Automated long-term recording and analysis of neural activity in behaving animals.
Automated long-term recording and analysis of neural activity in behaving animals.
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DOI：
10.7554/elife.27702
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发表时间：
2017-09-08
期刊：
影响因子：
7.7
通讯作者：
Ölveczky BP
中科院分区：
文献类型：
--
作者：
Dhawale AK;Poddar R;Wolff SB;Normand VA;Kopelowitz E;Ölveczky BP
研究神经回路如何构成行为的基础通常是通过测量实验中单个神经元的电活动来完成的。虽然这样的记录在指定的任务期间产生神经动态的快照，但它们不适合在与大多数发育和学习过程相关的较长时间尺度上跟踪单个单元活动，或者捕获不同行为状态的神经动态。在这里，我们描述了一个自动化平台，用于连续长期记录自由移动的啮齿动物的神经活动和行为。一种无监督的算法可以识别和跟踪单个单元在数周记录中的活动，大大简化了对大型数据集的分析。用我们的系统对运动皮层和纹状体长达数月的记录进行分析，发现基本神经元特性（如放电率和棘波间隔分布）具有显着的稳定性。神经元间的相关性和不同的运动和行为的代表性也同样稳定。这确立了在行为动物中进行高通量长期细胞外记录的可行性。
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.