在對感官刺激做出反應(yīng)時,，大腦被認(rèn)為會將這些刺激分成不相關(guān)聯(lián)的認(rèn)知類別,，但其中所涉及的神經(jīng)機制仍不清楚,。J?rn Niessing 和 Rainer Friedrich通過利用雙質(zhì)子鈣成像來監(jiān)測曝露于各種不同濃度的一系列氣味分子的斑馬魚嗅球中發(fā)射（激發(fā)）速度的變化情況,，對這一現(xiàn)象進行了研究,。
在有一系列逐漸變化的氣味存在的情況下,，當(dāng)從一種氣味向另一種切換時,，神經(jīng)發(fā)射（激發(fā)）模式會發(fā)生突變。氣味濃度的變化幾乎沒有影響,。這些結(jié)果與關(guān)于神經(jīng)回路的離散狀態(tài)的“Attractor”網(wǎng)絡(luò)模型（所預(yù)測的情況）是一致的,，這些模型也許可以延伸到其他感覺及認(rèn)知過程。（生物谷Bioon.com）
生物谷推薦原文出處：
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.
