In both insect and vertebrate olfactory systems only two synapses separate the sensory periphery from brain areas required for memory formation and the organisation of behaviour. In the Drosophila olfactory system, which is anatomically very similar to its vertebrate counterpart, there has been substantial recent progress in understanding the flow of information from experiments using molecular genetic, electrophysiological and optical imaging techniques. In this review, we shall focus on how olfactory information is processed and transformed in order to extract behaviourally relevant information. We follow the progress from olfactory receptor neurons, through the first processing area, the antennal lobe, to higher olfactory centres. We address both the underlying anatomy and mechanisms that govern the transformation of neural activity. We emphasise our emerging understanding of how different elementary computations, including signal averaging, gain control, decorrelation and integration, may be mapped onto different circuit elements.

Learning and memory has been studied extensively in Drosophila using behavioral, molecular, and genetic approaches. These studies have identified the mushroom body as essential for the formation and retrieval of olfactory memories. We investigated odor responses of the principal neurons of the mushroom body, the Kenyon cells (KCs), in Drosophila using whole cell recordings in vivo. KC responses to odors were highly selective and, thus sparse, compared with those of their direct inputs, the antennal lobe projection neurons (PNs). We examined the mechanisms that might underlie this transformation and identified at least three contributing factors: excitatory synaptic potentials (from PNs) decay rapidly, curtailing temporal integration, PN convergence onto individual KCs is low ( approximately 10 PNs per KC on average), and KC firing thresholds are high. Sparse activity is thought to be useful in structures involved in memory in part because sparseness tends to reduce representation overlaps. By comparing activity patterns evoked by the same odors across olfactory receptor neurons and across KCs, we show that representations of different odors do indeed become less correlated as they progress through the olfactory system.

Molecular genetics has revealed a precise stereotypy in the projection of primary olfactory sensory neurons onto secondary neurons. A major challenge is to understand how this mapping translates into odor responses in these second-order neurons. We investigated this question in Drosophila using whole-cell recordings in vivo. We observe that monomolecular odors generally elicit responses in large ensembles of antennal lobe neurons. Comparison of odor-evoked activity from afferents and postsynaptic neurons in the same glomerulus revealed that second-order neurons display broader tuning and more complex responses than their primary afferents. This indicates a major transformation of odor representations, implicating lateral interactions within the antennal lobe.

In the insect olfactory system, oscillatory synchronization is functionally relevant and reflects the coherent activation of dynamic neural assemblies. We examined the role of such oscillatory synchronization in information transfer between networks in this system. The antennal lobe is the obligatory relay for olfactory afferent signals and generates oscillatory output. The mushroom body is responsible for formation and retrieval of olfactory and other memories. The format of odor representations differs significantly across these structures. Whereas representations are dense, dynamic, and seemingly redundant in the antennal lobe, they are sparse and carried by more selective neurons in the mushroom body. This transformation relies on a combination of oscillatory dynamics and intrinsic and circuit properties that act together to selectively filter and synthesize the output from the antennal lobe. These results provide direct support for the functional relevance of correlation codes and shed some light on the role of oscillatory synchronization in sensory networks.