Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Graded Potential01:19

Graded Potential

6.9K
Graded potentials are localized fluctuations in the cell membrane's electrical charge, commonly found in the dendrites of neurons. The magnitude of these potential changes depends on the strength of the initiating stimulus. In a membrane at its resting potential, a graded potential signifies a voltage shift either above -70 mV or below -70 mV.
Graded potentials fall into two categories: depolarizing and hyperpolarizing. Depolarizing graded potentials typically occur when sodium (Na+) or...
6.9K
Neuroplasticity01:01

Neuroplasticity

1.6K
Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.
1.6K
Neuronal Communication01:28

Neuronal Communication

3.1K
Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
3.1K
Propagation of Action Potentials01:23

Propagation of Action Potentials

9.0K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
9.0K
Neural Circuits01:25

Neural Circuits

2.7K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.7K
Action Potentials01:41

Action Potentials

141.4K
Overview
141.4K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Repeatability of Relative Free Energy Calculations in Solution with ANI-2x and MACE-OFF23.

Journal of chemical theory and computation·2025
Same author

Architecture-Independent Absolute Solvation Free Energy Calculations with Neural Network Potentials.

The journal of physical chemistry letters·2025
Same author

Optimizing Absolute Binding Free Energy Calculations for Production Usage.

Journal of chemical theory and computation·2025
Same author

CHARMM at 45: Enhancements in Accessibility, Functionality, and Speed.

The journal of physical chemistry. B·2024
Same author

Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.

The journal of physical chemistry. B·2024
Same author

On Analytical Corrections for Restraints in Absolute Binding Free Energy Calculations.

Journal of chemical information and modeling·2024
Same journal

Advancing Biochemical Molecule Registration, Representation and Search for New Drug Modalities.

Journal of chemical information and modeling·2026
Same journal

A Unified Molecular Graph and Protein Language Model Framework for Predicting Human Drug-Hormone Receptor Interactions with Structure-Aware Validation.

Journal of chemical information and modeling·2026
Same journal

Intricate Role of Cholesterol in Membrane Fusion.

Journal of chemical information and modeling·2026
Same journal

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal Complexes from SMILES.

Journal of chemical information and modeling·2026
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
查看所有相关文章

相关实验视频

Updated: Jan 18, 2026

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

518

可转移的神经网络潜力和凝聚阶段属性

Anna Katharina Picha1,2, Marcus Wieder3, Stefan Boresch1

  • 1Faculty of Chemistry, Institute of Computational Biological Chemistry, University of Vienna, Wien 1090, Austria.

Journal of chemical information and modeling
|September 11, 2025
PubMed
概括
此摘要是机器生成的。

可转移的神经网络潜力 (NNP) 难以准确预测凝聚相性质. 测试揭示了ANI-2x和MACE-OFF23等模型的特定弱点,突出了在模拟中需要仔细选择的需要.

更多相关视频

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.2K

相关实验视频

Last Updated: Jan 18, 2026

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
08:44

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Published on: October 17, 2025

518
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.8K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.2K

科学领域:

  • 计算化学的计算化学
  • 材料科学 材料科学 材料科学
  • 机器学习 机器学习

背景情况:

  • 可转移的神经网络潜能 (NNP) 正在迅速发展,主要是通过单分子数据进行训练.
  • 目前的应用往往侧重于分子模拟,而不是复杂的凝结相.
  • 对于其培训数据之外的系统,NNP的准确性在很大程度上是未被探索的.

研究的目的:

  • 评估可转移NNP在重现凝结相特性方面的性能.
  • 在液体模拟中应用时识别NNP的特定弱点.
  • 为选择合适的NNP进行复杂的模拟提供信息.

主要方法:

  • 根据参考数据评估了两个可转让的NNP (ANI-2x,MACE-OFF23(S/M)).
  • 纯液体的模拟性质:密度,蒸发热量,热容量,同热压缩性.
  • 分析了辐射分布函数和自我扩散常数.

主要成果:

  • 在模拟缩相时,这两个NNP都表现出了特定的弱点.
  • 即使是轻微的模型缺陷也导致了液体模拟中的显著性能下降.
  • 在测试的NNP模型之间,性能差异很大.

结论:

  • 当前可转移的NNP可能对一般的冷凝相模拟不够准确.
  • 谨慎的模型选择和严格的测试对于超出其培训领域的NNP应用至关重要.
  • 需要进一步开发,以提高复杂系统的NNP可靠性.