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We analyze a model of mutually-propelled filaments suspended in a two-dimensional solvent. The system undergoes a mean-field isotropic-nematic transition for large enough filament concentrations and the nematic order parameter is allowed to…

Soft Condensed Matter · Physics 2011-05-26 L. Giomi , L. Mahadevan , B. Chakraborty , M. F. Hagan

We show that \emph{stochastic bursting} is observed in a ring of unidirectional delay-coupled noisy excitable systems, thanks to the combinational action of time-delayed coupling and noise. Under the approximation of timescale separation,…

Disordered Systems and Neural Networks · Physics 2019-05-01 Chunming Zheng , Arkady Pikovsky

Score-based diffusion models generate new samples by learning the score function associated with a diffusion process. While the effectiveness of these models can be theoretically explained using differential equations related to the…

Machine Learning · Computer Science 2026-01-21 Doheon Kim

We present a theoretical application of an optimal experiment design (OED) methodology to the development of mathematical models to describe the stimulus-response relationship of sensory neurons. Although there are a few related studies in…

Neurons and Cognition · Quantitative Biology 2016-10-19 R. Ozgur Doruk , Kechen Zhang

We present the first exact analysis of some of the temporal properties of multivariate self-excited Hawkes conditional Poisson processes, which constitute powerful representations of a large variety of systems with bursty events, for which…

Statistical Mechanics · Physics 2014-08-26 A. Saichev , D. Sornette

We develop an information-theoretic formulation of stochastic dynamics in which the fundamental stochastic variable is the total action connecting spacetime points, rather than individual paths. By maximizing Shannon entropy over a joint…

We study the dynamical rheology of spring networks with a percolation model constructed by bond dilution in a two-dimensional triangular lattice. Hydrodynamic interactions are implemented by a Stokesian viscous coupling between the network…

Soft Condensed Matter · Physics 2013-04-26 M. G. Yucht , M. Sheinman , C. P. Broedersz

We analyze states of stationary activity in randomly coupled quadratic integrate-and-fire neurons using stochastic mean-field theory. Specifically, we consider the two cases of Gaussian random coupling and Cauchy random coupling, which are…

Disordered Systems and Neural Networks · Physics 2026-05-13 Ralf Tönjes , Chunming Zheng , Wenping Cui , Benjamin Lindner

Biochemical reaction networks are subjected to large fluctuations attributable to small molecule numbers, yet underlie reliable biological functions. Most theoretical approaches describe them as purely deterministic or stochastic dynamical…

Molecular Networks · Quantitative Biology 2013-06-11 Jingkui Wang , Marc Lefranc , Quentin Thommen

We study collective dynamics of complex networks of stochastic excitable elements, active rotators. In the thermodynamic limit of infinite number of elements, we apply a mean-field theory for the network and then use a Gaussian…

Chaotic Dynamics · Physics 2013-11-19 Bernard Sonnenschein , Michael A. Zaks , Alexander B. Neiman , Lutz Schimansky-Geier

In this work, we consider the problem of identifying an unknown linear dynamical system given a finite hypothesis class. In particular, we analyze the effect of the excitation input on the sample complexity of identifying the true system…

Systems and Control · Electrical Eng. & Systems 2026-01-30 Nicolas Chatzikiriakos , Kevin Jamieson , Andrea Iannelli

We present a simple Markov model of spiking neural dynamics that can be analytically solved to characterize the stochastic dynamics of a finite-size spiking neural network. We give closed-form estimates for the equilibrium distribution,…

Neurons and Cognition · Quantitative Biology 2007-05-23 H. Soula , C. C. Chow

We study the activation process in large assemblies of type II excitable units whose dynamics is influenced by two independent noise terms. The mean-field approach is applied to explicitly demonstrate that the assembly of excitable units…

Chaotic Dynamics · Physics 2016-04-19 Igor Franović , Matjaz Perc , Kristina Todorović , Srđan Kostić , Nikola Burić

Experimental manipulations perturb the neuronal activity. This phenomenon is manifested in the fMRI response. Dynamic causal model and its variants can model these neuronal responses along with the BOLD responses [1, 2, 3, 4, 5] .…

Neurons and Cognition · Quantitative Biology 2021-01-15 Sayan Nag

Time is at a premium for recurrent network dynamics, and particularly so when they are stochastic and correlated: the quality of inference from such dynamics fundamentally depends on how fast the neural circuit generates new samples from…

Neurons and Cognition · Quantitative Biology 2014-04-24 Guillaume Hennequin , Laurence Aitchison , Máté Lengyel

In a recent study the initial rise of the mutual information between the firing rates of N neurons and a set of p discrete stimuli has been analytically evaluated, under the assumption that neurons fire independently of one another to each…

Disordered Systems and Neural Networks · Physics 2009-11-07 Valeria Del Prete , Alessandro Treves

Motivated by recent experiments on intracellular calcium dynamics, we study the general issue of fluctuation-induced nucleation of waves in excitable media. We utilize a stochastic Fitzhugh-Nagumo model for this study, a spatially-extended…

Condensed Matter · Physics 2009-11-10 Herve Henry , Herbert Levine

We study synchronization in a system of phase-only oscillators residing on the sites of a one-dimensional periodic lattice. The oscillators interact with a strength that decays as a power law of the separation along the lattice length and…

Chaotic Dynamics · Physics 2013-12-04 Shamik Gupta , Max Potters , Stefano Ruffo

We study the asymptotic decay of the Friedel density oscillations induced by an open boundary in a one-dimensional chain of lattice fermions with a short-range two-particle interaction. From Tomonaga-Luttinger liquid theory it is known that…

Strongly Correlated Electrons · Physics 2020-06-24 Jovan Odavić , Nicole Helbig , Volker Meden

As data-driven methods are deployed in real-world settings, the processes that generate the observed data will often react to the decisions of the learner. For example, a data source may have some incentive for the algorithm to provide a…

Machine Learning · Computer Science 2023-04-26 Roy Dong , Heling Zhang , Lillian J. Ratliff