A systems approach to the cellular and molecular organization of neural circuits for representation of space

用于空间表示的神经回路的细胞和分子组织的系统方法

• 批准号: BB/L010496/1
• 负责人: Matthew Nolan
• 金额: $ 91.67万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FL010496%2F1
• 关键词: systems; approach; cellular; molecular; organization

One of the most challenging problems in science is to understand how the molecules expressed by nerve cells in the brain enable thoughts and actions to take place. This is of fundamental importance for understanding how brains work. It will also underpin future industrial development of therapies for neurological and psychiatric disorders, and of biologically inspired computing technologies. Synthesis of molecules and assembly of cells is similar in the brain and other organs of the body, but the brain is distinguished by its ability to efficiently perform computations of considerable complexity. These computations rely upon communication of electrical signals between nerve cells. Some important computations are carried out by groups of nerve cells organized into modules, but how these modules relate to organization of electrical signaling between nerve cells is not known. This is important because molecules that control electrical signaling are a critical molecular link between gene expression and cognitive processes.We will focus on a sub-region of the brain called the entorhinal cortex. During exploration, nerve cells at the upper end of this region form a module that encodes an animal's location at a relatively high resolution of approximately 30 cm. Lower down within this region, different modules of nerve cells encode location at lower resolutions. As existing approaches rely on recording electrical activity from neurons in live animals it is currently exceptionally challenging to examine their physical basis. We aim to solve this problem by instead using in vitro experiments in combination with quantitative and predictive computational models.We will first establish if electrical properties of single nerve cells or their connections have a modular organization. We will use electrodes to record from many nerve cells in single slices of tissue. If electrical properties contribute to modular organization, then we expect cells from the same network to be more similar to one another than cells from different networks. We will next evoke coordinated network activity while making electrical recordings simultaneously from four cells at a time. We expect to identify cells that are part of the same module by specific correlations in their activity. To identify molecules that organize electrical properties and connectivity, we will identify candidate genes that mark modules. We will then determine if they label specific subgroups of neurons based on their electrical properties and connectivity.Data obtained at each stage of experimentation will guide development of computer models. By comparison of the experimental results with the model predictions we will be able to refine and improve the predictive power of the models, while also identifying functions that the model may not yet explain and that will therefore require further investigation. In this way we aim to reveal new computational principles for brain operation and to ultimately enable direct links to be established between gene expression, electrical signaling and brain function.The models and experimental results generated will be of benefit and application in several areas. 1) By establishing basic links between genes, electrical signaling and computation by nerve cells, the study will be important for understanding the healthy brain. They will form a key foundation for further investigations of how specific genes influences brain function. 2) The brain region that we will focus on is an important target for drug discovery. The computational models that we build will enable dry lab testing of potential therapeutic strategies in development by pharmaceutical or biotechnology companies. 3) The principles uncovered may stimulate future design of biologically based computational devices. For example, to improve navigation by robots, and to develop neurally inspired architectures to improve the energy efficiency of computational hardware.

科学中最具挑战性的问题之一是理解大脑中神经细胞表达的分子是如何使思想和行动发生的。这对于理解大脑是如何工作的至关重要。它还将支持神经和精神疾病治疗以及受生物学启发的计算技术的未来工业发展。在大脑和身体的其他器官中，分子的合成和细胞的组装是相似的，但大脑的特点在于它能有效地进行相当复杂的计算。这些计算依赖于神经细胞之间的电信号交流。一些重要的计算是由一组神经细胞组成的模块来完成的，但是这些模块是如何与神经细胞之间的电信号组织联系起来的还不清楚。这很重要，因为控制电信号的分子是基因表达和认知过程之间的关键分子联系。我们将专注于大脑的一个叫做内嗅皮层的区域。在探测过程中，该区域上端的神经细胞形成一个模块，以大约30厘米的相对高分辨率编码动物的位置。在这个区域的下方，不同的神经细胞模块以较低的分辨率编码位置。由于现有的方法依赖于记录活体动物神经元的电活动，因此目前检查其物理基础非常具有挑战性。我们的目标是通过体外实验与定量和预测计算模型相结合来解决这个问题。我们将首先确定单个神经细胞或它们的连接是否具有模块化组织的电特性。我们将使用电极记录单片组织中的许多神经细胞。如果电学性质有助于模块化组织，那么我们预计来自同一网络的细胞比来自不同网络的细胞更相似。接下来，我们将唤起协调的网络活动，同时对四个细胞同时进行电记录。我们希望通过它们活动中的特定相关性来识别属于同一模块的细胞。为了确定组织电学性质和连通性的分子，我们将确定标记模块的候选基因。然后，我们将确定他们是否根据神经元的电特性和连通性来标记特定的神经元亚群。在实验的每个阶段获得的数据将指导计算机模型的发展。通过将实验结果与模型预测进行比较，我们将能够改进和提高模型的预测能力，同时也可以识别模型可能尚未解释的功能，因此需要进一步研究。通过这种方式，我们的目标是揭示大脑运作的新计算原理，并最终使基因表达，电信号和大脑功能之间建立直接联系。所建立的模型和实验结果将在多个领域具有一定的参考价值和应用价值。1)通过建立基因、电信号和神经细胞计算之间的基本联系，这项研究将对了解健康的大脑具有重要意义。它们将为进一步研究特定基因如何影响大脑功能奠定关键基础。2)我们将关注的大脑区域是药物发现的重要靶点。我们建立的计算模型将使制药或生物技术公司开发的潜在治疗策略的干实验室测试成为可能。3)揭示的原理可能会刺激未来基于生物学的计算设备的设计。例如，改进机器人的导航，开发神经启发的架构来提高计算硬件的能源效率。

项目成果

Local field potentials get funny.

局部场潜力变得有趣。

• DOI: 10.1113/jp272673
• 发表时间: 2016-07-01
• 期刊: The Journal of physiology
• 作者: Nolan MF

Inter- and intra-animal variation of integrative properties of stellate cells in the medial entorhinal cortex

内侧内嗅皮层星状细胞整合特性的动物间和动物内变异

• DOI: 10.1101/678565
• 发表时间: 2019
• 作者: Pastoll H

Laminar and dorsoventral molecular organization of the medial entorhinal cortex revealed by large-scale anatomical analysis of gene expression.

• DOI: 10.1371/journal.pcbi.1004032
• 发表时间: 2015-01
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Ramsden HL;Sürmeli G;McDonagh SG;Nolan MF
• 通讯作者: Nolan MF

Grid cells encode local head direction

• DOI: 10.1101/681312
• 发表时间: 2019-06
• 期刊: bioRxiv
• 作者: Klara Gerlei;Jessica Passlack;Ian Hawes;Brianna Vandrey;Holly Stevens;Ioannis Papastathopoulos;M. Nolan

Synaptic integrative mechanisms for spatial cognition.

空间认知的突触整合机制。

• DOI: 10.1038/nn.4652
• 发表时间: 2017
• 期刊: Nature neuroscience
• 影响因子: 25
• 作者: Schmidt-Hieber C