NetClamp: A new experimental tool to manipulate neural networks (ref: 4273)

NetClamp：一种操纵神经网络的新实验工具（参考：4273）

• 批准号: 2705892
• 金额: --
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2705892
• 关键词: NetClamp; new; experimental; tool; manipulate

All of our behaviours, such as recognising friends or making a cup a tea, result from the coordinated behaviour of networks of neurons in our brain. Each behaviour is defined by a specific pattern of electrical activity in these networks. Understanding how these patterns are generated is one of the key problems in neuroscience. In recent years, neuroscientists have made tremendous progress in determining how neurons communicate with each other. Theoreticians have used this information to construct mathematical descriptions of neural network activity, called 'models'. Models predict how the connections in a network determine the patterns of electrical activity. In particular, they suggest that subtle changes in connection properties can have large effects on network activity. For example, activity can switch from appearing seemingly random to being highly coordinated, with changes resembling a Mexican wave in football stadiums. In some brain regions, such as those regulating breathing, coordinated rhythms are healthy. In other contexts, excessive synchrony is associated with diseases such as Parkinson's or epilepsy. By uncovering how neural connections shape network rhythms and synchrony, mathematical models are an essential part of the neuroscientist toolkit. However, there is currently no way to experimentally manipulate connection maps in networks of neurons to verify model predictions. This project will use a new technology to alter connection maps in real neural networks and test model predictions. This new system combines technologies that enable us to measure electrical activity in neurons using digital cameras and modulate this activity by shining light of specific colour and intensity on them. The system combines measurements and light stimulation directly with a sophisticated mathematical model of the connections between neurons, enabling full control of the biological network. The successful candidate will use the first prototype of this system to demonstrate that it can manipulate the connection maps of small networks in culture, and so doing realise in the real biological network the activity patterns predicted by mathematical models. The use of light to control neural behaviour will also allow for functional networks to be studied in vivo once proof-of-concept has been established in vitro. In the long term, this system will enable the development of smart implants to treat brain diseases characterised by abnormal network rhythms, building on Prof Nogaret's expertise in developing hardware neural pacemakers.These implants will detect when abnormal brain activity starts, then illuminate specific neurons to modify their connections and restore normal, healthy activity.

我们所有的行为，比如认出朋友或泡茶，都是大脑神经元网络协调行为的结果。每种行为都是由这些网络中特定的电活动模式定义的。了解这些模式是如何产生的是神经科学的关键问题之一。近年来，神经科学家在确定神经元如何相互通信方面取得了巨大进展。理论家们已经使用这些信息来构建神经网络活动的数学描述，称为“模型”。模型预测网络中的连接如何决定电活动的模式。特别是，他们认为连接属性的细微变化可能对网络活动产生巨大影响。例如，活动可以从看似随机的转变为高度协调的，其变化类似于足球场上的墨西哥浪潮。在某些大脑区域，比如那些调节呼吸的区域，协调的节奏是健康的。在其他情况下，过度的同步与帕金森氏症或癫痫等疾病有关。通过揭示神经连接如何塑造网络节奏和同步，数学模型是神经科学家工具包的重要组成部分。然而，目前还没有办法在实验上操纵神经元网络中的连接映射来验证模型预测。该项目将使用一种新技术来改变真实的神经网络中的连接映射并测试模型预测。这个新系统结合了一些技术，使我们能够使用数码相机测量神经元的电活动，并通过照射特定颜色和强度的光来调节这种活动。该系统将测量和光刺激直接与神经元之间连接的复杂数学模型相结合，从而能够完全控制生物网络。成功的候选人将使用该系统的第一个原型来证明它可以操纵文化中小网络的连接图，从而在真实的生物网络中实现数学模型预测的活动模式。一旦在体外建立了概念验证，使用光来控制神经行为也将允许在体内研究功能网络。从长远来看，这一系统将使智能植入物的开发成为可能，以治疗以异常网络节律为特征的脑部疾病，这是基于诺加雷特教授在开发硬件神经起搏器方面的专业知识。这些植入物将检测异常大脑活动何时开始，然后照亮特定的神经元，以修改它们的连接，恢复正常、健康的活动。