在对感官刺激做出反应时，大脑被认为会将这些刺激分成不相关联的认知类别，但其中所涉及的神经机制仍不清楚。J?rn Niessing 和 Rainer Friedrich通过利用双质子钙成像来监测曝露于各种不同浓度的一系列气味分子的斑马鱼嗅球中发射（激发）速度的变化情况，对这一现象进行了研究。
在有一系列逐渐变化的气味存在的情况下，当从一种气味向另一种切换时，神经发射（激发）模式会发生突变。气味浓度的变化几乎没有影响。这些结果与关于神经回路的离散状态的“Attractor”网络模型（所预测的情况）是一致的，这些模型也许可以延伸到其他感觉及认知过程。（生物谷Bioon.com）
生物谷推荐原文出处：
Nature doi:10.1038/nature08961
Olfactory pattern classification by discrete neuronal network states
J?rn Niessing& Rainer W. Friedrich
The categorial nature of sensory, cognitive and behavioural acts indicates that the brain classifies neuronal activity patterns into discrete representations. Pattern classification may be achieved by abrupt switching between discrete activity states of neuronal circuits, but few experimental studies have directly tested this. We gradually varied the concentration or molecular identity of odours and optically measured responses across output neurons of the olfactory bulb in zebrafish. Whereas population activity patterns were largely insensitive to changes in odour concentration, morphing of one odour into another resulted in abrupt transitions between odour representations. These transitions were mediated by coordinated response changes among small neuronal ensembles rather than by shifts in the global network state. The olfactory bulb therefore classifies odour-evoked input patterns into many discrete and defined output patterns, as proposed by attractor models. This computation is consistent with perceptual phenomena and may represent a general information processing strategy in the brain.
