Sanda Pavel, Kee Tiffany, Gupta Nitin, Stopfer Mark, Bazhenov Maxim
Department of Medicine, University of California, San Diego, California;
Department of Medicine, University of California, San Diego, California; Department of Cell Biology and Neuroscience, University of California, Riverside, California;
J Neurophysiol. 2016 May 1;115(5):2303-16. doi: 10.1152/jn.00921.2015. Epub 2016 Feb 10.
Olfactory processing takes place across multiple layers of neurons from the transduction of odorants in the periphery, to odor quality processing, learning, and decision making in higher olfactory structures. In insects, projection neurons (PNs) in the antennal lobe send odor information to the Kenyon cells (KCs) of the mushroom bodies and lateral horn neurons (LHNs). To examine the odor information content in different structures of the insect brain, antennal lobe, mushroom bodies and lateral horn, we designed a model of the olfactory network based on electrophysiological recordings made in vivo in the locust. We found that populations of all types (PNs, LHNs, and KCs) had lower odor classification error rates than individual cells of any given type. This improvement was quantitatively different from that observed using uniform populations of identical neurons compared with spatially structured population of neurons tuned to different odor features. This result, therefore, reflects an emergent network property. Odor classification improved with increasing stimulus duration: for similar odorants, KC and LHN ensembles reached optimal discrimination within the first 300-500 ms of the odor response. Performance improvement with time was much greater for a population of cells than for individual neurons. We conclude that, for PNs, LHNs, and KCs, ensemble responses are always much more informative than single-cell responses, despite the accumulation of noise along with odor information.
嗅觉处理过程涉及多层神经元,从外周气味分子的转导,到高级嗅觉结构中的气味质量处理、学习和决策。在昆虫中,触角叶中的投射神经元(PNs)将气味信息发送到蘑菇体的肯扬细胞(KCs)和侧角神经元(LHNs)。为了研究昆虫大脑不同结构(触角叶、蘑菇体和侧角)中的气味信息内容,我们基于在蝗虫体内进行的电生理记录设计了一个嗅觉网络模型。我们发现,所有类型(PNs、LHNs和KCs)的群体比任何给定类型的单个细胞具有更低的气味分类错误率。与调谐到不同气味特征的神经元空间结构化群体相比,这种改进在数量上与使用相同神经元的均匀群体所观察到的情况不同。因此,这一结果反映了一种涌现的网络特性。随着刺激持续时间的增加,气味分类得到改善:对于相似的气味分子,KC和LHN集合在气味反应的前300 - 500毫秒内达到最佳辨别能力。细胞群体随时间的性能提升比单个神经元大得多。我们得出结论,对于PNs、LHNs和KCs,尽管噪声与气味信息一起积累,但群体反应始终比单细胞反应提供更多信息。