paper-with-me

Papers

Chaos stabilizes synchronization in systems of coupled inner-ear hair cells

2020-12-08 · Justin Faber, Hancheng Li, Dolores Bozovic

Hair cells of the auditory and vestibular systems display astonishing sensitivity, frequency selectivity, and temporal resolution to external signals. These specialized cells utilize an internal active amplifier to achieve highly sensitive mechanical detection. One of the manifestations of this active process is the occurrence of spontaneous limit-cycle motion of the hair cell bundle. As hair bundles under in vivo conditions are typically coupled to each other by overlying structures, we explore the role of this coupling on the dynamics of the system, using a combination of theoretical and experimental approaches. Our numerical model suggests that the presence of chaotic dynamics in the response of individual bundles enhances their ability to synchronize when coupled, resulting in significant improvement in the system's ability to detect weak signals. This synchronization persists even for a large frequency dispersion and a large number of oscillators comprising the system. Further, the amplitude and coherence of the active motion is not reduced upon increasing the number of oscillators. Using artificial membranes, we impose mechanical coupling on groups of live and functional hair bundles, selected from in vitro preparations of the sensory epithelium, allowing us to explore the role of coupling experimentally. Consistent with the numerical simulations of the chaotic system, synchronization occurs even for large frequency dispersion and a large number of hair cells. Further, the amplitude and coherence of the spontaneous oscillations are independent of the number of hair cells in the network. We therefore propose that hair cells utilize their chaotic dynamics to stabilize the synchronized state and avoid the amplitude death regime, resulting in collective coherent motion that could play a role in generating spontaneous otoacoustic emissions and an enhanced ability to detect weak signals.

📄 PDF Abstract BibTeX arXiv:2012.04761

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Chaos in Homeostatically Regulated Neural Systems

2018-01-30

Low-dimensional yet rich dynamics often emerge in the brain. Examples include oscillations and chaotic dynamics during sleep, epilepsy, and voluntary movement. However, a general mechanism for the emergence of low dimens…

Decoupled DiLoCo for Resilient Distributed Pre-training

2026-04-23 · Arthur Douillard, Keith Rush, Yani Donchev, Zachary Charles 외 arxiv

Modern large-scale language model pre-training relies heavily on the single program multiple data (SPMD) paradigm, which requires tight coupling across accelerators. Due to this coupling, transient slowdowns, hardware fa…

Reservoir Computing based on Quenched Chaos

2019-09-04 · Jaesung Choi, Pilwon Kim

Reservoir computing(RC) is a brain-inspired computing framework that employs a transient dynamical system whose reaction to an input signal is transformed to a target output. One of the central problems in RC is to find …

Cooperators overcome migration dilemma through synchronization

2021-02-17 · Shubhadeep Sadhukhan, Rohitashwa Chattopadhyay, Sagar Chakraborty

Synchronization, cooperation, and chaos are ubiquitous phenomena in nature. In a population composed of many distinct groups of individuals playing the prisoner's dilemma game, there exists a migration dilemma: No cooper…

CHAOS: A Parallelization Scheme for Training Convolutional Neural Networks on Intel Xeon Phi

2017-02-25 · Andre Viebke, Suejb Memeti, Sabri Pllana, Ajith Abraham

Deep learning is an important component of big-data analytic tools and intelligent applications, such as, self-driving cars, computer vision, speech recognition, or precision medicine. However, the training process is co…

Self-Driving Carsspeech-recognitionSpeech Recognition