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Found 23 papers, 6 papers with code
Analytical Characterization of Epileptic Dynamics in a Bistable System
In this paper, we employ a bistable model, where a stable equilibrium and a stable limit cycle coexist, to describe epileptic dynamics.
Vibrational Control of Cluster Synchronization: Connections with Deep Brain Stimulation
Cluster synchronization underlies various functions in the brain.
Structural underpinnings of control in multiplex networks
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.
Network structure and dynamics of effective models of non-equilibrium quantum transport
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
Variability in higher order structure of noise added to weighted networks
We address this problem by examining the higher-order structure of noisy, weak edges added to model networks.
The growing topology of the C. elegans connectome
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.
Improving J-divergence of brain connectivity states by graph Laplacian denoising
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.
Phase-Amplitude Coupling in Neuronal Oscillator Networks
Phase-amplitude coupling (PAC) describes the phenomenon where the power of a high-frequency oscillation evolves with the phase of a low-frequency one.
BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control.
The network structure of scientific revolutions
Philosophers of science have long postulated how collective scientific knowledge grows.
Digital Libraries History and Philosophy of Physics
The growth and form of knowledge networks by kinesthetic curiosity
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.
The why, how, and when of representations for complex systems
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
Teaching Recurrent Neural Networks to Modify Chaotic Memories by Example
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.
Path-dependent connectivity, not modularity, consistently predicts controllability of structural brain networks
Yet, the precise relationship between community structure in structural brain networks and the communication dynamics that can emerge therefrom is not well-understood.
Efficient Coding in the Economics of Human Brain Connectomics
In doing so, we introduce the metric of compression efficiency, which quantifies the trade-off between lossy compression and transmission fidelity in structural networks.
Phase/amplitude synchronization of brain signals during motor imagery BCI tasks
In the last decade, functional connectivity (FC) estimators have been increasingly explored based on their ability to capture synchronization between multivariate brain signals.
Architecture and evolution of semantic networks in mathematics texts
Knowledge is a network of interconnected concepts.
Linear Dynamics & Control of Brain Networks
The brain is an intricately structured organ responsible for the rich emergent dynamics that support the complex cognitive functions we enjoy as humans.
Finding the needle in high-dimensional haystack: A tutorial on canonical correlation analysis
Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly.
Influence of Neighborhood SES on Functional Brain Network Development
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
Structure from noise: Mental errors yield abstract representations of events
Humans are adept at uncovering abstract associations in the world around them, yet the underlying mechanisms remain poorly understood.
Two's company, three (or more) is a simplex: Algebraic-topological tools for understanding higher-order structure in neural data
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
Small-World Propensity in Weighted, Real-World Networks
Quantitative descriptions of network structure in big data can provide fundamental insights into the function of interconnected complex systems.
