Lyzwa Dominika, Herrmann J Michael, Wörgötter Florentin
Max Planck Institute for Dynamics and Self-OrganizationGöttingen, Germany; Institute for Nonlinear Dynamics, Physics Department, Georg-August-UniversityGöttingen, Germany; Bernstein Focus NeurotechnologyGöttingen, Germany.
Bernstein Focus NeurotechnologyGöttingen, Germany; Institute of Perception, Action and Behavior, School of Informatics, University of EdinburghEdinburgh, UK.
Front Neural Circuits. 2016 Feb 1;9:91. doi: 10.3389/fncir.2015.00091. eCollection 2015.
How complex natural sounds are represented by the main converging center of the auditory midbrain, the central inferior colliculus, is an open question. We applied neural discrimination to determine the variation of detailed encoding of individual vocalizations across the best frequency gradient of the central inferior colliculus. The analysis was based on collective responses from several neurons. These multi-unit spike trains were recorded from guinea pigs exposed to a spectrotemporally rich set of eleven species-specific vocalizations. Spike trains of disparate units from the same recording were combined in order to investigate whether groups of multi-unit clusters represent the whole set of vocalizations more reliably than only one unit, and whether temporal response correlations between them facilitate an unambiguous neural representation of the vocalizations. We found a spatial distribution of the capability to accurately encode groups of vocalizations across the best frequency gradient. Different vocalizations are optimally discriminated at different locations of the best frequency gradient. Furthermore, groups of a few multi-unit clusters yield improved discrimination over only one multi-unit cluster between all tested vocalizations. However, temporal response correlations between units do not yield better discrimination. Our study is based on a large set of units of simultaneously recorded responses from several guinea pigs and electrode insertion positions. Our findings suggest a broadly distributed code for behaviorally relevant vocalizations in the mammalian inferior colliculus. Responses from a few non-interacting units are sufficient to faithfully represent the whole set of studied vocalizations with diverse spectrotemporal properties.
听觉中脑的主要汇聚中心——中央下丘是如何表征复杂自然声音的,这仍是一个悬而未决的问题。我们应用神经辨别技术来确定在中央下丘的最佳频率梯度上,单个发声的详细编码变化。该分析基于多个神经元的集体反应。这些多单元脉冲序列是从暴露于11种具有物种特异性且频谱时间丰富的发声的豚鼠身上记录的。来自同一记录中不同单元的脉冲序列被组合起来,以研究多单元簇组是否比单个单元更可靠地代表整个发声集合,以及它们之间的时间响应相关性是否有助于对发声进行明确的神经表征。我们发现在最佳频率梯度上,准确编码发声组的能力存在空间分布。在最佳频率梯度的不同位置,不同的发声能得到最佳辨别。此外,在所有测试发声中,几个多单元簇组比仅一个多单元簇能产生更好的辨别效果。然而,单元之间的时间响应相关性并不能产生更好的辨别效果。我们的研究基于从几只豚鼠和电极插入位置同时记录的大量反应单元。我们的研究结果表明,在哺乳动物下丘中,行为相关发声存在广泛分布的编码。少数非相互作用单元的反应足以忠实地代表具有不同频谱时间特性的整个发声研究集合。