Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

149
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
149
Parallel Processing01:20

Parallel Processing

224
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
224
Inertial Frames of Reference01:03

Inertial Frames of Reference

7.4K
Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
7.4K
Non-inertial Frames of Reference01:27

Non-inertial Frames of Reference

6.1K
A reference frame accelerating or decelerating relative to an inertial frame is a non-inertial frame. To help understand this, consider what taking off in an airplane, turning a corner in a car, riding a merry-go-round, and the circular motion of a tropical cyclone all have in common. All these systems are accelerating, decelerating, or rotating relative to the Earth; hence, they all are non-inertial frames. All these systems exhibit inertial forces, which merely seem to arise from motion,...
6.1K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

897
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
897

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Choice anticipation as gated accumulation of sensory predictions.

Journal of neurophysiology·2025
Same author

Main Sequence of Human Luminance-evoked Pupil Dynamics.

Journal of cognitive neuroscience·2025
Same author

Generative adversarial collaborations: a new model of scientific discourse.

Trends in cognitive sciences·2024
Same author

Latency and amplitude of catch-up saccades to accelerating targets.

Journal of neurophysiology·2024
Same author

Changes in social environment impact primate gut microbiota composition.

Animal microbiome·2024
Same author

Visual working memory models of delayed estimation do not generalize to whole-report tasks.

Journal of vision·2024
Same journal

Erratum: Yao et al., "Estrogen Regulates Bcl-w and Bim Expression: Role in Protection against β-Amyloid Peptide-Induced Neuronal Death".

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same journal

Erratum: L'Episcopo et al., "Plasticity of Subventricular Zone Neuroprogenitors in MPTP (1-Methyl-4-Phenyl-1,2,3,6-Tetrahydropyridine) Mouse Model of Parkinson's Disease Involves Cross Talk between Inflammatory and Wnt/β-Catenin Signaling Pathways: Functional Consequences for Neuroprotection and Repair".

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same journal

Representations of subsecond duration-based timing by complex spike synchrony in cerebellar Purkinje neurons.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same journal

The extended language network: Language-responsive brain areas whose contributions to language remain to be discovered.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same journal

Cortical and thalamic afferent connectomes distinguish ACC subregions of the macaque brain.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same journal

The synaptic vesicle priming protein Munc13 mediates evoked somatodendritic dopamine release.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
関連記事をすべて見る

関連する実験動画

Updated: Sep 9, 2025

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

12.7K

分割的な標準化を超えて: 参照フレーム全体に複数のセンサを統合するためのスケーラブルなフィードフォワードネットワーク

Arefeh Farahmandi1, Parisa Abedi Khoozani1, Gunnar Blohm1

  • 1Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada, K7L 3N6.

The Journal of neuroscience : the official journal of the Society for Neuroscience
|August 29, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,マルチセンサリー統合 (MSI) の新しいフィードフォワードニューラルネットワークモデルを導入します. このモデルは,既存の理論に挑戦する分割的正規化なしで,参照フレーム全体にわたってベイズ推論を近似しています.

さらに関連する動画

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K
Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
09:13

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

Published on: April 22, 2015

16.6K

関連する実験動画

Last Updated: Sep 9, 2025

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
09:46

MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

Published on: May 10, 2012

12.7K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K
Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder
09:13

Testing Sensory and Multisensory Function in Children with Autism Spectrum Disorder

Published on: April 22, 2015

16.6K

科学分野:

  • 神経科学
  • 計算神経科学
  • 認知科学

背景:

  • マルチセンサリー統合は 認識と行動に不可欠であり 参照枠の変換も含みます
  • ベイジアン推論モデルは多感覚統合ですが,神経メカニズムについては依然として議論されています.
  • 分割的正規化は提案されたメカニズムですが,その脳の実装は不明で,モデルはスケーラビリティに苦労しています.

研究 の 目的:

  • ベイジアン推論に近付くマルチセンサ統合の代替モデルを提案する.
  • フィード・フォワード・ニューラル・ネットワークが,明示的な分断操作なしに多感覚統合を達成できるかどうかを調査する.
  • マルチセンサリー統合のためのニューラルコンピューティングにおける 分割的正規化の必要性を挑戦する.

主な方法:

  • マルチセンサリー統合 (MSI) のための多層フィードフォワードニューラルネットワークモデルを開発した.
  • マルチセンサリー統合のための 分析的なベイジアンソリューションで ネットワークを訓練した.
  • マルチセンサリー統合と神経活動における経験的原理を複製するモデルの能力を評価した.

主要な成果:

  • 提案されたフィード・フォワード・ネットワークは,マルチセンサリー統合のためのベイジアン推論に成功しています.
  • このモデルは,多感覚統合の経験的原理を示し,VIPニューロンで観察された行動を真似しています.
  • 明確な分割型標準化や特定の接続性構造を必要とせずに,参照フレーム全体で多感知統合を達成した.

結論:

  • 添加的な単位を持つ単純なフィードフォワードネットワークは,マルチセンサリー統合のための最適なベイジアン推論を近似することができます.
  • 脳が多感覚統合を行うには明示的な分割型正常化は必要ないかもしれません
  • マルチセンサリー処理の基礎にある 神経コンピューティングの洞察を提供し,既存のモデルに挑戦しています.