Generating sparse and selective third-order responses in the olfactory system of the fly

Department of Neuroscience, Cellular Biophysics, Columbia University, New York, NY 10032, USA.
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 06/2010; 107(23):10713-8. DOI: 10.1073/pnas.1005635107
Source: PubMed


In the antennal lobe of Drosophila, information about odors is transferred from olfactory receptor neurons (ORNs) to projection neurons (PNs), which then send axons to neurons in the lateral horn of the protocerebrum (LHNs) and to Kenyon cells (KCs) in the mushroom body. The transformation from ORN to PN responses can be described by a normalization model similar to what has been used in modeling visually responsive neurons. We study the implications of this transformation for the generation of LHN and KC responses under the hypothesis that LHN responses are highly selective and therefore suitable for driving innate behaviors, whereas KCs provide a more general sparse representation of odors suitable for forming learned behavioral associations. Our results indicate that the transformation from ORN to PN firing rates in the antennal lobe equalizes the magnitudes of and decorrelates responses to different odors through feedforward nonlinearities and lateral suppression within the circuitry of the antennal lobe, and we study how these two components affect LHN and KC responses.

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    • "Their axons converge in glomeruli of the antennal lobes, and ~150 olfactory projection neurons per hemisphere convey the odor information from the antennal lobes to the lateral horn and the calyces of the mushroom bodies (Vosshall and Stocker, 2007). Here, the olfactory projection neurons synapse onto Kenyon cells, the intrinsic neurons of the mushroom body (Figure 1A), where odors are encoded as sparsely activated ensembles of Kenyon cells (Perez-Orive et al., 2002; Murthy et al., 2008; Turner et al., 2008; Luo et al., 2010; Honegger et al., 2011). Odor stimuli activate ensembles of about 5% out of the ~2500 Kenyon cells per hemisphere, independently from the concentration or chemical complexity of the odorant (Honegger et al., 2011), and those ensembles are non-stereotypical and variable across individuals (Murthy et al., 2008). "
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    ABSTRACT: Drosophila represents a model organism to analyze neuronal mechanisms underlying learning and memory. Kenyon cells of the Drosophila mushroom body are required for associative odor learning and memory retrieval. But is the mushroom body sufficient to acquire and retrieve an associative memory? To answer this question we have conceived an experimental approach to bypass olfactory sensory input and to thermogenetically activate sparse and random ensembles of Kenyon cells directly. We found that if the artifical activation of Kenyon cell ensembles coincides with a salient, aversive stimulus learning was induced. The animals adjusted their behavior in a subsequent test situation and actively avoided reactivation of these Kenyon cells. Our results show that Kenyon cell activity in coincidence with a salient aversive stimulus can suffice to form an associative memory. Memory retrieval is characterized by a closed feedback loop between a behavioral action and the reactivation of sparse ensembles of Kenyon cells.
    Frontiers in Behavioral Neuroscience 05/2014; 8:174. DOI:10.3389/fnbeh.2014.00174 · 3.27 Impact Factor
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    • "This is particularly important, since complex natural odors activate a large portion of the olfactory bulb with dense representation (Vincis et al., 2012). We postulate that under these conditions even a large network organization with canonical simplified neurons or abstract representations (Davison et al., 2003; Luo et al., 2010) or our previous models limited to 1D (Migliore et al., 2007, 2010; Migliore and Shepherd, 2008; McTavish et al., 2012; Yu et al., 2013), cannot solve the problem of understanding how odor discrimination is carried out efficiently by the actual neurons and their microcircuit connections. Using realistic 3D inputs, cell morphologies, and network connectivity, the model makes experimentally testable predictions on distributed processing of "
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    ABSTRACT: The functional consequences of the laminar organization observed in cortical systems cannot be easily studied using standard experimental techniques, abstract theoretical representations, or dimensionally reduced models built from scratch. To solve this problem we have developed a full implementation of an olfactory bulb microcircuit using realistic three-dimensional (3D) inputs, cell morphologies, and network connectivity. The results provide new insights into the relations between the functional properties of individual cells and the networks in which they are embedded. To our knowledge, this is the first model of the mitral-granule cell network to include a realistic representation of the experimentally-recorded complex spatial patterns elicited in the glomerular layer (GL) by natural odor stimulation. Although the olfactory bulb, due to its organization, has unique advantages with respect to other brain systems, the method is completely general, and can be integrated with more general approaches to other systems. The model makes experimentally testable predictions on distributed processing and on the differential backpropagation of somatic action potentials in each lateral dendrite following odor learning, providing a powerful 3D framework for investigating the functions of brain microcircuits.
    Frontiers in Computational Neuroscience 04/2014; 8(1):50. DOI:10.3389/fncom.2014.00050 · 2.20 Impact Factor
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    • "The r term and the exponents create the saturating property of the function. Based on physiological experiments, an exponent of 1.5 and a value of r = 12 was proposed (Luo et al., 2010; Olsen et al., 2010). For the visual example shown in Fig. 3, an exponent of 1.5 and r = 30 was used on pixel gray values from 0 to 255. "
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    ABSTRACT: Much progress has been made recently in understanding how olfactory coding works in insect brains. Here, I propose a wiring diagram for the major steps from the first processing network (the antennal lobe) to behavioral readout. I argue that the sequence of lateral inhibition in the antennal lobe, non-linear synapses, threshold-regulating gated spring network, selective lateral inhibitory networks across glomeruli, and feedforward inhibition to the lateral protocerebrum cover most of the experimental results from different research groups and model species. I propose that the main difference between mushroom bodies and the lateral protocerebrum is not about learned vs. innate behavior. Rather, mushroom bodies perform odor identification, whereas the lateral protocerebrum performs odor evaluation (both learned and innate). I discuss the concepts of labeled line and combinatorial coding and postulate that, under restrictive experimental conditions, these networks lead to an apparent existence of ‘labeled line’ coding for special odors. Modulatory networks are proposed as switches between different evaluating systems in the lateral protocerebrum. A review of experimental data and theoretical conjectures both contribute to this synthesis, creating new hypotheses for future research.
    European Journal of Neuroscience 04/2014; 39(11). DOI:10.1111/ejn.12558 · 3.18 Impact Factor
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