Bioengineering, California Institute of Technology, Pasadena, CA, USA.
Computer Science, California Institute of Technology, Pasadena, CA, USA.
Nature. 2018 Jul;559(7714):370-376. doi: 10.1038/s41586-018-0289-6. Epub 2018 Jul 4.
From bacteria following simple chemical gradients to the brain distinguishing complex odour information, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemented using DNA-based neural networks, but has been limited to the recognition of a set of no more than four patterns, each composed of four distinct DNA molecules. Winner-take-all computation has been suggested as a potential strategy for enhancing the capability of DNA-based neural networks. Compared to the linear-threshold circuits and Hopfield networks used previously, winner-take-all circuits are computationally more powerful, allow simpler molecular implementation and are not constrained by the number of patterns and their complexity, so both a large number of simple patterns and a small number of complex patterns can be recognized. Here we report a systematic implementation of winner-take-all neural networks based on DNA-strand-displacement reactions. We use a previously developed seesaw DNA gate motif, extended to include a simple and robust component that facilitates the cooperative hybridization that is involved in the process of selecting a 'winner'. We show that with this extended seesaw motif DNA-based neural networks can classify patterns into up to nine categories. Each of these patterns consists of 20 distinct DNA molecules chosen from the set of 100 that represents the 100 bits in 10 × 10 patterns, with the 20 DNA molecules selected tracing one of the handwritten digits '1' to '9'. The network successfully classified test patterns with up to 30 of the 100 bits flipped relative to the digit patterns 'remembered' during training, suggesting that molecular circuits can robustly accomplish the sophisticated task of classifying highly complex and noisy information on the basis of similarity to a memory.
从细菌跟随简单的化学梯度到大脑区分复杂的气味信息，识别分子模式的能力对于生物有机体至关重要。这种信息处理功能已在基于 DNA 的神经网络中实现，但仅限于识别不超过四个模式的一组，每个模式由四个不同的 DNA 分子组成。胜者全取计算已被提议作为增强基于 DNA 的神经网络能力的潜在策略。与之前使用的线性阈值电路和 Hopfield 网络相比，胜者全取电路在计算上更强大，允许更简单的分子实现，并且不受模式数量及其复杂性的限制，因此可以识别大量简单模式和少量复杂模式。在这里，我们报告了一种基于 DNA 链置换反应的胜者全取神经网络的系统实现。我们使用了以前开发的跷跷板 DNA 门电路模块，将其扩展到包括一个简单而强大的组件，该组件简化了参与选择“胜者”的协同杂交过程。我们表明，使用这种扩展的跷跷板模块，基于 DNA 的神经网络可以将模式分类为多达九个类别。这些模式中的每一个都由从代表 100 位的 100 个 DNA 分子中选择的 20 个独特的 DNA 分子组成，这 20 个 DNA 分子选择的轨迹是手写数字“1”到“9”之一。该网络成功地对与训练期间“记住”的数字模式相比翻转了多达 100 位中的 30 位的测试模式进行了分类，这表明分子电路可以稳健地完成根据相似性对高度复杂和嘈杂信息进行分类的复杂任务。
