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Is "sensory cortex" just sensory? Investigation by large-scale electrophysiology

“感觉皮层”只是感觉吗？

基本信息

批准号：
2619664
负责人：
金额：
--
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2619664
关键词：
sensory cortex just Investigation large

项目摘要

A fundamental question in sensory neuroscience is what information sensory neurons convey during natural behaviour ('sensory coding'). Neuroscience textbooks describe in detail how the cortex is parcelled up into 'sensory' and 'motor' regions. However, new studies have challenged this dogma by showing that the activity of neurons in areas of the cerebral cortex classically considered to be purely sensory actually depend on an animal's motor state (eg running speed or whisker movement). The full extent and significance of these findings remains, however, unclear.The aim of this project is to use exciting new methods (large-scale electrophysiological recording, behaviour imaging, machine learning) to investigate this general issue. Specifically, we will test between competing hypotheses for how motor state influences the activity of neurons in sensory cortex. This will be done by recording the activity of neurons in the somatosensory cortex using state-of-the-art Neuropixels probes. This project will suit motivated students with a passion for neuroscience. Students might come either a biology/neuroscience background or from a physical sciences background. The successful student will have the opportunity to investigate this question using in vivo electrophysiology and behavioural analysis. They will also have the chance to learn to analyse the data by means of machine learning methods.

感觉神经科学的一个基本问题是感觉神经元在自然行为中传递什么信息（“感觉编码”）。神经科学教科书详细描述了大脑皮层是如何被分成“感觉”和“运动”区域的。然而，新的研究挑战了这一教条，表明大脑皮层中被认为是纯粹感觉的区域的神经元活动实际上取决于动物的运动状态（例如跑步速度或胡须运动）。然而，这些发现的全部范围和意义仍不清楚。该项目的目的是使用令人兴奋的新方法（大规模电生理记录，行为成像，机器学习）来研究这个普遍问题。具体来说，我们将在相互竞争的假设之间测试运动状态如何影响感觉皮层神经元的活动。这将通过使用最先进的神经像素探针记录体感觉皮层神经元的活动来完成。这个项目将适合对神经科学有热情的学生。学生可能有生物学/神经科学背景，也可能有物理科学背景。成功的学生将有机会利用体内电生理学和行为分析来研究这个问题。他们还将有机会学习通过机器学习方法分析数据。