Novel analytical and datasharing tools for rich neuronal activity datasets obtained with a 4096 electrodes array

中枢神经系统(CNS)功能的复杂性源于特定网络中不同类型神经元之间复杂的解剖和动态相互作用。因此，神经回路中信息的编码是单个神经元之间相互作用的结果，也是通过微回路(由几个神经元组成)和涉及数千到数百万个细胞的大规模网络之间的相互作用而发生的。当今神经科学的一大挑战是了解这些神经网络是如何形成的，以及它们是如何运作的。这样的挑战只能通过同时记录在特定神经元任务中变得活跃的数千个神经元来解决。实现这一目标的实验方法之一是使用多电极阵列(MEA)，该阵列由几个通道(电极)组成，每个通道(电极)可以记录(和/或刺激)CNS特定区域内的几个相邻神经元。MEAs可用于体外记录分离的神经元培养物、脑片或分离的视网膜。这些MEA由嵌在平面衬底中的电极组件组成。典型的商用MEA由60-128个电极组成，间距为100-200微米。考虑到哺乳动物中枢神经系统中的一个普通神经元的直径约为10um，很明显，这种MEA不能传达涉及特定网络的所有神经元的活动信息，而只是来自这些细胞的样本。为了克服这种采样不足的问题，在这个项目中，我们将使用有源像素传感器(APS)MEA，这是我们的合作者Luca Berdondini(意大利理工学院，Genova)在Nest-EU项目中开发的一种新型MEA平台。这种MEA由4,096个电极组成，分辨率接近细胞分辨率(21x21um，42um中心到中心间隔，覆盖2.5 mm x 2.5 mm的有源区)，可以同时从所有通道进行记录。我们将使用APS MEA记录新生脊椎动物视网膜中存在的自发活动波。这些波发生在围产期的短时期内，已知它们在引导视觉系统中视网膜和视网膜外水平的神经连接的精确连接方面发挥着重要作用。APS-MEA由于其前所未有的大小和分辨率，将使我们能够以前所未有的方式对这些波的精确动态进行新的洞察。从如此大规模的网络以接近蜂窝分辨率的记录产生极其丰富的数据集，缺点是这些数据集非常大且难以处理，因此需要开发新的强大的分析工具，能够以快速、高效和用户友好的方式解码网络中的蜂窝元素如何相互作用。开发这样的计算工具是这个项目的中心目标，而视网膜的实验工作定义了一个具有挑战性和独特的科学背景。我们计划开发的工具将产生一些参数，这些参数将帮助我们更好地了解网络功能，从单个神经元的临时放电模式到活动如何在网络中准确传播。我们还将开发新的工具，以便更容易地可视化网络中活动的动态行为。这些工具将以一种其他调查员使用同一录音系统或他们选择的其他平台可以很容易地使用的语言开发。最后，为了确保广泛的神经生理学社区可以使用这些工具，它们将部署在Carmen(电子神经科学的代码分析、存储库和建模)上，这是一个新的基于互联网的神经生理学共享资源，旨在促进合作的神经生理学家之间的全球交流。

The functional intricacy of the central nervous system (CNS) arises from the complex anatomical and dynamic interactions between different types of neurones involved in specific networks. Hence, the encoding of information in neural circuits occurs as a result of interactions between individual neurones as well as through the interplay within both microcircuits (made of few neurones) and large scale networks involving thousands to millions of cells. One of the great challenges of neuroscience nowadays is to understand how these neural networks are formed and how they operate. Such challenge can be resolved only through simultaneous recording from thousands of neurones that become active during specific neuronal tasks. One of the experimental approaches to fulfil this goal is to use multielectrode arrays (MEAs) that consist of several channels (electrodes) that can each record (and/or stimulate) from few adjacent neurones within a particular area of the CNS. MEAs can be used in vitro to record from dissociated neuronal cultures or from brain slices or isolated retinas. These MEAs consist of assemblies of electrodes embedded in planar substrates. Typical commercial MEAs consist of 60-128 electrodes with a spacing of 100-200 um. Considering that a generic neurone in the mammalian CNS has a diameter of about 10 um, it is obvious that such MEAs cannot convey information on the activity of all neurones involved in a specific network, but rather just from a sample of these cells. To overcome this activity under-sampling, in this project, we will use the Active Pixel Sensor (APS) MEA, a novel type of MEA platform developed in a NEST-EU Project by our collaborator Luca Berdondini (Italian Institute of Technology, Genova). This MEA consists of 4,096 electrodes with near cellular resolution (21x21 um, 42 um centre-to-centre separation, covering an active area of 2.5 mm x 2.5 mm), where recording is possible from all channels at the same time. We will use the APS MEA to record spontaneous waves of activity that are present in the neonatal vertebrate retina. These waves occur during a short period of development during perinatal weeks and they are known to play an important role in guiding the precise wiring of neural connections in the visual system, both at the retinal and extra-retinal levels. The APS-MEA, thanks to its unmet size and resolution, will enable us to reach new insights into the precise dynamics of these waves as never achieved before. Recordings from such large scale networks at near cellular resolution generate extremely rich datasets with the drawback that these datasets are very large and difficult to handle, thus necessitating the development of new powerful analytical tools enabling to decode in a fast, efficient and user-friendly way how cellular elements interact in the network. The development of such computational tools is the central goal of this project, while the experimental work on the retina defines a challenging and unique scientific context. The tools we plan to develop will yield parameters that will help us reach better understanding of network function, from the temporal firing patterns of individual neurones to how activity precisely propagates within the network. We will also develop novel tools for easier visualisation of the dynamical behaviour of the activity within the network. These tools will be developed in a language that could be easily utilized by other investigators using the same recording system or other platforms of their choice. Finally, to ensure that these tools are accessible to the wide neurophysiology community, they will be deployed on CARMEN (Code Analysis, Repository and Modelling for e-Neuroscience), a new internet-based neurophysiology sharing resource designed for facilitating worldwide communication between collaborating neurophysiologists.