I Want It All and I Want It Now: How a Neural Circuit Encodes Odor with Speed and Accuracy
Authors: Kevin M. Franks
Journal: Neuron (2015)

A study by Jeanne and Wilson, 2015Jeanne J.M. Wilson R.I. Neuron. 2015; 88 (this issue): 1014-1026Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar describes a circuit and determines the distinct neural circuit mechanisms that allow a signal to be represented with both speed and accuracy in the Drosophila olfactory system. A study by Jeanne and Wilson, 2015Jeanne J.M. Wilson R.I. Neuron. 2015; 88 (this issue): 1014-1026Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar describes a circuit and determines the distinct neural circuit mechanisms that allow a signal to be represented with both speed and accuracy in the Drosophila olfactory system. Ideally, decisions should be made as quickly and accurately as possible. However, important information is often noisy and limited, forcing a tradeoff between speed and accuracy. Neural circuit configurations that optimize processing with both speed and accuracy may therefore be deployed widely, forming canonical circuit motifs that are represented across phyla and brain systems. The neural circuits that rapidly and reliably evoke stereotyped behaviors may provide insight into how such motifs form and function. The Drosophila pheromone CVA evokes stereotyped, sex-specific mating behaviors. Previous work from the Dickson, Axel, and Jefferis groups and others has elucidated some of the neural circuits that underlie these behaviors (Kohl et al., 2015Kohl J. Huoviala P. Jefferis G.S. Curr. Opin. Neurobiol. 2015; 34: 149-157Crossref PubMed Scopus (47) Google Scholar). CVA is detected by ∼40 olfactory receptor neurons (ORNs) expressing the odorant receptor, OR67d. These neurons all project to the DA1 glomerulus in the antennal lobe, where they activate six projection neurons (PNs) that relay this information to specific targets in the lateral horn (LH), where many of these stereotyped behaviors are thought to be initiated (Figure 1A). What does it mean to “relay information,” and why would the brain do this when surely an ORN axon that can find its way with remarkable precision to a specific glomerulus could just as easily continue on to specific downstream targets in LH? The easy answer is that the information is “processed at the relay station.” The nature of this processing, and how it is implemented, has been rather more difficult to determine. Now, using technically challenging in vivo recordings and principles from signal detection theory, Jeanne and Wilson, 2015Jeanne J.M. Wilson R.I. Neuron. 2015; 88 (this issue): 1014-1026Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar determine how noisy and asynchronous signals originating in ORNs are transformed through multiple stages to produce LH output that is both fast and accurate. The authors expressed channelrhodopsin-2 in DA1 ORNs and activated these cells with long, low-intensity light pulses that typically evoked only 1 to 2 spikes per ORN, with spike times distributed throughout the 100-ms-long stimulus. These weak and asynchronous inputs evoked strong responses in DA1 PNs that occurred with a short latency after stimulus onset. How can PN responses occur much earlier, on average, than the ORN spikes that triggered them? The answer to this apparent paradox is that all 40 ORNs converge onto each PN, that PNs have relatively low spike thresholds, and that ORN-PN connections are quite strong. Responses in all PNs can therefore be evoked by input from a small subset of ORNs that fire earliest. However, this combination of many strong inputs and low spike thresholds also results in high levels of spontaneous spiking that degrades the accuracy of PN responses (Figure 1B). When the authors recorded from pairs of DA1 PNs—which is not trivial—they found that evoked responses in these sister PNs were highly correlated, especially at the onset of the stimulus. If downstream neurons receive convergent PN input, they might use this correlated PN spiking to improve signal accuracy while maintaining fast detection. Sister PNs project to similar regions in LH (Datta et al., 2008Datta S.R. Vasconcelos M.L. Ruta V. Luo S. Wong A. Demir E. Flores J. Balonze K. Dickson B.J. Axel R. Nature. 2008; 452: 473-477Crossref PubMed Scopus (265) Google Scholar, Kohl et al., 2013Kohl J. Ostrovsky A.D. Frechter S. Jefferis G.S. Cell. 2013; 155: 1610-1623Abstract Full Text Full Text PDF PubMed Scopus (127) Google Scholar, Ruta et al., 2010Ruta V. Datta S.R. Vasconcelos M.L. Freeland J. Looger L.L. Axel R. Nature. 2010; 468: 686-690Crossref PubMed Scopus (239) Google Scholar), but the connectivity of individual PNs onto LH neurons (LHNs) was not known. In another set of heroic experiments, the authors recorded from pairs of DA1 PNs and DC1 LHNs, a specific cluster of neurons in LH known to receive DA1 input. In all cases, they found monosynaptic connections between pairs, strongly suggesting that each DC1 LHN gets inputs from multiple, and possibly all, DA1 PNs. So, ORN inputs converge onto individual PNs, and these then diverge from each PN and reconverge onto multiple LHNs (Figure 1A). As in PNs, LHNs fired reliably and rapidly after stimulus onset. Unlike PNs, LHNs exhibited little spontaneous activity, and so responses in LHNs were easily resolved from background; they were fast and accurate (Figure 1B). This accuracy resulted from weak synaptic coupling between individual PNs and LHNs and high spike thresholds in LHNs. Interestingly, based on the strength of individual PN-LHN connections and spike threshold for spontaneous spiking, the authors estimated that each LHN requires about ten PN inputs to drive spiking, more than the total number of PNs. However, they also noticed that thresholds for evoked spikes were considerably lower than for spontaneous spikes. This occurs because the membrane depolarizes much more quickly following the stimulus with correlated inputs arriving from multiple PNs. This “dynamic threshold,” which is likely a consequence of sodium channel inactivation, acts to further suppress the impact of spontaneous and uncorrelated PN inputs, lowering background noise, and thus increasing the ability to reliably identify the evoked response. Taken together, this work provides a two-step neural circuit mechanism that first transforms a weak stimulus into a response that is fast and strong but also noisy, and then effectively high-pass filters this intermediate response with a coincidence detector that produces a response that is both fast and accurate. This transformation occurs by virtue of the properties of this convergent-divergent-reconvergent circuit. This circuit configuration could also be described as a three-layered feedforward network with all-to-all (or many-to-many) connections. In other words, this is a mini synfire chain. Synfire chains have been the subject of extensive theoretical study (Kumar et al., 2010Kumar A. Rotter S. Aertsen A. Nat. Rev. Neurosci. 2010; 11: 615-627Crossref PubMed Scopus (319) Google Scholar), but experimental evidence for them has been difficult to find. As the authors suggest, this may simply be due to the inability to fully map a neural pathway through multiple stages of processing. However, limitations of the synfire chain model suggest their actual implementation in neural circuits may be quite rare. For example, synfire chains can become explosive and have limited dynamic range (Vogels and Abbott, 2005Vogels T.P. Abbott L.F. J. Neurosci. 2005; 25: 10786-10795Crossref PubMed Scopus (310) Google Scholar). These issues may be less important in cases where the numbers of neurons are small and intensity doesn’t matter but instead speed and precision are essential. An example of a synfire chain may be the circuits controlling the timing of birdsong (Long et al., 2010Long M.A. Jin D.Z. Fee M.S. Nature. 2010; 468: 394-399Crossref PubMed Scopus (246) Google Scholar). It is tempting to speculate that the ORN-PN-LHN circuit dissected here may represent another instance of this type of circuit. It will therefore be interesting to see if the principles uncovered here apply more generally or if they are a special instance of this type of stimulus in this particular pathway. For example, this study used an artifactual stimulus that produced weak and desynchronized activity in ORNs. Stronger stimuli that may be more ethologically relevant will evoke faster, stronger ORN responses that would propagate quickly and reliably through the network, obviating many of the optimizations described here. Stronger stimuli would also recruit inhibition, which could further increase precision (Gabernet et al., 2005Gabernet L. Jadhav S.P. Feldman D.E. Carandini M. Scanziani M. Neuron. 2005; 48: 315-327Abstract Full Text Full Text PDF PubMed Scopus (440) Google Scholar). This study also examined a pathway that has evolved to specifically and selectively detect an innately salient olfactory pheromone. In fact, the DC1 cluster of LHNs is only one of multiple DA1 targets in LH and is only present in males, and therefore represents a pathway that has likely evolved to mediate a highly specific innate response to a highly specific and salient stimulus. So, where else might this convergence-divergence-reconvergence motif be implemented? Perhaps the most obvious place to look for this is the analogous mammalian olfactory circuitry. In mammals, all olfactory sensory neurons expressing a given odorant receptor converge on specific pairs of glomeruli in the olfactory bulb, where they activate ∼15 mitral (and tufted) cells that send this information to different olfactory cortical structures, including the piriform cortex and cortical amygdala. Projections from individual glomeruli to the cortical amygdala, which, like LH may mediate innate behavioral responses to specific stimuli (Root et al., 2014Root C.M. Denny C.A. Hen R. Axel R. Nature. 2014; 515: 269-273Crossref PubMed Scopus (180) Google Scholar), appear stereotyped and clustered (Miyamichi et al., 2011Miyamichi K. Amat F. Moussavi F. Wang C. Wickersham I. Wall N.R. Taniguchi H. Tasic B. Huang Z.J. He Z. et al.Nature. 2011; 472: 191-196Crossref PubMed Scopus (371) Google Scholar, Sosulski et al., 2011Sosulski D.L. Bloom M.L. Cutforth T. Axel R. Datta S.R. Nature. 2011; 472: 213-216Crossref PubMed Scopus (325) Google Scholar). This clustering may allow inputs from sister mitral cells to reconverge on the same postsynaptic targets, thereby rapidly and reliably activating these cells with relatively weak inputs from one or a few glomeruli specific for a salient odorant using neural circuit strategies analogous to what Jeanne and Wilson, 2015Jeanne J.M. Wilson R.I. Neuron. 2015; 88 (this issue): 1014-1026Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar describe here. However, projections from olfactory bulb to piriform cortex are diffuse and overlapping, making it unlikely that mitral cells from any one (technically, two) of the ∼2,000 glomeruli routinely reconverge onto individual cortical neurons. Rather, each piriform neuron likely receives sparse and mixed inputs from a few hundred different glomeruli (Davison and Ehlers, 2011Davison I.G. Ehlers M.D. Neuron. 2011; 70: 82-94Abstract Full Text Full Text PDF PubMed Scopus (121) Google Scholar), consistent with the idea that piriform cortex forms associative odor representations. In the fly, intriguingly, PNs that project to LH also project to the mushroom body, where odor learning occurs and which is likely analogous to mammalian piriform cortex. In fact, connections from PNs, including DA1 PNs, onto individual Kenyon cells in the mushroom body appear sparse and random, without over-representation from any one glomerulus (Caron et al., 2013Caron S.J. Ruta V. Abbott L.F. Axel R. Nature. 2013; 497: 113-117Crossref PubMed Scopus (240) Google Scholar). Thus, convergence-divergence-reconvergence may be a neural circuit motif that quickly and reliably propagates a single, specific signal that does not require modulation or much dynamic range through multiple stations. Meanwhile, other motifs, such as convergence-divergence, but not reconvergence, can support formation of associative representations by integrating different types of information using combinations of stronger connections in more sparsely connected circuits. In both fly and mammalian circuits, the very different types of computations performed in LH versus the mushroom body or cortical amydala versus piriform cortex are a direct consequence of the specific properties of the cells and synapses connecting one node of the circuit to the next. A central goal of neuroscience is to understand how the concerted activity of neurons at different stages of processing generates complex behaviors. Mapping pathways is very much en vogue nowadays, and the development of genetic and optogenetic tools has been invaluable in these efforts by allowing gain- and loss-of-function experiments at defined points along a circuit. However, many studies simply connect the nodes of a particular circuit and concede that information is processed at that node and then relayed to the next. However, just determining who is connected to whom will not tell us how a circuit works. As Jeanne and Wilson, 2015Jeanne J.M. Wilson R.I. Neuron. 2015; 88 (this issue): 1014-1026Abstract Full Text Full Text PDF PubMed Scopus (42) Google Scholar so elegantly demonstrate here, the specific physiological properties of the neurons and synapses that constitute these circuits specify the computations they perform and ultimately determines neural circuit function. Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and AccuracyJeanne et al.NeuronNovember 12, 2015In BriefConvergence and reconvergence are thought to be canonical circuit motifs. Jeanne and Wilson identify a three-layer feedforward network in Drosophila olfaction that computes progressively more informative single-neuron sensory representations by matching postsynaptic properties to the statistics of presynaptic inputs. Full-Text PDF Open Archive