• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Classification of odorants across layers in locust olfactory pathway.蝗虫嗅觉通路上气味剂在各层间的分类
J Neurophysiol. 2016 May 1;115(5):2303-16. doi: 10.1152/jn.00921.2015. Epub 2016 Feb 10.
2
In-situ recording of ionic currents in projection neurons and Kenyon cells in the olfactory pathway of the honeybee.蜜蜂嗅觉通路中投射神经元和肯扬细胞离子电流的原位记录。
PLoS One. 2018 Jan 19;13(1):e0191425. doi: 10.1371/journal.pone.0191425. eCollection 2018.
3
Encoding of mixtures in a simple olfactory system.简单嗅觉系统中的混合物编码。
Neuron. 2013 Dec 4;80(5):1246-62. doi: 10.1016/j.neuron.2013.08.026. Epub 2013 Nov 7.
4
A neural network model of general olfactory coding in the insect antennal lobe.昆虫触角叶中一般嗅觉编码的神经网络模型。
Chem Senses. 1999 Aug;24(4):351-72. doi: 10.1093/chemse/24.4.351.
5
Intensity versus identity coding in an olfactory system.嗅觉系统中的强度与身份编码
Neuron. 2003 Sep 11;39(6):991-1004. doi: 10.1016/j.neuron.2003.08.011.
6
Olfactory coding in the honeybee lateral horn.蜜蜂侧角的嗅觉编码。
Curr Biol. 2014 Mar 3;24(5):561-7. doi: 10.1016/j.cub.2014.01.063. Epub 2014 Feb 20.
7
Multiple network properties overcome random connectivity to enable stereotypic sensory responses.多种网络特性克服了随机连接,从而实现了刻板的感觉反应。
Nat Commun. 2020 Feb 24;11(1):1023. doi: 10.1038/s41467-020-14836-6.
8
Relationship between afferent and central temporal patterns in the locust olfactory system.蝗虫嗅觉系统中传入与中枢时间模式之间的关系。
J Neurosci. 1999 Jan 1;19(1):381-90. doi: 10.1523/JNEUROSCI.19-01-00381.1999.
9
Effect of Circuit Structure on Odor Representation in the Insect Olfactory System.电路结构对昆虫嗅觉系统中气味表示的影响。
eNeuro. 2020 May 15;7(3). doi: 10.1523/ENEURO.0130-19.2020. Print 2020 May/Jun.
10
Learning classification in the olfactory system of insects.昆虫嗅觉系统中的学习分类
Neural Comput. 2004 Aug;16(8):1601-40. doi: 10.1162/089976604774201613.

引用本文的文献

1
Plasticity in inhibitory networks improves pattern separation in early olfactory processing.抑制性神经网络的可塑性可改善早期嗅觉处理中的模式分离。
Commun Biol. 2025 Apr 9;8(1):590. doi: 10.1038/s42003-025-07879-2.
2
Plasticity in inhibitory networks improves pattern separation in early olfactory processing.抑制性网络的可塑性改善了早期嗅觉处理中的模式分离。
bioRxiv. 2025 Feb 20:2024.01.24.576675. doi: 10.1101/2024.01.24.576675.
3
Gain modulation and odor concentration invariance in early olfactory networks.早期嗅觉网络中的增益调制和气味浓度不变性。
PLoS Comput Biol. 2023 Jun 21;19(6):e1011176. doi: 10.1371/journal.pcbi.1011176. eCollection 2023 Jun.
4
The functional logic of odor information processing in the Drosophila antennal lobe.果蝇触角叶中气味信息处理的功能逻辑。
PLoS Comput Biol. 2023 Apr 21;19(4):e1011043. doi: 10.1371/journal.pcbi.1011043. eCollection 2023 Apr.
5
Olfactory receptor neurons generate multiple response motifs, increasing coding space dimensionality.嗅觉受体神经元产生多种反应模式,增加了编码空间的维度。
Elife. 2023 Jan 31;12:e79152. doi: 10.7554/eLife.79152.
6
Disorder and the Neural Representation of Complex Odors.复杂气味的紊乱与神经表征
Front Comput Neurosci. 2022 Aug 8;16:917786. doi: 10.3389/fncom.2022.917786. eCollection 2022.
7
What the odor is not: Estimation by elimination.气味不是什么:通过排除法进行评估。
Phys Rev E. 2021 Aug;104(2-1):024415. doi: 10.1103/PhysRevE.104.024415.
8
Differential effects of adaptation on odor discrimination.适应对气味辨别能力的不同影响。
J Neurophysiol. 2018 Jul 1;120(1):171-185. doi: 10.1152/jn.00389.2017. Epub 2018 Mar 28.
9
Olfactory receptor neurons use gain control and complementary kinetics to encode intermittent odorant stimuli.嗅觉受体神经元利用增益控制和互补动力学来编码间歇性气味刺激。
Elife. 2017 Jun 28;6:e27670. doi: 10.7554/eLife.27670.

本文引用的文献

1
Circuit oscillations in odor perception and memory.气味感知与记忆中的神经回路振荡
Prog Brain Res. 2014;208:223-51. doi: 10.1016/B978-0-444-63350-7.00009-7.
2
A spatiotemporal coding mechanism for background-invariant odor recognition.一种用于背景不变的嗅觉识别的时空编码机制。
Nat Neurosci. 2013 Dec;16(12):1830-9. doi: 10.1038/nn.3570. Epub 2013 Nov 3.
3
The limits of deliberation in a perceptual decision task.在感知决策任务中的审议限制。
Neuron. 2013 Apr 24;78(2):339-51. doi: 10.1016/j.neuron.2013.02.010. Epub 2013 Mar 28.
4
Excitatory local interneurons enhance tuning of sensory information.兴奋性局部中间神经元增强感觉信息的调谐。
PLoS Comput Biol. 2012;8(7):e1002563. doi: 10.1371/journal.pcbi.1002563. Epub 2012 Jul 12.
5
Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity.嗅皮层中的气味表示:分布式率编码和去相关的群体活动。
Neuron. 2012 Jun 21;74(6):1087-98. doi: 10.1016/j.neuron.2012.04.021.
6
Functional analysis of a higher olfactory center, the lateral horn.高级嗅觉中枢——侧角的功能分析。
J Neurosci. 2012 Jun 13;32(24):8138-48. doi: 10.1523/JNEUROSCI.1066-12.2012.
7
Precise olfactory responses tile the sniff cycle.精确的嗅觉反应铺满嗅探周期。
Nat Neurosci. 2011 Jul 17;14(8):1039-44. doi: 10.1038/nn.2877.
8
Normalization for sparse encoding of odors by a wide-field interneuron.宽场中间神经元对气味稀疏编码的归一化。
Science. 2011 May 6;332(6030):721-5. doi: 10.1126/science.1201835.
9
Robust odor coding via inhalation-coupled transient activity in the mammalian olfactory bulb.通过哺乳动物嗅球中的吸入耦合并发瞬态活动进行稳健的气味编码。
Neuron. 2010 Nov 4;68(3):570-85. doi: 10.1016/j.neuron.2010.09.040.
10
Temporally diverse firing patterns in olfactory receptor neurons underlie spatiotemporal neural codes for odors.不同时间模式的嗅感受器神经元放电活动为气味的时空神经编码提供了基础。
J Neurosci. 2010 Feb 10;30(6):1994-2006. doi: 10.1523/JNEUROSCI.5639-09.2010.

蝗虫嗅觉通路上气味剂在各层间的分类

Classification of odorants across layers in locust olfactory pathway.

作者信息

Sanda Pavel, Kee Tiffany, Gupta Nitin, Stopfer Mark, Bazhenov Maxim

机构信息

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;

出版信息

J Neurophysiol. 2016 May 1;115(5):2303-16. doi: 10.1152/jn.00921.2015. Epub 2016 Feb 10.

DOI:10.1152/jn.00921.2015
PMID:26864765
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4922456/
Abstract

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.

摘要

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。