从神经元到认知:精确记录行为背后的神经活动的技术。

IF 5 Q1 ENGINEERING, BIOMEDICAL
BME frontiers Pub Date : 2020-12-25 eCollection Date: 2020-01-01 DOI:10.34133/2020/7190517
Richard H Roth, Jun B Ding
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引用次数: 6

摘要

理解大脑活动如何编码信息和控制行为是神经科学中一个长期存在的问题。这个复杂的问题需要神经科学和工程学的共同努力,包括在体内进行高精度和大规模神经元活动记录的技术解决方案,以及可靠测量和量化行为的无偏见方法。由于遗传学、分子生物学、工程和神经科学的进步,近几十年来,各种用于记录清醒动物神经元活动的光学成像和电生理方法已被开发并广泛应用于该领域。此外,已经开发出复杂的计算机视觉和机器学习算法来分析动物行为。在这篇综述中,我们概述了神经元记录的技术现状,重点是啮齿类动物的光学和电生理方法。此外,我们还讨论了未来技术发展需要涵盖的领域,以进一步了解行为背后的神经活动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

From Neurons to Cognition: Technologies for Precise Recording of Neural Activity Underlying Behavior.

From Neurons to Cognition: Technologies for Precise Recording of Neural Activity Underlying Behavior.

From Neurons to Cognition: Technologies for Precise Recording of Neural Activity Underlying Behavior.

From Neurons to Cognition: Technologies for Precise Recording of Neural Activity Underlying Behavior.

Understanding how brain activity encodes information and controls behavior is a long-standing question in neuroscience. This complex problem requires converging efforts from neuroscience and engineering, including technological solutions to perform high-precision and large-scale recordings of neuronal activity in vivo as well as unbiased methods to reliably measure and quantify behavior. Thanks to advances in genetics, molecular biology, engineering, and neuroscience, in recent decades, a variety of optical imaging and electrophysiological approaches for recording neuronal activity in awake animals have been developed and widely applied in the field. Moreover, sophisticated computer vision and machine learning algorithms have been developed to analyze animal behavior. In this review, we provide an overview of the current state of technology for neuronal recordings with a focus on optical and electrophysiological methods in rodents. In addition, we discuss areas that future technological development will need to cover in order to further our understanding of the neural activity underlying behavior.

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来源期刊
CiteScore
7.10
自引率
0.00%
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审稿时长
16 weeks
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