MACHINE LEARNING APPROACHES FOR ELECTROPHYSIOLOGICAL CELL CLASSIFICATION

电生理细胞分类的机器学习方法

基本信息

批准号：
9568053
负责人：
ALISON L BARTH
金额：
$ 19.76万
依托单位：
CARNEGIE-MELLON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-30 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9568053
关键词：
Acute Algorithms Animals Archaeology Architecture Area Behavior Biological Brain Cells Cerebral cortex Classification Clear Cell Cognition Collaborations Collection Complex Computers Cortical Column Data Data Set Development Dissent Distant Electrophysiology (science)Fiber Goals Greek In Vitro Interneurons Knowledge Label Link Machine Learning Methods Molecular Mus Neurons Neurophysiology - biologic function Parvalbumins Pattern Perception Performance Population Property Publishing Sampling Scientist Signal Transduction Slice Somatostatin Statistical Data Interpretation Stimulus Techniques Technology Testing Text Thalamic structure Training Translations Work awake base behavioral response cell type cognitive process excitatory neuron extracellular in vivo information processing inhibitory neuron neural circuit novel optogenetics relating to nervous system response sensory cortex somatosensory transmission process virtual

项目摘要

ABSTRACT We will use our expertise in somatosensory organization and plasticity to develop novel and automated solutions for cell identification based upon neural activity, in order to decode the algorithms neural circuits use for information processing. Extracellular recordings in sensory cortex have been thought to primarily represent excitatory neuron activity, since these cells comprise ~80% of the total cell population. However, targeted cell recordings in S1 reveal that firing activity is dominated by inhibitory neurons, and that excitatory neurons can show 10-100 fold lower firing rates depending on cortical layer. Furthermore, new findings that reveal the complex relationship between different subtypes of inhibitory neurons make it difficult to relate blindly-recorded firing activity to local- or network-level computations. Clearly, cell-types matter, and massively parallel extracellular recordings that do not enable the simultaneous identification of multiple cell types will be limited in identifying principles for information transmission and encoding. Based on our preliminary findings, we hypothesize that the complex spontaneous and evoked spike trains from molecularly-identified neurons will provide unique and cell-type specific signatures that will enable cell identification from in vivo extracellular recordings. In collaboration with computer scientists at Carnegie Mellon, we will develop machine-learning algorithms for cell classification, using data collected from in vitro and in vivo recordings.

抽象的我们将利用我们在体感组织和可塑性方面的专业知识来开发新颖且基于神经活动的细胞识别自动化解决方案，以解码用于信息处理的神经电路算法。细胞外感觉记录皮层被认为主要代表兴奋性神经元活动，因为这些细胞约占总细胞群的 80%。然而，S1 中的目标细胞记录表明放电活动以抑制性神经元为主，兴奋性神经元可表现出 10-100 根据皮质层折叠较低的发射率。此外，新的发现揭示了抑制性神经元不同亚型之间的复杂关系使得很难将其联系起来盲目记录本地或网络级计算的触发活动。显然，细胞类型很重要，以及无法同时识别的大规模并行细胞外记录多种细胞类型的识别信息传输原理将受到限制编码。根据我们的初步发现，我们假设复杂的自发从分子鉴定的神经元诱发的尖峰序列将提供独特的细胞类型特定的签名将使细胞能够从体内细胞外记录中进行识别。在我们将与卡内基梅隆大学的计算机科学家合作开发机器学习使用从体外和体内记录收集的数据进行细胞分类的算法。