神经科学
计算机科学
神经活动
生物神经网络
期限(时间)
人工智能
神经网络
动力学(音乐)
大脑活动与冥想
纹状体
Spike(软件开发)
运动前神经元活动
脑电图
心理学
物理
多巴胺
软件工程
量子力学
教育学
作者
Ashesh K. Dhawale,Rajesh Poddar,Steffen B. E. Wolff,Valentin A Normand,Evi Kopelowitz,Bence P. Ölveczky
出处
期刊:eLife
[eLife Sciences Publications, Ltd.]
日期:2017-09-08
卷期号:6
被引量:125
摘要
Addressing how neural circuits underlie behavior is routinely done by measuring electrical activity from single neurons in experimental sessions. While such recordings yield snapshots of neural dynamics during specified tasks, they are ill-suited for tracking single-unit activity over longer timescales relevant for most developmental and learning processes, or for capturing neural dynamics across different behavioral states. Here we describe an automated platform for continuous long-term recordings of neural activity and behavior in freely moving rodents. An unsupervised algorithm identifies and tracks the activity of single units over weeks of recording, dramatically simplifying the analysis of large datasets. Months-long recordings from motor cortex and striatum made and analyzed with our system revealed remarkable stability in basic neuronal properties, such as firing rates and inter-spike interval distributions. Interneuronal correlations and the representation of different movements and behaviors were similarly stable. This establishes the feasibility of high-throughput long-term extracellular recordings in behaving animals.
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