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从脉冲发放角度看触角叶中的局部中间神经元和投射神经元。

Local interneurons and projection neurons in the antennal lobe from a spiking point of view.

机构信息

Neuroinformatik/Theoretical Neuroscience, Institute of Biology, Freie Universität Berlin, Berlin, Germany;

出版信息

J Neurophysiol. 2013 Nov;110(10):2465-74. doi: 10.1152/jn.00260.2013. Epub 2013 Sep 4.

Abstract

Local computation in microcircuits is an essential feature of distributed information processing in vertebrate and invertebrate brains. The insect antennal lobe represents a spatially confined local network that processes high-dimensional and redundant peripheral input to compute an efficient odor code. Social insects can rely on a particularly rich olfactory receptor repertoire, and they exhibit complex odor-guided behaviors. This corresponds with a high anatomical complexity of their antennal lobe network. In the honeybee, a large number of glomeruli that receive sensory input are interconnected by a dense network of local interneurons (LNs). Uniglomerular projection neurons (PNs) integrate sensory and recurrent local network input into an efficient spatio-temporal odor code. To investigate the specific computational roles of LNs and PNs, we measured several features of sub- and suprathreshold single-cell responses to in vivo odor stimulation. Using a semisupervised cluster analysis, we identified a combination of five characteristic features as sufficient to separate LNs and PNs from each other, independent of the applied odor-stimuli. The two clusters differed significantly in all these five features. PNs showed a higher spontaneous subthreshold activation, assumed higher peak response rates and a more regular spiking pattern. LNs reacted considerably faster to the onset of a stimulus, and their responses were more reliable across stimulus repetitions. We discuss possible mechanisms that can explain our results, and we interpret cell-type-specific characteristics with respect to their functional relevance.

摘要

微电路中的局部计算是脊椎动物和无脊椎动物大脑中分布式信息处理的一个基本特征。昆虫触角叶代表了一个空间受限的局部网络,它处理高维冗余的外围输入,以计算出有效的气味代码。社会性昆虫可以依赖于特别丰富的嗅觉受体库,并且它们表现出复杂的气味导向行为。这与它们触角叶网络的高度解剖复杂性相对应。在蜜蜂中,接收感觉输入的大量神经节由局部中间神经元(LNs)的密集网络相互连接。单神经节投射神经元(PNs)将感觉和递归局部网络输入整合到有效的时空气味代码中。为了研究 LNs 和 PNs 的特定计算作用,我们测量了对体内气味刺激的亚阈值和超阈值单细胞反应的几个特征。使用半监督聚类分析,我们确定了五种特征的组合足以将 LNs 和 PNs 彼此分离,而与应用的气味刺激无关。这两个簇在所有这五个特征上都有显著差异。PNs 显示出更高的自发亚阈值激活,假设更高的峰值反应率和更规则的脉冲模式。 LNs 对刺激的开始反应更快,并且它们的反应在刺激重复中更可靠。我们讨论了可以解释我们结果的可能机制,并根据它们的功能相关性解释细胞类型特异性特征。

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