CARMEN: Code analysis, repository, and modelling for e-Neuroscience

CARMEN：电子神经科学的代码分析、存储库和建模

• 批准号: EP/E002331/1
• 负责人: Colin Ingram
• 金额: $ 511.43万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FE002331%2F1
• 关键词: CARMEN; Code; analysis; repository; modelling

Research in neurophysiology includes both analysis of data from neuronal systems (networks of brain cells; both live and cultured), and development of models to explain both the processes that form the character of data, and the high level function that these express; i.e. behaviour and thought. Capturing and analysing data from neuronal systems is time-consuming, difficult and expensive: many techniques exist, some using multichannel electrical recording, and some using ion-sensitive fluorescent dyes. Different techniques have different advantages: some have high time resolution, whereas others have high space resolution. The models that derive from this data also exist at many levels, from the detailed modelling of membrane-embedded ion channels and neurotransmitters to compartmental neural models, through models of small neural networks, to larger models of many thousands of neurons. All models and algorithms are hungry for data to determine their many parameters and characteristics. Currently this activity is largely a one-lab science: datasets are shared within a lab, and with some computational modellers. The research is also not organised to ensure that data and models produced by small communities of specialist researchs can easily be integrated to contribute to the bigger picture. Datasets are discarded after the experimentor has completed their experimental report, or are archived in a format that is not widely accessible. This project aims to use the GRID to change that: it will enable experimenters to archive their datasets in a structure, making them widely accessible for modellers and algorithm developers to exploit. Experimental datasets are useless without accurate descriptions of the experimental conditions, and hence an appropriate set of metadata will developed to augment the data, allowing the project researchers to collaborate more widely and persistently by sharing data in a sensible, referenced form. Further, the project will provide integrated and co-ordinated services for the neuroscience data, enabling neuronal signal detection, sorting and analysis, as well as visualisation and modelling. Data security is critically important to experimentors: they do not wish to be simply anonymous contributors of data, but to be directly involved in further analysis of their datasets, and this will be supported. Further we will enable direct near real-time analysis of streamed experimental data, providing information to distributed teams of specialists that will allow difficult experiments to be optimised. These interventions will catalyse a step change in research practice in this area of neuroscience, which will allow best value to be derived from the significant research investment that is made in order to understand the brain.

神经生理学的研究既包括对神经系统(脑细胞网络；活的和培养的)数据的分析，也包括模型的开发，以解释形成数据特征的过程，以及这些过程所表达的高级功能；即行为和思想。从神经系统获取和分析数据既耗时、困难又昂贵：存在许多技术，一些使用多通道电子记录，一些使用离子敏感荧光染料。不同的技术有不同的优势：一些技术具有高时间分辨率，而另一些技术具有高空间分辨率。从这些数据得出的模型也存在于许多层面，从膜嵌入的离子通道和神经递质的详细模型到隔室神经模型，再到小型神经网络的模型，再到数千个神经元的更大模型。所有的模型和算法都迫切需要数据来确定它们的许多参数和特性。目前，这项活动在很大程度上是一门只有一个实验室的科学：数据集在实验室内共享，并与一些计算建模人员共享。这项研究也没有被组织起来，以确保由专业研究人员组成的小社区产生的数据和模型可以很容易地整合在一起，为更大的图景做出贡献。在实验者完成他们的实验报告后，数据集被丢弃，或者以一种不能广泛访问的格式进行存档。该项目旨在使用网格来改变这一点：它将使实验者能够将他们的数据集归档到一个结构中，使建模人员和算法开发人员能够广泛地访问这些数据集。如果没有对实验条件的准确描述，实验数据集是无用的，因此将开发一组适当的元数据来增强数据，允许项目研究人员通过以合理的参考形式共享数据来进行更广泛和持久的合作。此外，该项目将为神经科学数据提供综合和协调的服务，使神经元信号检测、分类和分析以及可视化和建模成为可能。数据安全对实验者至关重要：他们不希望成为简单的匿名数据贡献者，而是直接参与对其数据集的进一步分析，这将得到支持。此外，我们将实现对流式实验数据的直接、接近实时的分析，向分散的专家团队提供信息，从而使困难的实验得以优化。这些干预措施将促进这一神经科学领域的研究实践的一步变化，这将使为了解大脑而进行的重大研究投资产生最佳价值。

项目成果

Efficient population coding of naturalistic whisker motion in the ventro-posterior medial thalamus based on precise spike timing.

• DOI: 10.3389/fncir.2015.00050
• 发表时间: 2015
• 期刊: Frontiers in neural circuits
• 影响因子: 3.5
• 作者: Bale MR;Ince RA;Santagata G;Petersen RS
• 通讯作者: Petersen RS

Automatic network fingerprinting through single-node motifs.

• DOI: 10.1371/journal.pone.0015765
• 发表时间: 2011-01-31
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Echtermeyer C;Costa Lda F;Rodrigues FA;Kaiser M
• 通讯作者: Kaiser M

Canalization of genetic and pharmacological perturbations in developing primary neuronal activity patterns.

• DOI: 10.1016/j.neuropharm.2015.07.027
• 发表时间: 2016-01
• 期刊: Neuropharmacology
• 影响因子: 4.7
• 作者: Charlesworth P;Morton A;Eglen SJ;Komiyama NH;Grant SG
• 通讯作者: Grant SG

Beyond element-wise interactions: identifying complex interactions in biological processes.

超越元素间的相互作用：识别生物过程中的复杂相互作用

• DOI: 10.1371/journal.pone.0006899
• 发表时间: 2009-09-23
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Ladroue C;Guo S;Kendrick K;Feng J
• 通讯作者: Feng J

Abnormal Connectional Fingerprint in Schizophrenia: A Novel Network Analysis of Diffusion Tensor Imaging Data.

• DOI: 10.3389/fpsyt.2016.00114
• 发表时间: 2016
• 期刊: Frontiers in psychiatry
• 影响因子: 4.7
• 作者: Edwin Thanarajah S;Han CE;Rotarska-Jagiela A;Singer W;Deichmann R;Maurer K;Kaiser M;Uhlhaas PJ
• 通讯作者: Uhlhaas PJ