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関連する概念動画

Contact-dependent Signaling01:19

Contact-dependent Signaling

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Contact-dependent signaling, as the name suggests, requires that communicating cells be in direct contact with each other. This is achieved either through receptor-ligand interactions or by specialized cytoplasmic channels that allow the flow of small molecules between cells. In animal cells, channels called gap junctions facilitate contact-dependent signaling in certain tissues, whereas, plasmodesmata perform a similar function in plants.
Gap Junctions
In animal cells, gap junctions are formed...
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Gap Junctions01:37

Gap Junctions

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Multicellular organisms employ a variety of ways for cells to communicate with each other. Gap junctions are specialized proteins that form pores between neighboring cells in animals, connecting the cytoplasm between the two, and allowing for the exchange of molecules and ions. They are found in a wide range of invertebrate and vertebrate species, mediate numerous functions including cell differentiation and development, and are associated with numerous human diseases, including cardiac and...
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Electrical Synapses01:28

Electrical Synapses

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Electrical synapses found in all nervous systems play important and unique roles. In these synapses, the presynaptic and postsynaptic membranes are very close together (3.5 nm) and are actually physically connected by channel proteins forming gap junctions.
Gap junctions allow the current to pass directly from one cell to the next. In contrast, in the chemical synapse, the neurotransmitters carry the information through the synaptic cleft from one neuron to the next. They consist of two...
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Control Systems: Applications01:25

Control Systems: Applications

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Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
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State Space Representation01:27

State Space Representation

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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
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Neuronal Communication01:28

Neuronal Communication

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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...
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関連する実験動画

Updated: May 1, 2026

In Vivo Intracerebral Stereotaxic Injections for Optogenetic Stimulation of Long-Range Inputs in Mouse Brain Slices
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空間的に拡散した入力信号によるヒトコネクトームの制御

Richard Betzel1,2,3,4,5, Maria Grazia Puxeddu6, Caio Seguin6

  • 1Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA. rbetzel@umn.edu.

Communications biology
|March 1, 2026
PubMed
まとめ
この要約は機械生成です。

研究者らは、空間的に拡張された入力を利用する新しい脳制御モデルを開発し、脳の状態遷移に必要なエネルギーを大幅に削減し、必要な入力を少なくした。

キーワード:
脳制御コネクトームネットワーク制御理論状態遷移エネルギー効率

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科学分野:

  • 神経科学
  • ネットワーク科学
  • 計算生物学

背景:

  • ヒトの脳は、さまざまな脳の状態に移行する連続的な動的活動を示します。
  • ネットワーク制御理論は、これらの状態遷移のエネルギーコストを分析するためのフレームワークを提供します。
  • 従来のモデルは、脳の空間的連続性と限られた刺激特異性を無視して、独立したノード入力を仮定しています。

研究 の 目的:

  • ネットワーク制御モデルを空間的に拡張された入力を組み込むように適合させること。
  • 現実的な入力戦略が脳の状態遷移に必要なエネルギーにどのように影響するかを調査すること。
  • 効率的な制御戦略とその神経生物学的な相関関係を特定すること。

主な方法:

  • 影響が距離とともに指数関数的に減衰する入力を包含するようにネットワーク制御モデルを適合させました。
  • 状態遷移に必要なエネルギーに対する空間的に拡張された入力の影響を分析しました。
  • ほぼ最適な制御戦略を特定し、入力部位密度をマッピングしました。

主要な成果:

  • 空間的に拡張された入力は、脳の状態遷移に必要なエネルギーを大幅に削減します。
  • ほぼ最適な制御戦略は、必要な入力を（2桁まで）大幅に減少させます。
  • 最適な入力部位密度の地図は、独立した機能的、代謝的、遺伝的、神経化学的地図と一致します。

結論:

  • 空間的に拡張された入力を組み込むことは、脳制御のためのより現実的でエネルギー効率の高いフレームワークを提供します。
  • このアプローチは、脳の接続性と活動における空間的依存性を活用します。
  • この発見は、脳のダイナミクスを理解し制御するための神経生物学的に根拠のある方法を提供します。