Gu Yuqiao, Liljenström Hans
Department of Biometry and Engineering, P.O. Box 7032, SLU, S-75007 Uppsala, Sweden.
Biosystems. 2007 May-Jun;89(1-3):236-43. doi: 10.1016/j.biosystems.2006.04.021. Epub 2006 Nov 15.
The olfactory system of insects is essential for the search of food and mates, and weak signals can be detected, amplified and discriminated in a fluctuating environment. The olfactory system also allows for learning and recall of odour memories. Based on anatomical, physiological, and behavioural data from the olfactory system of insects, we have developed a cross-scale dynamical neural network model to simulate the presentation, amplification and discrimination of host plant odours and sex pheromones. In particular, we model how the spatial and temporal patterns of the odour information emerging in the glomeruli of the antennal lobe (AL) rely on the glomerular morphology, the connectivity and the complex dynamics of the AL circuits. We study how weak signals can be amplified, how different odours can be discriminated, based on stochastic (resonance) dynamics and the connectivity of the network. We further investigate the spatial and temporal coding of sex pheromone components and plant volatile compounds, in relation to the glomerular structure, arborizing patterns of the projection neurons (PNs) and timing patterns of the neuronal spiking activity.
昆虫的嗅觉系统对于寻找食物和配偶至关重要，并且能够在波动的环境中检测、放大和区分微弱信号。嗅觉系统还支持气味记忆的学习和回忆。基于昆虫嗅觉系统的解剖学、生理学和行为学数据，我们开发了一个跨尺度动态神经网络模型，以模拟寄主植物气味和性信息素的呈现、放大和区分。特别是，我们模拟了触角叶（AL）肾小球中出现的气味信息的时空模式如何依赖于肾小球形态、连接性以及AL回路的复杂动力学。我们研究基于随机（共振）动力学和网络连接性，微弱信号如何被放大，不同气味如何被区分。我们进一步研究性信息素成分和植物挥发性化合物的时空编码，以及与肾小球结构、投射神经元（PNs）的分支模式和神经元放电活动的时间模式的关系。
