Olfactory pattern classification by discrete neuronal network states
Olfactory pattern classification by discrete neuronal network states
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DOI：
10.1038/nature08961
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发表时间：
2010-05-06
期刊：
影响因子：
64.8
通讯作者：
Friedrich, Rainer W.
中科院分区：
文献类型：
--
作者：
Niessing, Joern;Friedrich, Rainer W.
感觉、认知和行为行为的分类性质表明，大脑将神经元活动模式分类为离散的表征。模式分类可以通过神经元回路离散活动状态之间的突然切换来实现，但很少有实验研究直接对此进行测试。我们逐渐改变气味的浓度或分子特征，并在斑马鱼嗅球输出神经元上测量光学反应。尽管种群活动模式对气味浓度的变化不敏感，但一种气味转变为另一种气味会导致气味表征之间的突然转变。这些转变是由小神经元群之间的协调响应变化介导的，而不是由全局网络状态的变化介导的。因此，嗅球将气味诱发的输入模式分类为许多离散的和定义的输出模式，正如吸引子模型所提出的那样。这种计算与感知现象是一致的，可能代表了大脑中一般的信息处理策略。
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.