How many neurons can we see with current spike sorting algorithms?
How many neurons can we see with current spike sorting algorithms?
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DOI：
10.1016/j.jneumeth.2012.07.010
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发表时间：
2012-10-15
影响因子：
3
通讯作者：
Quiroga, Rodrigo Quian
中科院分区：
文献类型：
--
作者：
Pedreira, Carlos;Martinez, Juan;Quiroga, Rodrigo Quian
最近的研究强调了细胞外记录观察到的神经元的典型数量与基于解剖学和生理学考虑预期的神经元数量之间的不一致。这种分歧主要归因于稀疏放电神经元的存在。然而，这也可能是由于用于处理数据的尖峰排序算法的限制。为了解决这个问题，我们使用了细胞外记录的真实模拟，并发现包含大量神经元的模拟的尖峰分选性能相对较差。事实上，当数据中存在多达20个单位时，用于单通道记录的正确识别的神经元的数量显示出在约8-10个单位处饱和的渐近行为。对于具有低放电率的神经元，这种性能明显较差，因为在包含许多神经元的模拟中，这些单元被错过的可能性是具有高放电率的单元的两倍。这些结果揭示了细胞外记录中发现的神经元数量相对较少的主要原因之一，也强调了进一步发展尖峰分选算法的重要性。(C)2012爱思唯尔有限公司版权所有。
Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8-10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms. (C) 2012 Elsevier B.V. All rights reserved.