<i>Drosophila</i>
            olfaction: past, present and future

Benton, Richard

doi:10.1098/rspb.2022.2054

Cited by 20 publications

(17 citation statements)

References 316 publications

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“…Much of the detailed knowledge of insect olfactory system development, anatomy, and neural function comes from studies of the vinegar fly Drosophila melanogaster . However, ants possess an order of magnitude more odorant receptor genes (ORs) and AL glomeruli than Drosophila (Mysore et al, 2009; Kelber et al, 2010; Smith et al, 2011; Zhou et al, 2012; Zhou et al, 2015; McKenzie et al, 2016; McKenzie and Kronauer, 2018; Ryba et al, 2020; Trible et al, 2017; Ferguson et al, 2021; Benton 2022). In Drosophila , the ~50 AL glomeruli each receive input from a functional class of OSNs and have stereotyped positions across individuals, which allowed the creation of atlases mapping odor-evoked response functions for each glomerulus (Stocker et al, 1990; Stocker, 1994; Gao et al, 2000; Vosshall et al, 2000; Wang et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Sparse and stereotyped encoding implicates a core glomerulus for ant alarm behavior

Hart

Frank

Lopes

et al. 2022

Preprint

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Ants communicate via large arrays of pheromones and possess expanded, highly complex olfactory systems, with antennal lobes in the brain comprising ~500 glomeruli. This expansion implies that odors could activate hundreds of glomeruli, which would pose challenges for higher order processing. To study this problem, we generated the first transgenic ants, expressing the genetically encoded calcium indicator GCaMP6s in olfactory sensory neurons. Using two-photon imaging, we mapped complete glomerular responses to four ant alarm pheromones. Alarm pheromones robustly activated ≤6 glomeruli, and activity maps for the three pheromones inducing panic-alarm in our study species converged on a single glomerulus. These results demonstrate that, rather than using broadly tuned combinatorial encoding, ants employ precise, narrowly tuned, and stereotyped representation of alarm pheromone cues. The identification of a central sensory hub glomerulus for alarm behavior suggests that a simple neural architecture is sufficient to translate pheromone perception into behavioral outputs.

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Section: Introductionmentioning

confidence: 99%

Sparse and stereotyped encoding implicates a core glomerulus for ant alarm behavior

Hart

Frank

Lopes

et al. 2022

Preprint

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“…The olfactory system in Drosophila consists of specialized olfactory sensory neurons (OSNs) located in the antennae and maxillary palps. These detect specific odorant molecules 69 and convey this information to the brain’s antennal lobe, where their signals are further processed 70 . We emulated peripheral olfaction by attaching virtual odor sensors to the antennae and maxillary palps of our biomechanical model (Fig.…”

Section: Resultsmentioning

confidence: 99%

NeuroMechFly v2, simulating embodied sensorimotor control in adultDrosophila

Wang-Chen,

Stimpfling,

Lam

et al. 2023

Preprint

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Discovering the principles underlying the neural and biomechanical control of animal behavior requires a tight dialogue between real experiments and data-driven neuromechanical models. Until now, such models have primarily been used to further our understanding of lower-level motor control. For most whole-animal simulations, we still lack an effective framework for studying how the brain processes environmental signals to regulate motor behavior. The adult fly, Drosophila melanogaster, is well-suited for data-driven modeling and can be simulated using the neuromechanical model, NeuroMechFly. However, until now this simulation framework did not permit the exploration of full hierarchical sensorimotor loops. Here we present NeuroMechFly 2.0, a framework that greatly expands whole-animal modeling of Drosophila by enabling visual and olfactory processing as well as complex three-dimensional environments that can be navigated using leg adhesion. To illustrate its capabilities we explore the effectiveness of biologically-inspired leg controllers for navigating diverse terrain, and show how one can build and use Reinforcement Learning to train an end-to-end hierarchical model with multimodal sensory processing, descending commands, and low-level motor control in closed loop. NeuroMechFly 2.0 can accelerate the discovery of explanatory models of the nervous system and the development of machine learning models to control autonomous artificial agents and robots.

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“…one olfactory receptor neuron type) [33,34], are read out in a more random way by downstream neurons in the mushroom bodies [35,36], a hallmark of a distributed population code [37,38]. Still, that the LC types form such a code is puzzling.…”

Section: Training a Deep Neural Network To Model Transformations From...mentioning

confidence: 99%

“…For example, multiple LC types have overlapping responses to similar stimuli [30, 31, 32], and optogenetically ac-tivating a single LC type can lead to different behaviors [11]. Even the striking anatomy of the LC neuron types— bundled into separate “channels” that make up the optic glomeruli—may be misleading, as olfactory glomeruli, whose anatomy closely resembles that of the optic glomeruli (e.g., each olfactory glomerulus receives input from one olfactory receptor neuron type) [33, 34], are read out in a more random way by downstream neurons in the mushroom bodies [35, 36], a hallmark of a distributed population code [37, 38]. Still, that the LC types form such a code is puzzling.…”

Section: Training a Deep Neural Network To Model Transformations From...mentioning

confidence: 99%

One-to-one mapping between deep network units and real neurons uncovers a visual population code for social behavior

Cowley

Calhoun

Rangarajan

et al. 2022

Preprint

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The rich variety of behaviors observed in animals arises through the complex interplay between sensory processing and motor control [1, 2, 3, 4, 5]. To understand these sensorimotor transformations, it is useful to build models that predict not only neural responses to sensory input [6, 7, 8, 9, 10] but also how each neuron causally contributes to behavior [11, 12]. Here we demonstrate a novel modeling approach to identify a one-to-one mapping between internal units in a deep neural network and real neurons by predicting the behavioral changes arising from systematic perturbations of more than a dozen neuron types. A key ingredient we introduce is “knockout training”, which involves perturbing the network during training to match the perturbations of the real neurons during behavioral experiments. We apply this approach to model the sensorimotor transformation of Drosophila melanogaster males during a complex, visually-guided social behavior [13, 14, 15, 16]. Contrary to prevailing views [17, 18, 19], our model suggests that visual projection neurons at the interface between the eye and brain form a distributed population code that collectively sculpts social behavior. Overall, our framework consolidates behavioral effects elicited from various neural perturbations into a single, unified model, providing a detailed map from stimulus to neuron to behavior.

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Drosophila olfaction: past, present and future

Cited by 20 publications

References 316 publications

Sparse and stereotyped encoding implicates a core glomerulus for ant alarm behavior

Sparse and stereotyped encoding implicates a core glomerulus for ant alarm behavior

NeuroMechFly v2, simulating embodied sensorimotor control in adultDrosophila

One-to-one mapping between deep network units and real neurons uncovers a visual population code for social behavior

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