Discovering optimal features for neuron-type identification from extracellular recordings

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Vergil R. Haynes, Yi Zhou, Sharon M. Crook
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引用次数: 0

Abstract

Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations. However, spatiotemporal EAP waveforms are the product of signals from underlying current sources being mixed within the extracellular space. We introduce a machine learning approach to demix the underlying sources of spatiotemporal EAP waveforms. Using biophysically realistic computational models, we simulate EAP waveforms and characterize them by the relative prevalence of these sources, which we use as features for identifying the neuron-types corresponding to recorded single units. These EAP sources have distinct spatial and multi-resolution temporal patterns that are robust to various sampling biases. EAP sources also are shared across many neuron-types, are predictive of gross morphological features, and expose underlying morphological domains. We then organize known neuron-types into a hierarchy of latent morpho-electrophysiological types based on differences in the source prevalences, which provides a multi-level classification scheme. We validate the robustness, accuracy, and interpretations of our demixing approach by analyzing simulated EAPs from morphologically detailed models with classification and clustering methods. This simulation-based approach provides a machine learning strategy for neuron-type identification.
从细胞外记录中发现识别神经元类型的最佳特征
体内单细胞活动(SUA)多通道记录技术的进步为发现空间变化的细胞外记录动作电位(EAPs)的新特征提供了机会,这些特征有助于识别神经元类型。传统的神经元类型分类方法通常依赖于根据单通道记录的惯例计算 EAP 波形特征,因此有其局限性。然而,时空 EAP 波形是来自潜在电流源的信号在细胞外空间混合的产物。我们介绍了一种机器学习方法,用于消除时空 EAP 波形的潜在来源。我们利用符合生物物理现实的计算模型模拟 EAP 波形,并通过这些源的相对普遍性来描述其特征,以此作为识别记录的单个单元对应的神经元类型的特征。这些 EAP 源具有独特的空间和多分辨率时间模式,不受各种采样偏差的影响。此外,EAP 源在许多神经元类型中是共享的,可预测总体形态特征,并揭示潜在的形态领域。然后,我们根据源流行率的差异,将已知神经元类型组织成一个潜在形态电生理类型的层次结构,从而提供了一个多层次的分类方案。我们通过使用分类和聚类方法分析形态详细模型的模拟 EAP,验证了我们的去混合方法的稳健性、准确性和解释性。这种基于模拟的方法为神经元类型识别提供了一种机器学习策略。
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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