13 papers · ranked by Valyu relevance
Yarden Cohen, Predrag Cvitanović, Sara A. Solla
Learning to execute precise, yet complex, motor actions through practice is a trait shared by most organisms. Here we develop a novel experimental approach for the comprehensive investigation and characterization of the learning dynamics of practiced motion. Following the dynamical systems framework, we consider a…
Alejandro Apolinar-Fernández, Jorge Barrasa-Fano, Mar Cóndor, Hans Van Oosterwyck + 1 more
Traction force microscopy (TFM) allows to estimate tractions on the surface of cells when they mechanically interact with hydrogel substrates that mimic the extracellular matrix (ECM). The field of mechanobiology has a strong interest in using TFM in 3D in vitro models. However, there are a number of challenges that…
Erdem Pulcu
We are living in a dynamic world in which stochastic relationships between cues and outcome events create different sources of uncertainty^1^ (e.g. the fact that not all grey clouds bring rain). Living in an uncertain world continuously probes learning systems in the brain, guiding agents to make better decisions. This…
H. van de Beek, M. Beldjenna, M. Fidler, L.B. Zwep + 1 more
Asymptotic standard errors for the parameters of a nonlinear mixed-effects model fitted by first-order conditional estimation (FOCE) or FOCE with interaction (FOCEI) require the observed (Fisher) information — the negative second derivative of the population objective at the optimum. The gradient of this objective can…
Qianli Yang, Edgar Walker, R. James Cotton, Andreas S. Tolias + 1 more
Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our…
Erfan Nozari, Jennifer Stiso, Lorenzo Caciagli, Eli J. Cornblath + 5 more
A central challenge in the computational modeling of neural dynamics is the trade-off between accuracy and simplicity. At the level of individual neurons, nonlinear dynamics are both experimentally established and essential for neuronal functioning. One may therefore expect the collective dynamics of massive networks…
Ikechukwu I. Udema
A burning concern among researchers studying enzyme kinetics has been ways of improving the accuracy of initial rates (v) with much greater precision. The goal of this study was to establish a formal (mathematical) way of achieving more accurate v values in enzyme assay. By adopting Bernfeld method of assay, the v…
Ying Wang, Min Li, Ronaldo García Reyes, Deirel Paz-Linares + 4 more
Parameterizing electroencephalography (EEG) signals in the spectral domain reveals physiologically relevant components of neural stochastic processes, yet the linearity or nonlinearity of these components remains debated and could not solved by the current Spectral Parameter Analysis (SPA). We address this using BiSCA…
Daniel Kaschek, Wolfgang Mader, Mirjam Fehling-Kaschek, Marcus Rosenblatt + 1 more
In a wide variety of research elds, dynamic modeling is employed as an instrument to learn and understand complex systems. The differential equations involved in this process are usually non-linear and depend on many parameters whose values decide upon the characteristics of the emergent system. The inverse problem…
Aishani Ghosal, Yu-Huan Wang, Nguyen Nguyen, Laura Troyer + 1 more
Advances in fluorescence microscopy have enabled high-resolution tracking of individual biomolecules in living cells. However, accurate estimation of diffusion parameters from single-particle trajectories remains challenging due to static and dynamic localization errors inherent in these measurements. While previous…
Chikoo Oosawa
Biochemical Systems Theory (BST) represents nonlinear biochemical rate laws by local power-law approximations in logarithmic concentration coordinates. First-order coefficients are elasticities, whereas higher-order derivatives describe local log-synergism and its variation. Ordinary higher derivatives, however, are…
Fabian Fröhlich, Peter K. Sorger
Ordinary differential equation (ODE) models are widely used to describe biochemical processes, since they effectively represent mass action kinetics. Optimization-based calibration of ODE models on experimental data can be challenging, even for low-dimensional problems. However, reliable model calibration is a…
C. H. Fleming, J. Drescher-Lehman, M. J. Noonan, T. S. B. Akre + 34 more
Animal tracking data are being collected more frequently, in greater detail, and on smaller taxa than ever before. These data hold the promise to increase the relevance of animal movement for understanding ecological processes, but this potential will only be fully realized if their accompanying location error is…