RUI: Nonlinear and Neural Dynamics in Josephson Networks

RUI：约瑟夫森网络中的非线性和神经动力学

• 批准号: 1105444
• 负责人: Kenneth Segall
• 金额: $ 25.5万
• 依托单位: Colgate University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-15 至 2014-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1105444&HistoricalAwards=false
• 关键词: RUI; Nonlinear; Neural; Dynamics; Josephson

****Technical abstract**** This individual investigator award supports an experimental and computational study of nonlinear dynamics in networks of superconducting Josephson junctions. Josephson junctions are examples of nonlinear systems which can be fabricated with adjustable parameters, measured in a straightforward fashion, and easily scaled to large network sizes. In addition, a large Josephson junction circuit measured over a long time contains dynamics which would essentially be impossible to calculate on a computer, but which can be observed with electrical measurements. This project will take a multi-faceted approach to studying the collective, emergent behavior of Josephson junction networks. First, it will follow previous work in the field on soliton-like modes called fluxons and localized modes called discrete breathers. Next, studies will be performed on the synchronization of a system of disordered oscillators. Finally, a circuit of Josephson junctions designed to accurately model the time-dependent voltage of a biological neuron will be fabricated and tested. This has a longer-term goal of studying the emergent behavior of a large, coupled neural network. ****Non-technical abstract**** An important aspect of physics today is the effort to understand how the fundamental laws of nature result in complex behavior. For example, consider a system of gas molecules. Simple laws of force and momentum govern their collisions. With only a few molecules, the system is simple and uninteresting. With a large number of molecules, however, the system can organize itself into something complex, like a tornado. This new behavior comes about not because of a change in the fundamental laws, but rather a change in the number of constituents, in this case gas molecules, of the system. In this research, a simple electrical circuit element known as a Josephson junction is studied. Josephson junctions are made from superconducting metals and work at very low temperatures. Past experiments have looked at the behavior of a single Josephson junction and found it capable of interesting electrical behavior. However, circuits composed of large numbers of Josephson junctions have yet to be fully studied. Just like the case of gas molecules, new collective behaviors result when the number of constituents is increased. This project will look at several of these new behaviors. One of these, like a tornado, is a swirl of electrical current. Another is a collective voltage oscillation, a back and forth motion like pendulums swinging together. A final behavior is voltage spiking, similar to the on-off firing of a biological neuron. With this last behavior, a longer term goal is to build circuits which would emulate collective behaviors in the human brain, where large numbers of neurons are connected together. This project incorporates undergraduate students as the primary researchers, preparing them for technical careers in the sciences.

****技术摘要****该个人研究者奖支持超导约瑟夫森结网络非线性动力学的实验和计算研究。约瑟夫森结是非线性系统的例子，它可以用可调参数制造，以直接的方式测量，并且很容易扩展到大的网络尺寸。此外，长时间测量的大型约瑟夫森结电路包含的动力学本质上是不可能在计算机上计算的，但可以用电气测量来观察。该项目将采用多方面的方法来研究约瑟夫森结网络的集体涌现行为。首先，它将遵循先前在类孤子模式（称为通量子）和局部模式（称为离散呼吸子）领域的工作。接下来，我们将研究一个无序振子系统的同步。最后，设计一个约瑟夫森结电路来精确模拟生物神经元的时变电压，并进行测试。它的长期目标是研究大型耦合神经网络的突发行为。****非技术摘要****当今物理学的一个重要方面是努力理解自然的基本定律是如何导致复杂行为的。例如，考虑一个气体分子系统。它们的碰撞受简单的力和动量定律的支配。由于只有几个分子，这个系统既简单又无趣。然而，有了大量的分子，这个系统可以自己组织成一些复杂的东西，比如龙卷风。这种新的行为不是由于基本定律的改变，而是由于系统中组分的数量的改变，在这个例子中是气体分子。在这项研究中，一个简单的电路元件被称为约瑟夫森结进行了研究。约瑟夫森结由超导金属制成，工作温度非常低。过去的实验研究了单个约瑟夫森结的行为，发现它具有有趣的电行为。然而，由大量约瑟夫森结组成的电路尚未得到充分的研究。就像气体分子的情况一样，当成分数量增加时，新的集体行为就会产生。本项目将研究这些新行为中的几个。其中之一，就像龙卷风一样，是电流的漩涡。另一种是集体电压振荡，像钟摆一样来回摆动。最后一种行为是电压尖峰，类似于生物神经元的开关放电。对于最后一种行为，一个更长期的目标是建立电路，模仿人类大脑中的集体行为，在那里大量的神经元连接在一起。该项目将本科生作为主要研究人员，为他们在科学领域的技术职业做好准备。