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NetClamp: conducting neural network rhythms with mathematics

NetClamp：用数学控制神经网络节奏

基本信息

批准号：
EP/V048716/1
负责人：
Joel Tabak
金额：
$ 25.65万
依托单位：
UNIVERSITY OF EXETER
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FV048716%2F1
关键词：
NetClamp conducting neural network rhythms

项目摘要

All of our behaviours, from recognising friends, to remembering where we parked our car, to 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.Solving this problem requires cooperation between lab-based, and theory-based neuroscientists. Lab-based neuroscientists determine how individual neurons work electrically and determine how neurons communicate with each other. They create maps of connections which represent which neurons influence the electrical activity of which other neurons.Theory-based neuroscientists use this information to construct mathematical descriptions of neural network activity, called 'models'. Models predict how the map of connections in a network determines the patterns of electrical activity. They show that subtle changes in the map can have large effects on network activity. For example, activity can switch from seemingly random to highly coordinated, with rhythmic changes in neuron activity resembling a Mexican wave in football stadiums. In some brain regions, coordinated rhythms are healthy. For example, our breathing is controlled by a brain area with highly synchronised activity. In other contexts, such as epilepsy, high levels of synchrony can lead to seizures. By uncovering how maps of connections affect network activity, mathematical models can be a useful part of the neuroscientist toolkit. Models are only useful if we can show that they match results from experiments with real neural networks. This requires altering the map of connections between neurons. To this end, lab-based neuroscientists can turn on and off groups of neurons, and can block large fractions of the connections in a network in rather crude ways. However, there is currently no experimental way to alter the connections in the subtle way required for testing models.This project is about building new technology to address this. To do this, we will integrate recently developed experimental tools with models in a single system. The experimental tools allow us to measure electrical activity in neurons using a digital camera, and to alter this activity by shining light of specific colour and intensity on each neuron. The key component of our system is that we will combine these tools with a mathematical model of the connection map between neurons. The model will use the camera recordings of electrical activity to calculate what input signals each neuron in the network should receive according to the map of connections. It will then control an illumination system, which will shine light patterns to deliver the computed signals to each neuron. In this way, we will be able to manipulate the map of connections between neurons in the same way as our mathematical model.This system will enable lab-based neuroscientists to do experiments in a radically new way. They will be able to explore how communication between neurons affects network activity with the same freedom that theory-based neuroscientists enjoy with models. They will be able to directly test theories about how connection maps shape patterns of activity. This will be a significant step towards understanding how the brain creates behaviour.Our system will also enable the development of smart implants to treat brain diseases such as Parkinson's and epilepsy, which are characterised by abnormal network rhythms. When drugs fail in changing these rhythms, doctors may turn to drastically invasive medical procedures that permanently alter network structures. However, if the right connections can be altered at the right time, more subtle therapies might suffice. Future smart implants will detect when abnormal activity starts, then shine light to specific neurons to modify their connections to restore normal, healthy activity.

我们所有的行为，从认出朋友，到记住我们把车停在哪里，再到泡一杯茶，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。理解这些模式是如何产生的是神经科学的关键问题之一，解决这个问题需要实验室和理论基础的神经科学家之间的合作。基于实验室的神经科学家确定单个神经元如何电工作，并确定神经元如何相互通信。他们创建的连接图代表了哪些神经元影响其他神经元的电活动。基于理论的神经科学家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图如何决定电活动的模式。他们表明，地图中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，神经元活动的节奏变化类似于足球场上的墨西哥波。在某些大脑区域，协调的节奏是健康的。例如，我们的呼吸是由一个具有高度同步活动的大脑区域控制的。在其他情况下，如癫痫，高水平的同步可能导致癫痫发作。通过揭示连接图如何影响网络活动，数学模型可以成为神经科学家工具包的有用部分。只有当我们能够证明模型与真实的神经网络的实验结果相匹配时，模型才是有用的。这需要改变神经元之间的连接图。为此，基于实验室的神经科学家可以打开和关闭神经元组，并可以以相当粗糙的方式阻止网络中的大部分连接。然而，目前还没有实验性的方法来改变连接的微妙方式所需的测试模型。这个项目是关于建立新的技术来解决这个问题。为此，我们将把最近开发的实验工具与模型集成在一个系统中。实验工具允许我们使用数码相机测量神经元的电活动，并通过在每个神经元上照射特定颜色和强度的光来改变这种活动。我们系统的关键组成部分是，我们将联合收割机这些工具与神经元之间连接映射的数学模型相结合。该模型将使用相机记录的电活动来计算网络中的每个神经元应该根据连接图接收什么输入信号。然后，它将控制一个照明系统，该系统将发出光图案，将计算出的信号传递给每个神经元。通过这种方式，我们将能够以与我们的数学模型相同的方式操纵神经元之间的连接图，这一系统将使实验室神经科学家能够以一种全新的方式进行实验。他们将能够探索神经元之间的通信如何影响网络活动，这与基于理论的神经科学家在模型中享有的自由相同。他们将能够直接测试关于连接图如何塑造活动模式的理论。这将是理解大脑如何创造行为的重要一步。我们的系统还将使智能植入物的开发成为可能，以治疗帕金森氏症和癫痫等脑部疾病，这些疾病的特征是异常的网络节律。当药物无法改变这些节律时，医生可能会转向彻底侵入性的医疗程序，永久改变网络结构。然而，如果正确的连接可以在正确的时间改变，更微妙的治疗可能就足够了。未来的智能植入物将检测异常活动何时开始，然后将光线照射到特定的神经元，以修改它们的连接，从而恢复正常，健康的活动。