Vickers N J, Christensen T A, Baker T C, Hildebrand J G
Arizona Research Laboratories Division of Neurobiology, The University of Arizona, PO Box 210077, Tucson, Arizona 85721, USA.
Nature. 2001 Mar 22;410(6827):466-70. doi: 10.1038/35068559.
The neural computations used to represent olfactory information in the brain have long been investigated. Recent studies in the insect antennal lobe suggest that precise temporal and/or spatial patterns of activity underlie the recognition and discrimination of different odours, and that these patterns may be strengthened by associative learning. It remains unknown, however, whether these activity patterns persist when odour intensity varies rapidly and unpredictably, as often occurs in nature. Here we show that with naturally intermittent odour stimulation, spike patterns recorded from moth antennal-lobe output neurons varied predictably with the fine-scale temporal dynamics and intensity of the odour. These data support the hypothesis that olfactory circuits compensate for contextual variations in the stimulus pattern with high temporal precision. The timing of output neuron activity is constantly modulated to reflect ongoing changes in stimulus intensity and dynamics that occur on a millisecond timescale.
长期以来，人们一直在研究大脑中用于表征嗅觉信息的神经计算。最近对昆虫触角叶的研究表明，精确的时间和/或空间活动模式是不同气味识别和区分的基础，并且这些模式可能通过联想学习得到加强。然而，当气味强度快速且不可预测地变化时（这在自然界中经常发生），这些活动模式是否持续存在仍是未知的。在这里，我们表明，在自然间歇性气味刺激下，从蛾类触角叶输出神经元记录的尖峰模式随气味的精细时间动态和强度而可预测地变化。这些数据支持了这样一种假设，即嗅觉回路以高时间精度补偿刺激模式中的背景变化。输出神经元活动的时间不断被调制，以反映在毫秒时间尺度上发生的刺激强度和动态的持续变化。
