Memory in a Droplet: Collections of Brain-Inspired Biomolecular Elements

液滴中的记忆：受大脑启发的生物分子元素的集合

• 批准号: 2310360
• 负责人: Eric Freeman
• 金额: $ 37.27万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310360&HistoricalAwards=false
• 关键词: Memory; Droplet; Collections; Brain; Inspired

The development of alternative computing approaches is urgently needed as artificial intelligence methods become prevalent, requiring greater resources for their execution. One approach to alternative computing draws inspiration directly from the brain itself, reconstructing aspects of conventional circuitry through combinations of the ionic transport and selective membranous barriers present in living tissues. These approaches require low power consumption through collocating memory and processing units, building memory into the structure of the material itself. This project builds networks of lipid-coated droplets as model neurons capable of adapting their properties in response to the signals they transmit. This ability to tune the exchange across the model synapses is a crucial component of neuromorphic architectures, and this project explores controlling the exchanges using emergent mechanics in networks of interconnected synthetic cells through a combination of mathematical predictions and experimental observations. Completion of the project will provide a foundation for dynamic synthetic tissues capable of chemical computation and able to interface both with conventional electronics and biochemical signals. In addition, the project provides interdisciplinary training for future scientists and enhances the development of the STEM workforce by recruiting and training undergraduate and high school students. This research provides a new approach to droplet-based neuromorphic materials, expanding them to larger multidimensional networks of reconfigurable elements. Investigating these networks of neuromorphic compartments has demonstrated how the internal droplet states (such as accumulation of charge) may be exploited as a form of memory. The selected approach recognizes that droplet interface bilayers may be viewed through the lens of viscoelasticity/viscoplasticity in soft materials, enabling metastable behaviors. A novel multiphysics model will be created for simulating adhesive droplet mechanics by combining the electrical and chemical activities. This model will then be used to explore complex electrowetting events in collections of biological membranes, guiding the design of biomolecular neuromorphic materials. Model predictions will be validated experimentally, and the model will be gradually expanded to study emergent mechanics present in larger collections of biomolecular elements operating in unison. This new approach to the brain-inspired material provides long term synaptic plasticity and the ability to permanently retain changes in the membranous structure through voltage signals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着人工智能方法的普及，迫切需要开发替代计算方法，这需要更多的资源来执行。 替代计算的一种方法直接从大脑本身汲取灵感，通过结合离子传输和活组织中存在的选择性膜屏障来重建传统电路的各个方面。这些方法通过配置存储器和处理单元，将存储器构建到材料本身的结构中，从而需要低功耗。该项目建立了脂质包被液滴网络，作为模型神经元，能够根据它们传输的信号调整它们的特性。 这种调整模型突触之间交换的能力是神经形态架构的重要组成部分，该项目通过数学预测和实验观察的结合，探索在相互连接的合成细胞网络中使用紧急机制来控制交换。 该项目的完成将为能够进行化学计算的动态合成组织提供基础，并能够与传统的电子和生物化学信号进行接口。 此外，该项目还为未来的科学家提供跨学科培训，并通过招募和培训本科生和高中生来加强STEM劳动力的发展。 这项研究为基于液滴的神经形态材料提供了一种新的方法，将其扩展到更大的多维可重构元件网络。 研究这些神经形态区室的网络已经证明了内部液滴状态（例如电荷的积累）如何被利用作为一种形式的记忆。所选择的方法认识到，液滴界面双层可以通过软材料中的粘弹性/粘塑性的透镜来观察，从而实现亚稳态行为。 将电化学活动结合起来，建立了一个新的多物理场模型来模拟粘附液滴的力学行为。 然后，该模型将用于探索生物膜集合中的复杂电润湿事件，指导生物分子神经形态材料的设计。模型预测将通过实验验证，模型将逐渐扩展到研究在更大的生物分子元素集合中一致操作的涌现机制。这一新的大脑启发材料的方法提供了长期的突触可塑性和通过电压信号永久保留膜结构变化的能力。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。