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扩大基于DNA的获胜者获取全部神经网络的分子模式识别
Kevin M Cherry1, Lulu Qian2,3
1Bioengineering, California Institute of Technology, Pasadena, CA, USA.
Nature
|July 6, 2018
概括
这项研究引入了一种基于DNA的新型神经网络, 新的分子电路可以分类复杂,杂的模式, 显著提升DNA计算能力.
科学领域:
- 生物物理
- 分子计算
- 合成生物学
背景情况:
- 分子模式识别对于生物来说至关重要,从简单的化学反应到复杂的嗅觉处理.
- 之前基于DNA的神经网络的模式识别能力有限 (例如,四个DNA分子的四个模式).
- 与之前的模型相比,获胜者获取所有 (WTA) 计算提供了增强的功能,更简单的实现,并且对DNA神经网络的限制更少.
研究的目的:
- 使用DNA链移位反应系统地实施WTA神经网络.
- 增强合作性杂交的现有DNA门模式,使得"获胜者"能够得到强大的选择.
- 展示这些增强网络的复杂模式分类能力.
主要方法:
- 使用一个摇摆DNA门图案, 扩展了合作杂交组件.
- 在网络操作中使用DNA链移位反应.
- 训练网络识别代表手写数字"1"到"9"的模式 (100位,每个模式有20个不同的DNA分子).
主要成果:
- 成功实施基于DNA的WTA神经网络,能够将模式分为多达9个类别.
- 由一组100个不同的DNA分子组成的模式的证明分类.
- 测试模式的稳固分类达到30%的位翻转,表明对噪声的耐受性.
结论:
- 通过分子电路进行复杂的模式分类.
- 基于DNA的WTA神经网络可以根据存储模式的相似性强有力的分类高度复杂和杂的信息.
- 这项工作提升了分子计算在复杂信息处理任务中的潜力.
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