Frequency Transitions in Odor-Evoked Neural Oscillations

National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892, USA.
Neuron (Impact Factor: 15.05). 12/2009; 64(5):692-706. DOI: 10.1016/j.neuron.2009.10.004
Source: PubMed


In many species, sensory stimuli elicit the oscillatory synchronization of groups of neurons. What determines the properties of these oscillations? In the olfactory system of the moth, we found that odors elicited oscillatory synchronization through a neural mechanism like that described in locust and Drosophila. During responses to long odor pulses, oscillations suddenly slowed as net olfactory receptor neuron (ORN) output decreased; thus, stimulus intensity appeared to determine oscillation frequency. However, changing the concentration of the odor had little effect upon oscillatory frequency. Our recordings in vivo and computational models based on these results suggested that the main effect of increasing odor concentration was to recruit additional, less well-tuned ORNs whose firing rates were tightly constrained by adaptation and saturation. Thus, in the periphery, concentration is encoded mainly by the size of the responsive ORN population, and oscillation frequency is set by the adaptation and saturation of this response.

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Available from: Baranidharan Raman
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    • "If we assume typical conduction velocities of 20–25 cm/s as found in the honeybee brain (Oleskevich et al. 1997), simultaneously evoked action potentials along both pathways should arrive about synchronously at the medial MB calyx, but with a delay of *2 ms in the lateral MB calyx and a delay of *4 ms in the LH (Fig. 1c and model in Fig. 3). A model of two opposing delay lines appears attractive as various studies of temporal coding in insect olfactory systems have demonstrated synchronization in PN activities (Laurent et al. 1996; Wehr and Laurent 1996; Stopfer et al. 1997, 2003; Lei et al. 2002; Perez-Orive et al. 2002, 2004; Ito et al. 2009; Riffell et al. 2009a, b, 2013; Gupta and Stopfer 2012). Furthermore, the unique electrical properties of MB target neurons (Kenyon cells, KCs) as shown in the locust and cockroach suggest that synaptic input to KCs provides an ideal substrate for coincidence detection by KCs from synchronized input of PNs (Laurent 2002; Perez-Orive et al. 2002, 2004; Cassenaer and Laurent 2007; Demmer and Kloppenburg 2009; Tabuchi et al. 2012). "
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    ABSTRACT: Animals face highly complex and dynamic olfactory stimuli in their natural environments, which require fast and reliable olfactory processing. Parallel processing is a common principle of sensory systems supporting this task, for example in visual and auditory systems, but its role in olfaction remained unclear. Studies in the honeybee focused on a dual olfactory pathway. Two sets of projection neurons connect glomeruli in two antennal-lobe hemilobes via lateral and medial tracts in opposite sequence with the mushroom bodies and lateral horn. Comparative studies suggest that this dual-tract circuit represents a unique adaptation in Hymenoptera. Imaging studies indicate that glomeruli in both hemilobes receive redundant sensory input. Recent simultaneous multi-unit recordings from projection neurons of both tracts revealed widely overlapping response profiles strongly indicating parallel olfactory processing. Whereas lateral-tract neurons respond fast with broad (generalistic) profiles, medial-tract neurons are odorant specific and respond slower. In analogy to “what-” and “where” subsystems in visual pathways, this suggests two parallel olfactory subsystems providing “what-” (quality) and “when” (temporal) information. Temporal response properties may support across-tract coincidence coding in higher centers. Parallel olfactory processing likely enhances perception of complex odorant mixtures to decode the diverse and dynamic olfactory world of a social insect. Electronic supplementary material The online version of this article (doi:10.1007/s00359-013-0821-y) contains supplementary material, which is available to authorized users.
    Full-text · Article · Apr 2013 · Journal of Comparative Physiology
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    • "This evolution produces a progressive decrease in the overlap between the representations of chemically related odors (for example, several aromatic amino acids) (Friedrich and Laurent, 2001; Wilson et al., 2004) thereby increasing the ability of the system to discriminate between odors. Local field potentials (LFP) recordings from different insects including locust (Laurent and Davidowitz, 1994; Laurent et al., 1996; Wehr and Laurent, 1996), moth (Ito et al., 2009), honeybee (Stopfer et al., 1997) and fly (Tanaka et al., 2009) have revealed fast 20–40 Hz oscillations that persist in spite of large changes in both the identity and the concentration of an odor (Stopfer et al., 2003). Successive oscillatory cycles are constructed from a dynamically evolving constellation of PN spikes. "
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    ABSTRACT: In a variety of neuronal systems it has been hypothesized that inhibitory interneurons corral principal neurons into synchronously firing groups that encode sensory information and sub-serve behavior (Buzsáki and Chrobak, 1995; Buzsáki, 2008). This mechanism is particularly relevant to the olfactory system where spatiotemporal patterns of projection neuron (PN) activity act as robust markers of odor attributes (Laurent et al., 1996; Wehr and Laurent, 1996). In the insect antennal lobe (AL), a network of local inhibitory interneurons arborizes extensively throughout the AL (Leitch and Laurent, 1996) providing inhibitory input to the cholinergic PNs. Our theoretical work has attempted to elaborate the exact role of inhibition in the generation of odor specific PN responses (Bazhenov et al., 2001a,b; Assisi et al., 2011). In large-scale AL network models we characterized the inhibitory sub-network by its coloring (Assisi et al., 2011) and showed that it can entrain excitatory PNs to the odor specific patterns of transient synchronization. In this focused review, we further examine the dynamics of entrainment in more detail by simulating simple model networks in various parameter regimes. Our simulations in conjunction with earlier studies point to the key role played by lateral (between inhibitory interneurons) and feedback (from inhibitory interneurons to principal cells) inhibition in the generation of experimentally observed patterns of transient synchrony.
    Full-text · Article · Apr 2012 · Frontiers in Neuroengineering
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    • "In animals where a precision analysis of the convergence of ORNs to PNs is possible, it has been shown that ORNs converge by receptor type onto PNs (Mombaerts et al., 1996; Vosshall et al., 2000). Over a range of concentrations, any given odorant can activate a diverse assortment of ORNs (Hallem and Carlson, 2006; Ito et al., 2009). Therefore, PNs can respond to and carry information about multiple odorants (Stopfer et al., 2003; Mazor and Laurent, 2005; Wilson et al. 2004) so that information about odorants is broadly distributed across many PNs. "
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    ABSTRACT: Noisy or spontaneous activity is common in neural systems and poses a challenge to detecting and discriminating signals. Here we use the locust to answer fundamental questions about noise in the olfactory system: Where does spontaneous activity originate? How is this activity propagated or reduced throughout multiple stages of neural processing? What mechanisms favor the detection of signals despite the presence of spontaneous activity? We found that spontaneous activity long observed in the secondary projection neurons (PNs) originates almost entirely from the primary olfactory receptor neurons (ORNs) rather than from spontaneous circuit interactions in the antennal lobe, and that spontaneous activity in ORNs tonically depolarizes the resting membrane potentials of their target PNs and local neurons (LNs) and indirectly tonically depolarizes tertiary Kenyon cells (KCs). However, because these neurons have different response thresholds, in the absence of odor stimulation, ORNs and PNs display a high spontaneous firing rate but KCs are nearly silent. Finally, we used a simulation of the olfactory network to show that discrimination of signal and noise in the KCs is best when threshold levels are set so that baseline activity in PNs persists. Our results show how the olfactory system benefits from making a signal detection decision after a point of maximal information convergence, e.g., after KCs pool inputs from many PNs.
    Full-text · Article · Feb 2012 · The Journal of Neuroscience : The Official Journal of the Society for Neuroscience
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