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Neural network based pattern matching and spike detection tools and services— in the CARMEN neuroinformatics project

机译:CARMEN神经信息学项目中基于神经网络的模式匹配和峰值检测工具和服务

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摘要

In the study of information flow in the brain, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are distributed CARMEN Active Information Repository Nodes (CAIRNs) which provide: data and metadata storage, new and third party / legacy services and tools. In this paper, we describe the CARMEN project as well as the CAIRN infrastructure. In particular, we will introduce an adapted version of the Signal Data Explorer (SDE) tool and a discussion of spike detection services. The SDE tool provides data visualization, signal processing, pattern matching and act as a client for the CAIRN Grid services. The SDE was developed for use in the aero-engine domain but is being expanded and used in CARMEN to visualise and search raw and derived neuroscience data. The SDE uses an AURA (Advanced Uncertain Reasoning Architecture) neural network to perform extremely fast pattern matching. The papers shows SDE and the AURA pattern match services detecting spikes from neuronal data and how the tools can be used to search for complex conditions comprising of many different patterns across the large data sets that are typical in neuroinformatics. The SDE provides a valuable means of querying patterns from the CARMEN data depository which is complementary to conventional text based querying (using metadata, etc.) to provide further insight and annotations where possible. Spike detection services which use wavelet and morphology techniques are discussed and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be supplied to users of the CARMEN infrastructure to allow users to compare the performance.
机译:在研究大脑中的信息流时,可以使用一系列电生理和成像技术来研究组成过程。尽管数据难以生成且昂贵,但很少共享和协作利用。电子神经科学代码分析,存储库和建模(CARMEN)项目通过提供虚拟神经科学实验室来解决这一挑战:虚拟实验室用于共享数据,工具和服务。 CARMEN概念的核心是分布式CARMEN活动信息存储库节点(CAIRN),这些节点提供:数据和元数据存储,新的和第三方/旧式服务和工具。在本文中,我们描述了CARMEN项目以及CAIRN基础设施。特别是,我们将介绍信号数据资源管理器(SDE)工具的改编版本,并讨论尖峰检测服务。 SDE工具提供数据可视化,信号处理,模式匹配,并充当CAIRN Grid服务的客户端。 SDE被开发用于航空发动机领域,但正在扩展并用于CARMEN,以可视化和搜索原始和衍生的神经科学数据。 SDE使用AURA(高级不确定推理体系)神经网络来执行非常快速的模式匹配。论文显示了SDE和AURA模式匹配服务可检测神经元数据中的尖峰,以及如何使用该工具来搜索复杂条件,这些条件包括神经信息学中典型的大型数据集中的许多不同模式。 SDE提供了一种从CARMEN数据存储库中查询模式的有价值的手段,它是对常规基于文本的查询(使用元数据等)的补充,以在可能的情况下提供进一步的见解和注释。讨论了使用小波和形态学技术的峰值检测服务,并证明它们优于传统的阈值和基于模板的系统。 CARMEN基础设施的用户可以使用多种不同的峰值检测和分类技术,以使用户可以比较性能。

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