Dendrites play an essential role in the integration of highly fluctuating input in vivo into neurons across all nervous systems. Yet, they are often studied under conditions where inputs to dendrites are sparse. The dynamic properties of active dendrites facing in vivo-like fluctuating input thus remain elusive. In this paper, we uncover dynamics in a canonical model of a dendritic compartment with active calcium channels, receiving in vivo-like fluctuating input. In a single-compartment model of the active dendrite with fast calcium activation, we show noise-induced nonmonotonic behavior in the relationship of the membrane potential output, and mean input emerges. In contrast, noise can induce bistability in the input-output relation in the system with slowly activating calcium channels. Both phenomena are absent in a noiseless condition. Furthermore, we show that timescales of the emerging stochastic bistable dynamics extend far beyond a deterministic system due to stochastic switching between the solutions. A numerical simulation of a multicompartment model neuron shows that in the presence of in vivo-like synaptic input, the bistability uncovered in our analysis persists. Our results reveal that realistic synaptic input contributes to sustained dendritic nonlinearities, and synaptic noise is a significant component of dendritic input integration.
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http://dx.doi.org/10.1073/pnas.2023381118 | DOI Listing |
J Biomech
January 2025
Sport and Physical Activity Research Centre, Sheffield Hallam University, Olympic Legacy Park, 2 Old Hall Rd, Sheffield S9 3TY, United Kingdom. Electronic address:
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January 2025
Department of Robotics, Hanyang University, Ansan, 15588, Republic of Korea.
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Key Laboratory of Concrete and Pre-Stressed Concrete Structures of the Ministry of Education, Southeast University, Nanjing 210096, China.
Monitoring existing cracks is a critical component of structural health monitoring in bridges, as temperature fluctuations significantly influence crack development. The study of the Huai'an Bridge indicated that concrete cracks predominantly occur near the central tower, primarily due to temperature variations between the inner and outer surfaces. This research aims to develop a deep learning model utilizing Long Short-Term Memory (LSTM) neural networks to predict crack depth based on the thermal variations experienced by the main tower.
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Key Laboratory of Marine Ecosystem Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China.
Xiangshan Bay, one of China's most eutrophic semi-enclosed bays, was studied to examine the seasonal distributions of salinity, temperature, nutrients, and nitrate isotopes (δN and δO) to elucidate seasonal variations in nitrate sources and the key factors driving nitrogen level fluctuations. Based on nitrate δN (6.1-8.
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