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関連する概念動画

Action Potential01:14

Action Potential

10.2K
Neurons communicate by firing action potentials—the electrochemical signal that is propagated along the axon. The signal results in the release of neurotransmitters at axon terminals, thereby transmitting information to the nervous system. An action potential is a specific "all-or-none" change in membrane potential that results in a rapid spike in voltage.
Membrane potential in neurons
Neurons typically have a resting membrane potential of about -70 millivolts (mV). When they receive...
10.2K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.3K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.3K
Propagation of Action Potentials01:23

Propagation of Action Potentials

15.4K
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...
15.4K
Neural Circuits01:25

Neural Circuits

3.0K
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...
3.0K
Neuronal Communication01:28

Neuronal Communication

5.5K
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...
5.5K
Neuroplasticity01:01

Neuroplasticity

2.6K
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.
2.6K

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関連する実験動画

Updated: May 3, 2026

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

Published on: March 2, 2015

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ニューラルネットワークの先行者との拡散プロセスにおける推論.

Davide Ghio1, Fabrizio Boncoraglio2, Lenka Zdeborová2

  • 1École Polytechnique Fédérale de Lausanne, Information, Learning and Physics Laboratory, (EPFL), Lausanne, Switzerland.

Physical review. E
|February 20, 2026
PubMed
まとめ

流行状態を推論するためのニューラルネットワークモデルを導入し,より現実的な初期条件のためにノード共変数を組み込みます. このアプローチは,状態の回復を高めるが,段階的移行は,統計から計算のギャップを生成する可能性がある.

科学分野:

  • 複雑なシステム 複雑なシステム
  • 統計的推論 統計的な推論
  • 機械学習 (Machine Learning) とは,機械学習 (Machine Learning) について学ぶことです.

背景:

  • グラフ上のストキャスティックプロセスは,流行をモデル化しますが,しばしばランダムな初期状態を想定します.
  • 現実世界のシステムには,初期状態に影響を与えるノード共変数があり,これは推論でしばしば無視される要因です.

研究 の 目的:

  • ノード共変数のニューラルネットワーク関数としてグラフ上のストカスティックプロセスの初期状態をモデル化します.
  • プロセスダイナミクスと共変量情報の両方を活用するベイジアン推論の枠組みを開発する.
  • 状態と軌道の回復に対するニューラルネットワークのプリオアの影響を分析する.

主な方法:

  • ハイブリッドの信念伝播と近似メッセージパス (BP-AMP) アルゴリズムが派生しました.
  • アルゴリズムは,ノード共変数からの情報と拡散ダイナミクスを統合します.
  • 性能は,拡散情報のみまたは共変量情報のみを使用する方法と比較した.

主要な成果:

  • 提案されたモデルは,共変量情報を組み込むことにより,初期状態の回復と伝播軌道を強化します.
  • ファースト・オーダー・フェーズ・トランジションは,特にラデマッハーの分布したニューラル・ネットワークの重みで,いくつかのシステムで観察されました.

さらに関連する動画

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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関連する実験動画

Last Updated: May 3, 2026

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

11.5K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

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  • 完全な回復は理論的には可能ですが,計算的には達成不可能である統計から計算のギャップが生まれました.

    • 結論:

    • ノード共変数に基づいたニューラルネットワークの priors を統合することで,グラフ上のストキャスティックプロセスの推論が改善されます.
    • 段階移行と,その結果生じる統計から計算のギャップは,正確な状態推定に課題をもたらす.
    • BP-AMPアルゴリズムは,統合された共変量情報で複雑な推論問題を扱うための堅牢なアプローチを提供します.