Olfactory pattern classification by discrete neuronal network states

Abstract

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.