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

这个新兴的研究和创新前沿(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电路的行为，并设计受大脑启发的计算机。这些器件是记忆元件的一种，或具有记忆功能的电子元件，称为记忆电容器。神经动力学的经典模型可以用带有记忆的电阻--称为忆阻器--来捕捉轴突细胞膜中离子通道的影响。这个项目建立在表明记忆电容可以用于神经模型的工作的基础上。这些模型可以用来设计模拟生物神经元学习和计算能力的硬件电路。在这种被称为神经形态计算的背景下，记忆电容器承诺比记忆阻器节省大量能源。研究小组将执行一项计划，以最大限度地扩大招募有代表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，神经和人工智能系统受益于具有依赖历史的元素，也称为内在记忆。在单个神经元中，动作电位(棘波)的时序或放电频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。脉冲历史依赖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度适应。从理论上讲，分数阶动力学抓住了神经元内在兴奋性的复杂性。分数阶泄漏积分-点火模型通过假设膜的电容本身是历史相关的，再现了广泛的非线性尖峰行为。有记忆的电子元件，或记忆元件，是其固有特性随先前活动而改变的物理元件。研究最多的记忆元件是忆阻器，这是一种消耗静态能量的无源元件。相比之下，记忆电容消耗的静态能量要少得多，因此有可能构建出更高效的神经形态系统。这个项目的主要目标是使用分数阶微分形式来模拟神经元中的历史依赖性，并将其应用于模型化、设计和制造记忆元件，特别是记忆电容。使用一种高度跨学科的方法，该团队将应用这一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上的能源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。我们的以下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NSF的法定使命，并通过使用基金会的智力优势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.