EFRI BRAID: Fractional-order neuronal dynamics for next generation memcapacitive computing networks

EFRI BRAID：下一代记忆电容计算网络的分数阶神经元动力学

• 批准号: 2318139
• 负责人: Fidel Santamaria
• 金额: $ 200万
• 依托单位: University of Texas at San Antonio
• 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 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318139&HistoricalAwards=false
• 关键词: EFRI; BRAID; Fractional; order; neuronal

This Emerging Frontiers in Research and Innovation (EFRI) project will explore the use of a special type of capacitor to both describe the behavior of biological neural circuits and to design brain-inspired computers. These devices, which are a type of memelement, or electrical component with memory, are called memcapacitors. Classical models of neural dynamics can be formulated using resistors with memory – called memristors – to capture the effects of ion channels in the axon membrane. This project builds on work showing that memcapacitors may be used for neural models instead. These models may be used to design hardware circuits that emulate the learning and computing capability of biological neurons. In this context, called neuromorphic computing, memcapacitors promise significant energy savings over memristors. The research team will implement a plan to maximize the opportunity to recruit Under Represented Minorities to participate in the activities of the project. This project will stablish a collaboration with a Biomedical Ethicist to promote the study of Ethical and Social Implications of adoption of new technologies through a series of workshops and project based activities.Increasing evidence suggests that nervous and artificial intelligence systems benefit from having elements that are history-dependent, also referred to as intrinsic memory. In single neurons, history-dependence in the timing or firing rate of action potentials (spikes) is the result of the continuous interactions of different membrane conductances distributed over a complex morphology. Spiking history-dependence underlies important computational functions such as efficient adaptive coding of both infrequent and persistent natural stimuli and contrast adaptation over multiple scales of input levels. Theoretically, fractional-order dynamics captures the complexity of the intrinsic neuron excitability. A fractional order leaky integrate-and-fire model reproduces a wide range of non-linear spiking behaviors by assuming that the capacitance of the membrane is itself history-dependent. Electrical elements with memory, or memelements, are physical components whose intrinsic characteristics change with previous activity. The most studied memelement is the memristor, a passive component that consumes static energy. In contrast, memcapacitors consume far less static energy, and thus have the potential for building orders of magnitude more efficient neuromorphic systems. The main objective of this project is to use a fractional order differential formalism to model history-dependence in neurons and apply it to model, design, and fabricate memelements, particularly memcapacitors. Using a highly interdisciplinary approach, the team will apply this theory to characterize the computational and physical properties of realized memcapacitors. The project will then evaluate the computational properties of memcapacitive and fractional order spiking neurons and their networks. The team will use energy and performance metrics across the different devices to compare with related work. This project brings together a team of researchers at the intersections of computer science, neuroscience, engineering, and physics to address these questions and challenges. Our following tasks integrate theory, modeling, and physical implementations of neuronal fractional order and memcapacitive systems.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.

这个新兴前沿研究和创新（EFRI）项目将探索使用一种特殊类型的电容器来描述生物神经电路的行为和设计大脑启发的计算机。这些设备是一种记忆元件或具有记忆的电气元件，称为记忆电容器。神经动力学的经典模型可以使用具有记忆的电阻器（称为忆阻器）来描述，以捕获轴突膜中离子通道的影响。该项目建立在工作的基础上，表明memcapacitors可以用于神经模型。这些模型可用于设计模拟生物神经元的学习和计算能力的硬件电路。在这种情况下，称为神经形态计算，记忆电容器承诺显着节能忆阻器。研究小组将执行一项计划，最大限度地增加招募代表不足的少数群体参加项目活动的机会。该项目将与生物医学伦理学家建立合作关系，通过一系列研讨会和项目活动，促进对采用新技术的伦理和社会影响的研究。越来越多的证据表明，神经和人工智能系统受益于具有历史依赖性的元素，也称为内在记忆。在单个神经元中，动作电位（尖峰）的时间或放电速率的历史依赖性是分布在复杂形态上的不同膜电导的连续相互作用的结果。尖峰历史依赖性的基础重要的计算功能，如有效的自适应编码的罕见和持久的自然刺激和对比度适应多尺度的输入水平。从理论上讲，分数阶动力学捕捉的内在神经元兴奋性的复杂性。一个分数阶泄漏积分和消防模型再现了广泛的非线性尖峰行为，假设膜的电容本身是历史依赖的。具有记忆的电子元件或memelements是其固有特性随先前活动而变化的物理组件。研究最多的记忆元件是忆阻器，它是一种消耗静态能量的无源元件。相比之下，记忆电容器消耗的静态能量要少得多，因此有可能构建数量级更有效的神经形态系统。该项目的主要目标是使用分数阶微分形式主义来模拟神经元中的历史依赖性，并将其应用于建模，设计和制造记忆元件，特别是记忆电容器。使用高度跨学科的方法，该团队将应用该理论来表征实现的记忆电容器的计算和物理特性。然后，该项目将评估电容和分数阶尖峰神经元及其网络的计算特性。该团队将使用不同设备的能量和性能指标来与相关工作进行比较。该项目汇集了计算机科学，神经科学，工程和物理学交叉点的研究人员团队，以解决这些问题和挑战。我们的以下任务集成了神经元分数阶和忆电容系统的理论、建模和物理实现。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。