Search research articles

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 Journal Methods Collections JoVE Encyclopedia of Experiments 存档

教育

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual 教师资源中心教师网站

使用条款与条件

相关概念视频

Neural Regulation

Neural Regulation

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.

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

Bacterial gene regulatory neural network as a biocomputing library of mathematical solvers.

NPJ systems biology and applications·2026

Same author

Magneto-Photonic Gene Circuit for Minimally Invasive Control of Gene Expression in Mammalian Cells.

ACS omega·2026

Same author

Machine learning-based development of Gadolinium binding peptides.

bioRxiv : the preprint server for biology·2025

Same author

Magneto-Photonic Gene Circuit for Minimally Invasive Control of Gene Expression in Mammalian Cells.

bioRxiv : the preprint server for biology·2025

Same author

Engineered Peptide Scrambling for Enhanced Drug Delivery to Resistant Breast Cancer Cells via Small Extracellular Vesicles.

ACS applied bio materials·2025

Same author

Non-invasive tracking of rAAV-mediated gene expression in the liver through a genetically encoded MRI reporter gene.

Molecular therapy : the journal of the American Society of Gene Therapy·2025

Search research articles

相关实验视频

Updated: Jun 24, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

使用基因调节神经网络进行湿神经形计算的非线性分类器.

Adrian Ratwatte¹, Samitha Somathilaka², Sasitharan Balasubramaniam¹

¹School of Computing, University of Nebraska-Lincoln, 104 Schorr Center, Lincoln, Nebraska, USA.

Biophysical reports

|June 7, 2024

概括

此摘要是机器生成的。

生物基因调节网络 (GRNs) 可以作为人工神经网络 (ANNs) 发挥作用,使新的湿神经形态计算成为可能. 本研究开发了使用基因调节神经网络 (GRNNs) 进行分子机器学习的三种非线性分类器.

更多相关视频

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

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

Published on: March 2, 2015

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

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

Published on: June 21, 2022

相关实验视频

Last Updated: Jun 24, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

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

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

Published on: March 2, 2015

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

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

Published on: June 21, 2022

科学领域:

计算生物学计算生物学
生物信息学是一种生物信息学.
系统生物学系统生物学

背景情况:

细胞基因调节网络 (GRNs) 与人工神经网络 (ANNs) 具有结构和操作的相似之处.
这种相似之处为开发湿神经形态计算系统提供了潜力.
基因可以被概念化为基因感知子,处理转录因子输入以产生蛋白质输出.

研究的目的:

建立分子机器学习的非线性分类器,利用基因表达的固有西格形非线性.
分析基因调节神经网络 (GRNNs) 的时间稳定性,以获得可靠的计算性能.
为各种应用开发和评估基于GRNN的分类器.

主要方法:

将基因调节网络 (GRNs) 建模为具有加权输入和非线性激活函数的基因调节神经网络 (GRNNs).
采用基于自值的稳定性分析来确定最大稳定度水平并最大限度地减少错误.
使用Lyapunov稳定定理来分析动态GRNNs的时间稳定性.
开发了三种非线性分类器,使用两种通用多层子GRNN和一种来自大肠杆菌的子GRNN.

主要成果:

成功建立基于GRNN的非线性分类器,证明了分子机器学习的可行性.
稳定性分析证实了最大度水平,这对于最小化GRNN的波动和计算错误至关重要.
开发的分类器显示出适应不同应用要求的适应性,突出了GRNN方法的多功能性.

结论:

GRN-to-GRNN映射为开发生物启发的计算系统提供了一个强大的框架.
稳定性分析对于确保基于GRNN的分类器可靠运行至关重要.
开发的GRNN分类器表明了各种分子机器学习应用的潜力.