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

这个研究与创新前沿（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回路的行为和设计受大脑启发的计算机。这些器件是记忆元件的一种，或具有记忆的电子元件，称为记忆电容器。经典的神经动力学模型可以使用具有记忆的电阻器（称为忆阻器）来描述轴突膜中离子通道的影响。这个项目建立在表明记忆电容器可以用于神经模型的工作基础上。这些模型可用于设计硬件电路，以模拟生物神经元的学习和计算能力。在这种被称为神经形态计算的情况下，记忆电容有望比记忆电阻器显著节省能量。研究小组将执行一项计划，最大限度地利用机会招募代表性不足的少数民族参与该项目的活动。该项目将与一名生物医学伦理学家建立合作关系，通过一系列讲习班和基于项目的活动，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，神经系统和人工智能系统受益于具有依赖历史的元素，也被称为内在记忆。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连续相互作用的结果。峰值历史依赖性是重要的计算功能的基础，例如对不频繁和持续的自然刺激进行有效的自适应编码，以及在多个输入水平尺度上的对比适应。从理论上讲，分数阶动力学捕获了神经元固有兴奋性的复杂性。分数阶泄漏积分点火模型通过假设膜的电容本身与历史相关，再现了大范围的非线性尖峰行为。具有记忆的电元件或记忆元件是其内在特征随先前活动而变化的物理元件。研究最多的元件是忆阻器，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，因此有可能构建效率提高几个数量级的神经形态系统。该项目的主要目标是使用分数阶微分形式来模拟神经元的历史依赖性，并将其应用于模型、设计和制造memelement，特别是memcapacitors。采用高度跨学科的方法，该团队将应用这一理论来表征已实现的记忆电容器的计算和物理特性。然后，该项目将评估记忆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、工程学和物理学交叉领域的一组研究人员，以解决这些问题和挑战。我们接下来的任务整合了神经元分数阶和记忆电容系统的理论、建模和物理实现。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.