Early olfactory processing in Drosophila: mechanisms and principles

Abstract

In the olfactory system of Drosophila melanogaster, it is relatively straightforward to target in vivo measurements of neural activity to specific processing channels. This, together with the numerical simplicity of the Drosophila olfactory system, has produced rapid gains in our understanding of Drosophila olfaction. This review summarizes the neurophysiology of the first two layers of this system: the peripheral olfactory receptor neurons and their postsynaptic targets in the antennal lobe. We now understand in some detail the cellular and synaptic mechanisms that shape odor representations in these neurons. Together, these mechanisms imply that interesting neural adaptations to environmental statistics have occurred. These mechanisms also place some fundamental constraints on early sensory processing that pose challenges for higher brain regions. These findings suggest some general principles with broad relevance to early sensory processing in other modalities.

Figure 1

Anatomy of the Drosophila olfactory system. Olfactory receptor neuron (ORN) cell bodies and dendrites reside in peripheral olfactory organs. All of the ORNs that express a given odorant receptor converge onto the same glomerulus in the antennal lobe, schematized here as a single ORN per glomerulus. Each projection neuron (PN) sends a dendrite into a single glomerulus, where it receives monosynaptic input from ORNs. Although each glomerulus contains the dendrites of several PNs, only one PN for each glomerulus is shown here. Glomeruli are laterally interconnected by a network of local neurons (LNs), which interact with PNs, ORNs, and other LNs. Many individual LNs innervate most or all glomeruli, but some are more selective.

Figure 2

The nonlinear relationship between olfactory receptor neuron (ORN) and projection neuron (PN) firing rates. (a) Schematic tuning curves (i.e., a plot of firing rate versus stimulus number) for an ORN (dashed curve) and a PN (solid curve). Stimuli are arbitrarily ordered so that the strongest responses are in the center of the plot because this ordering makes it easier to visually assess tuning breadth. In this example, the ORN tuning curve is shown as Gaussian, although this may not be typical. The PN tuning curve was created by transforming the ORN tuning curve using a hyperbolic ratio function (like that in panel d). Tuning curves are normalized to the same peak. (b) A recording from a PN showing synaptic currents elicited by electrical stimulation of a train of spikes in ORN axons (arrowheads). The synaptic currents are depressed during the train. Modified from Kazama & Wilson (2008). (c) Schematic illustration of how total postsynaptic current increases sublinearly as presynaptic firing rates increase, owing to synaptic depression (as in panel b). Modified from Kazama & Wilson (2008). (d) Schematic showing the typical relationship between the odor-evoked firing rates of ORNs and PNs in the same glomerulus. Each black symbol represents a different odor stimulus; odor stimuli might be different concentrations of the same chemical or different chemicals. The relationship between ORN and PN firing rates is monotonic (as shown in this schematic) in a situation in which only one ORN type is activated by the odor. The relationship is strongly sublinear (arrow), probably due to the sublinear relationship between presynaptic spiking and postsynaptic current. Projecting these points into the x- and y-axes (blue symbols) makes it clear that most of the ORN responses cluster near the bottom of the cell’s dynamic range; this behavior is typical of ORNs. By contrast, PN responses are more uniformly distributed throughout the cell’s dynamic range. Modified from Bhandawat et al. (2007), Olsen et al., (2010). (e) Lateral inhibition (arrow) inhibits neurotransmitter release from ORNs and thereby increases the level of ORN input required to drive the PNs to saturation. The magnitude of lateral inhibition is correlated with total ORN activity, as is the activity of each ORN type; thus, a glomerulus tends to receive strong lateral inhibition when its ORN inputs are also strong. The distribution of ORN firing rates in this schematic has been shifted to the right to represent this idea, and this shift means that a shallower curve is needed to make the PN odor responses uniformly distributed within its dynamic range (compare blue symbols to panel d). In this schematic, the magnitude of lateral inhibition is the same for all the odor stimuli; however, in a situation where different stimuli elicit different levels of lateral inhibition, the relationship between ORN and PN activity would not be monotonic. (f) Lateral inhibition makes PNs more narrowly tuned than they otherwise would be, although it does not necessarily make PNs more narrowly tuned than ORNs.

Figure 3

Possible components of specificity in lateral inhibition. (a) All glomeruli may be mutually interconnected, as implied by the finding that many LNs innervate most or all glomeruli. Alternatively, some glomeruli might be interconnected in specific subnetworks. These subnetworks might be created by LNs with sparse innervation patterns or by electrical compartmentalization within the arbors of broadly innervating LNs. (b) Glomeruli may have varied sensitivity to LN activity, possibly reflecting heterogeneous levels of GABA receptor expression or heterogeneous release properties of LN arbors. Alternatively, all glomeruli might have similar levels of sensitivity to LN activity. Note that spatial inhomogeneity would create glomerulus-specific levels of inhibition, but these spatial patterns may or may not be odor specific.