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一个大规模的蝗虫触角叶模型。

A large-scale model of the locust antennal lobe.

作者信息

Patel Mainak, Rangan Aaditya V, Cai David

机构信息

The Sackler Institute of Graduate Biomedical Sciences, NYU School of Medicine, New York, NY 10016, USA.

出版信息

J Comput Neurosci. 2009 Dec;27(3):553-67. doi: 10.1007/s10827-009-0169-z. Epub 2009 Jun 23.

Abstract

The antennal lobe (AL) is the primary structure within the locust's brain that receives information from olfactory receptor neurons (ORNs) within the antennae. Different odors activate distinct subsets of ORNs, implying that neuronal signals at the level of the antennae encode odors combinatorially. Within the AL, however, different odors produce signals with long-lasting dynamic transients carried by overlapping neural ensembles, suggesting a more complex coding scheme. In this work we use a large-scale point neuron model of the locust AL to investigate this shift in stimulus encoding and potential consequences for odor discrimination. Consistent with experiment, our model produces stimulus-sensitive, dynamically evolving populations of active AL neurons. Our model relies critically on the persistence time-scale associated with ORN input to the AL, sparse connectivity among projection neurons, and a synaptic slow inhibitory mechanism. Collectively, these architectural features can generate network odor representations of considerably higher dimension than would be generated by a direct feed-forward representation of stimulus space.

摘要

触角叶(AL)是蝗虫大脑中的主要结构,它接收来自触角内嗅觉受体神经元(ORN)的信息。不同的气味激活不同的ORN子集,这意味着触角水平的神经元信号以组合方式编码气味。然而,在触角叶内,不同的气味会产生由重叠神经群体携带的具有持久动态瞬变的信号,这表明存在更复杂的编码方案。在这项工作中,我们使用蝗虫触角叶的大规模点神经元模型来研究这种刺激编码的转变及其对气味辨别可能产生的影响。与实验一致,我们的模型产生了对刺激敏感、动态演化的活跃触角叶神经元群体。我们的模型关键依赖于与ORN输入到触角叶相关的持续时间尺度、投射神经元之间的稀疏连接以及一种突触慢抑制机制。总体而言,这些结构特征能够生成维度比刺激空间的直接前馈表示所产生的维度高得多的网络气味表征。

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