Daniel P C, Burgess M F, Derby C D
Department of Biology, Hofstra University, Hempstead, NY 11550-1090, USA.
J Comp Physiol A. 1996 Apr;178(4):523-36. doi: 10.1007/BF00190182.
Coding of binary mixtures by a population of olfactory receptor neurons in the spiny lobster (Panulirus argus) was examined. Extracellular single-unit responses of 50 neurons to seven compounds and their binary mixtures were recorded. The ability of a noncompetitive model with correction for binding inhibition to predict responses to mixtures based on responses to their components was compared with the predictive abilities of other models. This model assumes that different compounds activate different transduction processes in the same neuron leading to excitation or inhibition, and it includes a term quantifying the degree to which binding of an odorant to its receptor sites is inhibited by other compounds. The model accurately predicted the absolute response magnitude of the population of neurons for 13 of 15 mixtures assessed, which is superior to the predictive power of any of the other models. The model also accurately predicted the across neuron patterns generated by the binary mixtures, as evaluated by multidimensional scaling analysis. The results suggest that there is no emergence of unique qualities for binary mixtures relative to components of these mixtures.
研究了多刺龙虾（Panulirus argus）中一群嗅觉受体神经元对二元混合物的编码。记录了50个神经元对七种化合物及其二元混合物的细胞外单单位反应。将一种具有结合抑制校正的非竞争性模型基于对混合物成分的反应来预测对混合物反应的能力与其他模型的预测能力进行了比较。该模型假设不同的化合物在同一神经元中激活不同的转导过程，导致兴奋或抑制，并且它包括一个量化一种气味剂与其受体位点的结合被其他化合物抑制程度的项。该模型准确预测了所评估的15种混合物中13种混合物的神经元群体的绝对反应幅度，这优于任何其他模型的预测能力。通过多维标度分析评估，该模型还准确预测了二元混合物产生的跨神经元模式。结果表明，相对于这些混合物的成分，二元混合物没有出现独特的性质。
