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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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The Role of Ion Channels in Neuronal Computation01:19

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A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential....
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Amplifying Signals via Enzymatic Cascade01:22

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When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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酵素ニューラルネットワークによる非線形意思決定

S Okumura1, G Gines2, N Lobato-Dauzier1

  • 1LIMMS, CNRS-Institute of Industrial Science, University of Tokyo, Tokyo, Japan.

Nature
|October 19, 2022
PubMed
まとめ

研究者は 分子的な意思決定のために DNAでコードされた 酵素性ニューロンを開発しました これらの人工ニューロンは複雑な分子データを分類できる多層ネットワークを形成し,高度なアプリケーションのために生物学的ニューラルネットワークを模倣します.

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科学分野:

  • バイオテクノロジー
  • 分子コンピューティング
  • 合成生物学

背景:

  • 人工ニューラルネットワークは 電子コンピューティングに変革をもたらしました
  • 分子ネットワークは遺伝子規制ネットワークに匹敵する 生物学的意思決定の可能性を秘めています
  • 以前の非酵素神経形構造は,感度,速度,非線形応答の制限に直面した.

研究 の 目的:

  • 調節可能な特性を備えた 酵素性ニューロンを導入する
  • 分子分類のための多層神経形構造を構築する.
  • 分子データにおける非線形分離領域の分類を達成する.

主な方法:

  • 調整可能な重量とバイアスを有する 酵素ニューロンを利用する
  • 複雑な計算をするために ニューロンを多層ネットワークに組み立てます
  • ニューラルと論理操作を組み合わせた ハイブリッド回路の開発

主要な成果:

  • 個々のニューロンを用いて10ビットの入力で多数関数の計算を証明した.
  • マイクロRNAの入力に基づいた長方形の機能を合成するために2層のネットワークを構築しました.
  • ハイブリッド回路を作成し 意思決定ツリーを使って 集中平面を回帰的に分割します

結論:

  • DNAでコードされた酵素ニューロンは,非線形分離可能な分子データを分類するための多層構造を可能にします.
  • このアプローチは,複雑な分子システムを分析するための計算能力と小型化を提供します.
  • 潜在的な応用には,液体生検とDNAデータベースのクエリが含まれます.