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数据驱动的蜜蜂触角叶模型揭示了刺激起始异步如何有助于气味分离。

Data-driven honeybee antennal lobe model suggests how stimulus-onset asynchrony can aid odour segregation.

作者信息

Nowotny Thomas, Stierle Jacob S, Galizia C Giovanni, Szyszka Paul

机构信息

Centre for Computational Neuroscience and Robotics, School of Engineering and Informatics, University of Sussex, Falmer, Brighton BN1 9QJ, UK.

出版信息

Brain Res. 2013 Nov 6;1536:119-34. doi: 10.1016/j.brainres.2013.05.038. Epub 2013 Jun 4.

Abstract

Insects have a remarkable ability to identify and track odour sources in multi-odour backgrounds. Recent behavioural experiments show that this ability relies on detecting millisecond stimulus asynchronies between odourants that originate from different sources. Honeybees, Apis mellifera, are able to distinguish mixtures where both odourants arrive at the same time (synchronous mixtures) from those where odourant onsets are staggered (asynchronous mixtures) down to an onset delay of only 6ms. In this paper we explore this surprising ability in a model of the insects' primary olfactory brain area, the antennal lobe. We hypothesize that a winner-take-all inhibitory network of local neurons in the antennal lobe has a symmetry-breaking effect, such that the response pattern in projection neurons to an asynchronous mixture is different from the response pattern to the corresponding synchronous mixture for an extended period of time beyond the initial odourant onset where the two mixture conditions actually differ. The prolonged difference between response patterns to synchronous and asynchronous mixtures could facilitate odoursegregation in downstream circuits of the olfactory pathway. We present a detailed data-driven model of the bee antennal lobe that reproduces a large data set of experimentally observed physiological odour responses, successfully implements the hypothesised symmetry-breaking mechanism and so demonstrates that this mechanism is consistent with our current knowledge of the olfactory circuits in the bee brain. This article is part of a Special Issue entitled Neural Coding 2012.

摘要

昆虫具有在多种气味背景中识别和追踪气味源的非凡能力。最近的行为实验表明,这种能力依赖于检测来自不同源的气味剂之间毫秒级的刺激异步性。蜜蜂(Apis mellifera)能够区分两种气味剂同时到达的混合物(同步混合物)和气味剂起始时间错开的混合物(异步混合物),起始延迟低至仅6毫秒。在本文中,我们在昆虫的主要嗅觉脑区——触角叶的模型中探究这种惊人的能力。我们假设触角叶中局部神经元的胜者全得抑制网络具有对称破缺效应,使得投射神经元对异步混合物的反应模式在两种混合条件实际不同的初始气味剂起始时间之后的很长一段时间内,与对相应同步混合物的反应模式不同。对同步和异步混合物反应模式的长期差异可能有助于嗅觉通路下游回路中的气味分离。我们提出了一个详细的数据驱动的蜜蜂触角叶模型,该模型再现了大量实验观察到的生理气味反应数据集,成功实现了假设的对称破缺机制,从而证明该机制与我们目前对蜜蜂大脑嗅觉回路的认识是一致的。本文是名为《神经编码2012》特刊的一部分。

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