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A non-Markovian counting process, the `generalized fractional Poisson process' (GFPP) introduced by Cahoy and Polito in 2013 is analyzed. The GFPP contains two index parameters $0<\beta\leq 1$, $\alpha >0$ and a time scale parameter.…

Statistical Mechanics · Physics 2020-04-22 Thomas M. Michelitsch , Alejandro P. Riascos

Noninvasive brain stimulation can activate neurons in the brain but requires power electronics with exceptionally high power in the mega-volt-ampere and high frequencies in the kilohertz range. Whereas oscillator circuits offered only one…

Medical Physics · Physics 2025-03-11 Jinshui Zhang , Angel Peterchev , Stefan Goetz

Today's deep learning architectures are primarily based on perceptron models, which do not capture the oscillatory dynamics characteristic of biological neurons. Although oscillatory systems have recently gained attention for their closer…

Machine Learning · Computer Science 2025-10-14 Zhongju Yuan , Geraint Wiggins , Dick Botteldooren

Learning control policies for real-world robotic tasks often involve challenges such as multimodality, local discontinuities, and the need for computational efficiency. These challenges arise from the complexity of robotic environments,…

Robotics · Computer Science 2025-02-05 Shu-yuan Wang , Hikaru Sasaki , Takamitsu Matsubara

Traditional top-down robotic design often lacks the adaptability needed to handle real-world complexities, prompting the need for more flexible approaches. Therefore, this study introduces a novel cellular plasticity model tailored for…

Robotics · Computer Science 2024-08-13 Trevor R. Smith , Thomas J. Smith , Nicholas S. Szczecinski , Sergiy Yakovenko , Yu Gu

Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples. Neural machine learning models, including the now ubiquitous Transformers, struggle to generalize in this way, and…

Machine Learning · Computer Science 2024-01-19 Tim Klinger , Luke Liu , Soham Dan , Maxwell Crouse , Parikshit Ram , Alexander Gray

Within cardiovascular disease detection using deep learning applied to ECG signals, the complexities of handling physiological signals have sparked growing interest in leveraging deep generative models for effective data augmentation. In…

Computer Vision and Pattern Recognition · Computer Science 2024-05-06 Nour Neifar , Achraf Ben-Hamadou , Afef Mdhaffar , Mohamed Jmaiel

Cardiovascular diseases are the leading cause of death and disability in the world and thus their detection is extremely important as early as possible so that it can be prognosed and managed appropriately. Hence, electrophysiological…

Medical Physics · Physics 2024-03-07 Sourav Chowdhury , Apratim Ghosal , Suparna Roychowhury , Indranath Chaudhuri

Chaotic oscillators have gained significant attention in the research community because of their ability to reproduce and investigate the complex dynamics of real-world phenomena. Recent advances in the design of chaotic oscillator…

Chaotic Dynamics · Physics 2026-03-19 Toni Ivas , Georgios Violakis , Roland Richter , Patrik Hoffmann , Sergey Shevchik

This paper introduces a class of stochastic models of interacting neurons with emergent dynamics similar to those seen in local cortical populations, and compares them to very simple reduced models driven by the same mean excitatory and…

Neurons and Cognition · Quantitative Biology 2017-11-07 Yao Li , Logan Chariker , Lai-Sang Young

Stick insect stepping patterns have been studied for insights about locomotor rhythm generation and control, because the underlying neural system is relatively accessible experimentally and produces a variety of rhythmic outputs. Harnessing…

Neurons and Cognition · Quantitative Biology 2025-04-17 Zahra Aminzare , Jonathan E. Rubin

A piecewise continuous map for modeling bursting and spiking behaviour of isolated neuron is proposed. The map was created from phenomenological viewpoint. The map demonstrates oscillations, which are qualitatively similar to oscillations…

Chaotic Dynamics · Physics 2007-05-23 K. V. Andreev , L. V. Krasichkov

After decades of study, there are only two known mechanisms to induce global synchronization in a population of oscillators: deterministic coupling and common forcing. The inclusion of independent random forcing in these models typically…

Adaptation and Self-Organizing Systems · Physics 2023-03-31 Jeremy Worsfold , Tim Rogers

A model for cross-over designs with repeated measures within each period was developed. It is obtained using an extension of generalized estimating equations that includes a parametric component to model treatment effects and a…

Methodology · Statistics 2023-03-21 N. A. Cruz , O. O. Melo , C. A. Martinez

Cortical neurons include many sub-cellular processes, operating at multiple timescales, which may affect their response to stimulation through non-linear and stochastic interaction with ion channels and ionic concentrations. Since new…

Neurons and Cognition · Quantitative Biology 2014-05-01 Daniel Soudry , Ron Meir

Generative AI, in particular large transformer models, are increasingly driving HPC system design in science and industry. We analyze performance characteristics of such transformer models and discuss their sensitivity to the transformer…

We present the simplest discrete model to date that leads to synchronization of stochastic phase-coupled oscillators. In the mean field limit, the model exhibits a Hopf bifurcation and global oscillatory behavior as coupling crosses a…

Statistical Mechanics · Physics 2009-11-11 Kevin Wood , C. Van den Broeck , R. Kawai , Katja Lindenberg

A number of biological rhythms originate from networks comprised of multiple cellular oscillators. But analytical results are still lacking on the collective oscillation period of inter-coupled gene regulatory oscillators, which, as has…

Systems and Control · Computer Science 2014-02-26 Yongqiang Wang , Yutaka Hori , Shinji Hara , Francis J. Doyle

The activity of collections of synchronizing neurons can be represented by weakly coupled nonlinear phase oscillators satisfying Kuramoto's equations. In this article, we build such neural-oscillator models, partly based on…

Neurons and Cognition · Quantitative Biology 2012-04-27 Patrick Suppes , Jose Acacio de Barros , Gary Oas

Long term behavior of nonlinear deterministic continuous time signals can be studied in terms of their reconstructed attractors. Reconstructed attractors of a continuous signal are meant to be topologically equivalent representations of the…