.org/abs/2407.089...
#timeseriesanalysis #complexsystems
Annie Bryant is presenting her method to extract interpretable dynamical patterns from fMRI time series at #OHBM at:
—'Transdiagnostic Perspectives on Neurodevelopmental and Psychiatric Disorders - Part 1' 12pm oral session (Tue)
—Poster #1740 (Wed/Thurs)
www.biorxiv.org/content/10.1...
—'Transdiagnostic Perspectives on Neurodevelopmental and Psychiatric Disorders - Part 1' 12pm oral session (Tue)
—Poster #1740 (Wed/Thurs)
www.biorxiv.org/content/10.1...
June 21, 2024 at 7:09 AM
Annie Bryant is presenting her method to extract interpretable dynamical patterns from fMRI time series at #OHBM at:
—'Transdiagnostic Perspectives on Neurodevelopmental and Psychiatric Disorders - Part 1' 12pm oral session (Tue)
—Poster #1740 (Wed/Thurs)
www.biorxiv.org/content/10.1...
—'Transdiagnostic Perspectives on Neurodevelopmental and Psychiatric Disorders - Part 1' 12pm oral session (Tue)
—Poster #1740 (Wed/Thurs)
www.biorxiv.org/content/10.1...
Great work devised and undertaken by Fabiano Baroni comparing >100 statistics of dynamic structure (of synchrony, oscillations, phase relationships, spiking intensity, and variability) of multineuron spike trains, and evaluating them on synthetic and real-world data.
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
May 9, 2024 at 3:50 AM
Great work devised and undertaken by Fabiano Baroni comparing >100 statistics of dynamic structure (of synchrony, oscillations, phase relationships, spiking intensity, and variability) of multineuron spike trains, and evaluating them on synthetic and real-world data.
www.biorxiv.org/content/10.1...
www.biorxiv.org/content/10.1...
New preprint:
"Tracking the distance to criticality in systems with unknown noise"
By Brendan Harris, w/ Leonardo Gollo.
We identify new, noise-robust time-series features for tracking the distance to criticality
Paper: arxiv.org/abs/2310.14791
Code: github.com/brendanjohnh...
#ComplexSystems
"Tracking the distance to criticality in systems with unknown noise"
By Brendan Harris, w/ Leonardo Gollo.
We identify new, noise-robust time-series features for tracking the distance to criticality
Paper: arxiv.org/abs/2310.14791
Code: github.com/brendanjohnh...
#ComplexSystems
October 25, 2023 at 1:55 AM
New preprint:
"Tracking the distance to criticality in systems with unknown noise"
By Brendan Harris, w/ Leonardo Gollo.
We identify new, noise-robust time-series features for tracking the distance to criticality
Paper: arxiv.org/abs/2310.14791
Code: github.com/brendanjohnh...
#ComplexSystems
"Tracking the distance to criticality in systems with unknown noise"
By Brendan Harris, w/ Leonardo Gollo.
We identify new, noise-robust time-series features for tracking the distance to criticality
Paper: arxiv.org/abs/2310.14791
Code: github.com/brendanjohnh...
#ComplexSystems
New paper: "Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions" in
@elife.bsky.social
elifesciences.org/articles/78620
@elife.bsky.social
elifesciences.org/articles/78620
October 16, 2023 at 8:45 AM
New paper: "Neuromodulation of striatal D1 cells shapes BOLD fluctuations in anatomically connected thalamic and cortical regions" in
@elife.bsky.social
elifesciences.org/articles/78620
@elife.bsky.social
elifesciences.org/articles/78620
catch22 now has a logo!
Fast time-series feature-extraction in C, python, Julia, R, and Matlab.
github.com/DynamicsAndN...
Fast time-series feature-extraction in C, python, Julia, R, and Matlab.
github.com/DynamicsAndN...
October 13, 2023 at 2:18 AM
catch22 now has a logo!
Fast time-series feature-extraction in C, python, Julia, R, and Matlab.
github.com/DynamicsAndN...
Fast time-series feature-extraction in C, python, Julia, R, and Matlab.
github.com/DynamicsAndN...
Some of my time-series feature extraction art now up on the walls of the School of Physics :)
October 12, 2023 at 3:51 AM
Some of my time-series feature extraction art now up on the walls of the School of Physics :)
Teacher point of view: setting a 24 h timed assignment
October 2, 2023 at 5:02 AM
Teacher point of view: setting a 24 h timed assignment