no code implementations • 1 Apr 2022 • Yuzhen Qin, Danielle S. Bassett, Fabio Pasqualetti
Cluster synchronization underlies various functions in the brain.
no code implementations • 15 Mar 2021 • Pragya Srivastava, Peter J. Mucha, Emily Falk, Fabio Pasqualetti, Danielle S. Bassett
For this purpose, we calculate the exact expression of optimal control energy in terms of layer spectra and the relative alignment between the eigenmodes of the input layer and the deeper target layer.
no code implementations • 12 Jan 2021 • Abigail N. Poteshman, Mathieu Ouellet, Lee C. Bassett, Danielle S. Bassett
To elucidate and address this challenge, we study networks that represent non-equilibrium quantum-electronic transport through quantum antidot devices -- an example of an open, mesoscopic quantum system.
Mesoscale and Nanoscale Physics
1 code implementation • 11 Jan 2021 • Ann S. Blevins, Jason Z. Kim, Danielle S. Bassett
We address this problem by examining the higher-order structure of noisy, weak edges added to model networks.
no code implementations • 31 Dec 2020 • Alec Helm, Ann S. Blevins, Danielle S. Bassett
Our results suggest that the connectomes growing topology is a robust feature of the developing connectome that is distinct from other network properties, and that the growing topology is particularly sensitive to the exact birth times of a small set of predominantly motor neurons.
no code implementations • 21 Dec 2020 • Tiziana Cattai, Gaetano Scarano, Marie-Constance Corsi, Danielle S. Bassett, Fabrizio De Vico Fallani, Stefania Colonnese
Using our novel formulation of the J-divergence, we are able to quantify the distance between the FC networks in the motor imagery and resting states, as well as to understand the contribution of each Laplacian variable to the total J-divergence between two states.
no code implementations • 8 Dec 2020 • Yuzhen Qin, Tommaso Menara, Danielle S. Bassett, Fabio Pasqualetti
Phase-amplitude coupling (PAC) describes the phenomenon where the power of a high-frequency oscillation evolves with the phase of a low-frequency one.
no code implementations • 26 Oct 2020 • Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Nathalie George, Laurent Hugueville, Ari E. Kahn, Sophie Dupont, Danielle S. Bassett, Fabrizio De Vico Fallani
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control.
1 code implementation • 16 Oct 2020 • Harang Ju, Dale Zhou, Ann S. Blevins, David M. Lydon-Staley, Judith Kaplan, Julio R. Tuma, Danielle S. Bassett
Philosophers of science have long postulated how collective scientific knowledge grows.
Digital Libraries History and Philosophy of Physics
no code implementations • 4 Jun 2020 • Leo Torres, Ann S. Blevins, Danielle S. Bassett, Tina Eliassi-Rad
At each step we consider different types of \emph{dependencies}; these are properties of the system that describe how the existence of one relation among the parts of a system may influence the existence of another relation.
Social and Information Networks Discrete Mathematics Quantitative Methods 68R10
no code implementations • 4 Jun 2020 • Dale Zhou, David M. Lydon-Staley, Perry Zurn, Danielle S. Bassett
The practice of curiosity can be viewed as an extended and open-ended search for valuable information with hidden identity and location in a complex space of interconnected information.
no code implementations • 3 May 2020 • Jason Z. Kim, Zhixin Lu, Erfan Nozari, George J. Pappas, Danielle S. Bassett
Here we demonstrate that a recurrent neural network (RNN) can learn to modify its representation of complex information using only examples, and we explain the associated learning mechanism with new theory.
no code implementations • 16 Feb 2020 • Shubhankar P. Patankar, Jason Z. Kim, Fabio Pasqualetti, Danielle S. Bassett
Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well-understood.
1 code implementation • 14 Jan 2020 • Dale Zhou, Christopher W. Lynn, Zaixu Cui, Rastko Ciric, Graham L. Baum, Tyler M. Moore, David R. Roalf, John A. Detre, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, Danielle S. Bassett
In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks.
no code implementations • 5 Dec 2019 • Tiziana Cattai, Stefania Colonnese, Marie-Constance Corsi, Danielle S. Bassett, Gaetano Scarano, Fabrizio De Vico Fallani
In the last decade, functional connectivity (FC) estimators have been increasingly explored based on their ability to capture synchronization between multivariate brain signals.
1 code implementation • 14 Aug 2019 • Nicolas H. Christianson, Ann Sizemore Blevins, Danielle S. Bassett
Knowledge is a network of interconnected concepts.
1 code implementation • 8 Feb 2019 • Jason Z. Kim, Danielle S. Bassett
The brain is an intricately structured organ responsible for the rich emergent dynamics that support the complex cognitive functions we enjoy as humans.
no code implementations • 6 Dec 2018 • Hao-Ting Wang, Jonathan Smallwood, Janaina Mourao-Miranda, Cedric Huchuan Xia, Theodore D. Satterthwaite, Danielle S. Bassett, Danilo Bzdok
Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly.
no code implementations • 20 Jul 2018 • Ursula A. Tooley, Allyson P. Mackey, Rastko Ciric, Kosha Ruparel, Tyler M. Moore, Ruben C. Gur, Raquel E. Gur, Theodore D. Satterthwaite, Danielle S. Bassett
We quantitatively characterize this topology using a local measure of network segregation known as the clustering coefficient, and find that it accounts for a greater degree of SES-associated variance than meso-scale segregation captured by modularity.
Neurons and Cognition
no code implementations • 31 May 2018 • Christopher W. Lynn, Ari E. Kahn, Danielle S. Bassett
Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood.
no code implementations • 7 Jan 2016 • Chad Giusti, Robert Ghrist, Danielle S. Bassett
Specifically, we explore the use of \emph{simplicial complexes}, a theoretical notion developed in the field of mathematics known as algebraic topology, which is now becoming applicable to real data due to a rapidly growing computational toolset.
Neurons and Cognition Algebraic Topology Quantitative Methods 92-02, 92B20, 57Q05
1 code implementation • 8 May 2015 • Sarah Feldt Muldoon, Eric W. Bridgeford, Danielle S. Bassett
Quantitative descriptions of network structure in big data can provide fundamental insights into the function of interconnected complex systems.