Real-time classification of experience-related ensemble spiking patterns for closed-loop applications.
Real-time classification of experience-related ensemble spiking patterns for closed-loop applications.
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DOI：
10.7554/elife.36275
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发表时间：
2018-10-30
期刊：
影响因子：
7.7
通讯作者：
Kloosterman F
中科院分区：
文献类型：
--
作者：
Ciliberti D;Michon F;Kloosterman F
大脑皮层神经回路中的交流被认为是由持续约50-200毫秒的自发的时间组织的群体活动模式所介导的。闭环操作具有独特的力量，可以揭示这些模式与它们对认知的贡献之间的直接和因果联系。然而，目前的脑机接口并不能解释毫秒级的多神经元尖峰模式。为了弥补这一差距，我们开发了一个在闭环环境下对集合模式进行分类的系统，并展示了它在大鼠海马神经元重放序列在线识别中的应用。我们的系统以10ms的分辨率解码多神经元模式，在50ms内识别与经验相关的模式，敏感度和特异度超过70%，并以95%的准确率对其内容进行分类。这项技术可扩展到高数量的电极阵列，并将有助于阐明内部产生的神经活动对协调的神经组装交互和认知的贡献。
Communication in neural circuits across the cortex is thought to be mediated by spontaneous temporally organized patterns of population activity lasting ~50 –200 ms. Closed-loop manipulations have the unique power to reveal direct and causal links between such patterns and their contribution to cognition. Current brain–computer interfaces, however, are not designed to interpret multi-neuronal spiking patterns at the millisecond timescale. To bridge this gap, we developed a system for classifying ensemble patterns in a closed-loop setting and demonstrated its application in the online identification of hippocampal neuronal replay sequences in the rat. Our system decodes multi-neuronal patterns at 10 ms resolution, identifies within 50 ms experience-related patterns with over 70% sensitivity and specificity, and classifies their content with 95% accuracy. This technology scales to high-count electrode arrays and will help to shed new light on the contribution of internally generated neural activity to coordinated neural assembly interactions and cognition.