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The dynamics of complex systems generally include high-dimensional, non-stationary and non-linear behavior, all of which pose fundamental challenges to quantitative understanding. To address these difficulties we detail a new approach based…

Quantitative Methods · Quantitative Biology 2020-09-11 Antonio Carlos Costa , Tosif Ahamed , Greg J. Stephens

All vertebrates are capable of performing various types of physical activity. Locomotor patterns are created by the cyclical coordinated work of the skeletal muscles. The organization of such a system in living organisms is responsible for…

Neurons and Cognition · Quantitative Biology 2021-12-15 A. I. Zharinov , Y. A. Tsybina , S. Y. Gordleeva

Sensory receptor neurons match their dynamic range to ecologically relevant stimulus intensities. How this tuning is achieved is poorly understood in most receptors. We show that in the C. elegans URX O2 sensing neurons two putative…

Neurons and Cognition · Quantitative Biology 2022-10-12 Shigekazu Oda , Yu Toyoshima , Mario de Bono

Neuromorphic computing systems, where information is transmitted through action potentials in a bio-plausible fashion, is gaining increasing interest due to its promise of low-power event-driven computing. Application of neuromorphic…

Neural and Evolutionary Computing · Computer Science 2025-09-03 Zhuangyu Han , Abhronil Sengupta

Rapid advances in genetics, genomics, and imaging have given insight into the molecular and cellular basis of behaviour in a variety of model organisms with unprecedented detail and scope. It is increasingly routine to isolate behavioural…

Quantitative Methods · Quantitative Biology 2013-01-08 André E. X. Brown , William R. Schafer

We exploit the reduced space of C. elegans postures to develop a novel tracking algorithm which captures both simple shapes and also self-occluding coils, an important, yet unexplored, component of worm behavior. We apply our algorithm to…

Quantitative Methods · Quantitative Biology 2016-11-01 Onno D Broekmans , Jarlath B Rodgers , William S Ryu , Greg J Stephens

We present an automated method to track and identify neurons in C. elegans, called "fast Deep Learning Correspondence" or fDLC, based on the transformer network architecture. The model is trained once on empirically derived synthetic data…

Quantitative Methods · Quantitative Biology 2021-07-16 Xinwei Yu , Matthew S. Creamer , Francesco Randi , Anuj K. Sharma , Scott W. Linderman , Andrew M. Leifer

Humanoid robots are made to resemble humans but their locomotion abilities are far from ours in terms of agility and versatility. When humans walk on complex terrains, or face external disturbances, they combine a set of strategies,…

Robotics · Computer Science 2021-10-28 Mohammadreza Kasaei , Miguel Abreu , Nuno Lau , Artur Pereira , Luis Paulo Reis

Gastrulation, namely cell internalization, is a significant milestone during the development of metazoans from worm to human, which generates multiple embryonic layers with distinct cell fates and spatial organizations. Although many…

Cell Behavior · Quantitative Biology 2022-01-17 Jiao Miao , Guoye Guan , Chao Tang

Rapid transduction of sensory stimulation to action is essential for an animal to survive. To this end, most animals use the sub-second excitable and multistable dynamics of a neuromuscular system. Here, studying an animal without neurons…

Biological Physics · Physics 2021-07-08 Matthew S. Bull , Laurel A. Kroo , Manu Prakash

Complex spin textures in itinerant electron magnets hold promises for next-generation memory and information technology. The long-ranged and often frustrated electron-mediated spin interactions in these materials give rise to intriguing…

Strongly Correlated Electrons · Physics 2024-06-18 Xinlun Cheng , Sheng Zhang , Phong C. H. Nguyen , Shahab Azarfar , Gia-Wei Chern , Stephen S. Baek

Maxmin-$\omega$ is a new threshold model, where each node in a network waits for the arrival of states from a fraction $\omega$ of neighborhood nodes before processing its own state, and subsequently transmitting it to downstream nodes.…

Physics and Society · Physics 2021-09-24 Ebrahim L. Patel

We study external, electrical perturbations and their responses in the brain dynamic network of the \textit{Caenorhabditis elegans} soil worm, given by the connectome of its large somatic nervous system. Our analysis is inspired by a…

Neurons and Cognition · Quantitative Biology 2016-02-17 Chris G. Antonopoulos

The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model…

Neurons and Cognition · Quantitative Biology 2019-06-19 Bryan C. Daniels , William S. Ryu , Ilya Nemenman

We show that the flagellar beat of bull spermatozoa and Chlamydomonas Reinhardtii can be modelled by a minimal, geometrically nonlinear reaction-diffusion system. Model solutions are spatio-temporally animated patterns describing flagellar…

Biological Physics · Physics 2023-10-09 James F. Cass , Hermes Bloomfield-Gadelha

Legged locomotion is a challenging task in the field of robotics but a rather simple one in nature. This motivates the use of biological methodologies as solutions to this problem. Central pattern generators are neural networks that are…

Neural and Evolutionary Computing · Computer Science 2020-03-18 Elie Aljalbout , Florian Walter , Florian Röhrbein , Alois Knoll

Central Pattern Generators (CPGs) form the neural basis of the observed rhythmic behaviors for locomotion in legged animals. The CPG dynamics organized into networks allow the emergence of complex locomotor behaviors. In this work, we take…

Robotics · Computer Science 2023-03-03 Aditya M. Deshpande , Eric Hurd , Ali A. Minai , Manish Kumar

The effects of fluid viscosity on the kinematics of a small swimmer at low Reynolds number are investigated in both experiments and in a simple model. The swimmer is the nematode Caenorhabditis elegans, which is an undulating roundworm…

Biological Physics · Physics 2009-12-18 Josue Sznitman , Xiaoning Shen , Prashant K Purohit , Paulo E Arratia

We propose an effective way to create interpretable control agents, by re-purposing the function of a biological neural circuit model, to govern simulated and real world reinforcement learning (RL) test-beds. We model the tap-withdrawal…

Neurons and Cognition · Quantitative Biology 2018-03-26 Mathias Lechner , Ramin M. Hasani , Radu Grosu

Recordings of whole brain activity with single neuron resolution are now feasible in simple organisms. Yet, it is still challenging to appropriately simplify such complex, noisy, and multivariate data in order to reveal general principles…

Neurons and Cognition · Quantitative Biology 2017-11-27 Connor Brennan , Alex Proekt
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