Chaos (Woodbury, N.Y.)
Mar 1, 2025
We propose a classification method for distinguishing atrial fibrillation from sinus rhythm in pulse-wave measurements obtained with a blood pressure monitor. This method combines recurrence-based plots with convolutional neural networks. Moreover, w...
Chaos (Woodbury, N.Y.)
Mar 1, 2025
For years, a main focus of ecological research has been to better understand the complex dynamical interactions between species that comprise food webs. Using the connectance properties of a widely explored synthetic food web called the cascade model...
Chaos (Woodbury, N.Y.)
Feb 1, 2025
Typical reservoir networks are based on random connectivity patterns that differ from brain circuits in two important ways. First, traditional reservoir networks lack synaptic plasticity among recurrent units, whereas cortical networks exhibit plasti...
Chaos (Woodbury, N.Y.)
Feb 1, 2025
Reinforcement learning (RL) is a powerful machine learning technique that has been successfully applied to a wide variety of problems. However, it can be unpredictable and produce suboptimal results in complicated learning environments. This is espec...
Chaos (Woodbury, N.Y.)
Dec 1, 2024
Complex networks are susceptible to contagious cascades, underscoring the urgency for effective epidemic mitigation strategies. While physical quarantine is a proven mitigation measure for mitigation, it can lead to substantial economic repercussions...
Chaos (Woodbury, N.Y.)
Aug 1, 2024
In biological neural networks, it has been well recognized that a healthy brain exhibits 1/f noise patterns. However, in artificial neural networks that are increasingly matching or even out-performing human cognition, this phenomenon has yet to be e...
Chaos (Woodbury, N.Y.)
Apr 1, 2024
Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the ...
Chaos (Woodbury, N.Y.)
Jan 1, 2024
We propose a machine-learning approach to construct reduced-order models (ROMs) to predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent high-dimensional fMRI ...
Chaos (Woodbury, N.Y.)
Sep 1, 2023
Up to now, it still remains an open question about the relation between the structure of brain networks and their functions. The effects of structure on the dynamics of neural networks are usually investigated via extensive numerical simulations, whi...
Chaos (Woodbury, N.Y.)
Jul 1, 2023
In this paper, we show the possibility of creating and identifying the features of an artificial neural network (ANN), which consists of mathematical models of biological neurons. The FitzHugh-Nagumo (FHN) system is used as a paradigmatic model demon...