Using mathematics to disentangle the role of synaptic communication

用数学来阐明突触通讯的作用

• 批准号: MR/X034240/1
• 负责人: Kyle Wedgwood
• 金额: $ 185.14万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FX034240%2F1
• 关键词: Using; mathematics; disentangle; role; synaptic

This fellowship will provide new understanding on how neuronal networks encode and store information by incorporating mathematical models in electrophysiology experiments to enable direct manipulation, at cellular scales, of synaptic communication parameters including synaptic conductances, axonal and propagation delays, and network connectivity features. Neuronal networks operate through a combination of spiking electrical activity generated in individual neurons and communication (typically through synapses) between cells, which together allow the network to generate a variety of distinct patterns of electrical activity. In turn, these electrical activity patterns underpin a wide range of neuronal computation in sensing, learning and memory.Decades of electrophysiological research, supported by mathematical modelling, have yielded key insights into the response properties of individual neurons to stimuli. Far less progress has been made in understanding how communication properties shape electrical network rhythms. In part, this is due to the difficulty in ascertaining the so-called 'wiring diagram' that details which neurons communicate with one another. In addition, there are no experimental tools to directly modify communication properties at fine scales. Were such tools available, they would facilitate quantitative investigations that directly link communication properties to network rhythms. This fellowship will develop such tools by embedding mathematical models using closed-loop real-time feedback during electrophysiological recordings to enable direct manipulation of communication parameters.The tools developed during this fellowship will be used to characterise and quantify the role of synaptic parameters, such as synaptic conductances and transmission delay, in the generation of electrical rhythms in neuronal networks. I will first develop a mathematical model of synaptic communication in a cultured neuronal network. This model, which will be calibrated to patch clamp and voltage imaging recordings of network activity, will account for the dynamics of synaptic communication as well as the network wiring diagram. The model will be analysed through bifurcation analysis and numerical simulation to predict how the number and type of electrical rhythms supported by the network changes with respect to:1) variation of communication parameters such as synapse conductance and axonal and dendritic propagation delays; 2) heterogeneity in communication parameters across the network;3) synaptic plasticity, in which synaptic conductances vary in response to network activity. A closed-loop control strategy will be used to test these predictions by modulating the synaptic communication properties in real neuronal networks in the same way as in the mathematical model. The fellowship will thus provide a framework for hypothesis generation and testing on the contribution of individual synaptic parameters to neuronal network rhythms. The first phase of the fellowship (years 1-4) will use cultured cell lines to minimise its ethical costs. In the second phase (years 5-7), studies will be performed in neuronal networks cultured from patient-derived induced pluripotent stem cells, made available through collaboration with experimental partners, to investigate how synaptic deficits contribute to the aberrant electrical rhythms observed in these networks.This fellowship will provide deeper understanding of the fundamental mechanisms associated with neuronal network functioning at small scales. This improved understanding may help, in the long term, to treat disorders such as autism and motor neuron disease. In the shorter term, this will accelerate the development of so-called biological neuronal networks that attempt to harness the complexity of cultured neuronal networks to improve the performance of machine learning algorithms by replacing computer chips with real neurons.

该奖学金将提供关于神经元网络如何编码和存储信息的新理解，通过将数学模型纳入电生理学实验，以实现在细胞尺度上直接操纵突触通信参数，包括突触电导，轴突和传播延迟以及网络连接功能。神经元网络通过在单个神经元中产生的尖峰电活动和细胞之间的通信（通常通过突触）的组合来操作，它们一起允许网络产生各种不同的电活动模式。反过来，这些电活动模式支撑着传感、学习和记忆中的广泛神经元计算。数十年的电生理学研究，在数学建模的支持下，已经对单个神经元对刺激的响应特性产生了关键的见解。在理解通信特性如何塑造电网络节奏方面，进展要小得多。在某种程度上，这是由于难以确定所谓的“接线图”，即详细说明哪些神经元相互通信。此外，还没有实验工具来直接修改精细尺度上的通信特性。如果有这样的工具，它们将促进直接将通信特性与网络节奏联系起来的定量研究。该研究项目将通过在电生理记录过程中使用闭环实时反馈嵌入数学模型来开发此类工具，以实现对通信参数的直接操作。该研究项目期间开发的工具将用于确定和量化突触参数（如突触电导和传输延迟）在神经元网络中产生电节律的作用。我将首先在一个培养的神经元网络中建立一个突触通讯的数学模型。这个模型，将被校准到膜片钳和电压成像记录的网络活动，将占突触通信的动态以及网络布线图。将通过分叉分析和数值模拟来分析该模型，以预测网络支持的电节律的数量和类型如何在以下方面变化：1）通信参数的变化，如突触电导和轴突和树突传播延迟; 2）整个网络中通信参数的异质性;3）突触可塑性，其中突触电导响应于网络活动而变化。一个闭环控制策略将被用来测试这些预测调制的突触通信特性在真实的神经元网络中以同样的方式在数学模型。因此，该奖学金将提供一个框架，假设生成和测试的贡献，个别突触参数的神经元网络的节奏。研究金的第一阶段（1-4年）将使用培养的细胞系，以尽量减少其伦理成本。第二阶段（5-7年级），通过与实验伙伴合作，在从患者来源的诱导多能干细胞培养的神经元网络中进行研究，探讨突触缺陷如何导致在这些网络中观察到的异常电节律。该奖学金将在小尺度上加深对神经元网络功能相关的基本机制的理解。从长远来看，这种改进的理解可能有助于治疗自闭症和运动神经元疾病等疾病。从短期来看，这将加速所谓的生物神经元网络的发展，这种网络试图利用培养的神经元网络的复杂性，通过用真实的神经元取代计算机芯片来提高机器学习算法的性能。