Origins of Cell-Type-Specific Olfactory Processing in the Drosophila Mushroom Body Circuit

Inada, Kengo; Tsuchimoto, Yoshiko; Kazama, Hokto

doi:10.1016/j.neuron.2017.06.039

Cited by 60 publications

(77 citation statements)

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“…that predicts that the average Kenyon cell inhibits itself more than it inhibits other 532 individual Kenyon cells. This prediction is supported by previous experimental results 533 that some Kenyon cells can inhibit some Kenyon cells more than others, and that an 534 individual Kenyon cell can inhibit itself (Inada et al, 2017). Our model goes beyond 535 these results in predicting that the average Kenyon cell actually preferentially inhibits 536 itself ( Fig.…”

Section: Apl Inhibit Kenyon Cells Where It Itself Is Not Active? Widesupporting

confidence: 76%

“…We first asked whether physiological responses to sensory stimuli are spatially 97 localized within APL. At very low odor concentrations, odor responses in APL can be 98 restricted to one lobe (Inada et al, 2017), but it remains unclear how sensory-evoked 99 activity is structured across APL at larger scales. We examined this question using 100 electric shock, a typical 'punishment' used for olfactory aversive conditioning.…”

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confidence: 99%

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Localized inhibition in theDrosophilamushroom body

Amin

Suárez-Grimalt

Vrontou

et al. 2020

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15Many neurons show compartmentalized activity, in which activity does not spread 16 readily across the cell, allowing input and output to occur locally. However, the 17 functional implications of compartmentalized activity for the wider neural circuit are 18 often unclear. We addressed this problem in the Drosophila mushroom body, whose 19 principal neurons, Kenyon cells, receive feedback inhibition from a large, non-spiking 20 interneuron called APL. We used local stimulation and volumetric calcium imaging to 21show that APL inhibits Kenyon cells in both their dendrites and axons, and that both 22 activity in APL and APL's inhibitory effect on Kenyon cells are spatially localized, 23 allowing APL to differentially inhibit different mushroom body compartments. 24Applying these results to the Drosophila hemibrain connectome predicts that 25 individual Kenyon cells inhibit themselves via APL more strongly than they inhibit 26 other individual Kenyon cells. These findings reveal how cellular physiology and 27 detailed network anatomy can combine to influence circuit function. 28 29 effect on Kenyon cells, or how APL's physiology would affect the structure of 81 feedback inhibition in the mushroom body. 82 83We addressed these questions by volumetric calcium imaging in APL. By quantifying 84 the spatial attenuation of the effects of locally stimulating APL, we found that both 85 activity in APL and APL's inhibitory effect on Kenyon cells are spatially restricted, 86 allowing APL to differentially inhibit different compartments of the mushroom body. 87Combining these physiological findings with recent connectomic data predicts that 88 individual Kenyon cells inhibit themselves via APL more strongly than they do other 89 Kenyon cells. Our findings establish APL as a model system for studying local 90 computations and their role in learning and memory. 91 92Results 93 94Spatially restricted responses to electric shock in APL 95 96We first asked whether physiological responses to sensory stimuli are spatially 97 localized within APL. At very low odor concentrations, odor responses in APL can be 98 restricted to one lobe (Inada et al., 2017), but it remains unclear how sensory-evoked 99 activity is structured across APL at larger scales. We examined this question using 100 electric shock, a typical 'punishment' used for olfactory aversive conditioning. 101 102 APL responds to electric shock (Liu and Davis, 2008;, but it is 103 unknown if these responses are spatially restricted. To test this, we subjected flies to 104 electric shock and recorded activity volumetrically throughout the APL lobes using 105

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Section: Apl Inhibit Kenyon Cells Where It Itself Is Not Active? Widesupporting

confidence: 76%

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confidence: 99%

Localized inhibition in theDrosophilamushroom body

Amin

Suárez-Grimalt

Vrontou

et al. 2020

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“…To understand this issue, it would necessary to identify the basic components of the decoder: (i) convergence of input from multiple PNs onto downstream neurons, (ii) linear combination of the inputs, and (iii) a detection threshold that does not require all input neurons to be simultaneously co-active. Existing anatomical and functional studies have shown that downstream Kenyon cells in the mushroom body linearly combine inputs from multiple projection neurons 43,44 . Further photostimulation of Kenyon cell dendritic claws in fruit flies have revealed that activating more than half of these input regions is sufficient for driving these cells to spike 45 (i.e.…”

Section: Discussionmentioning

confidence: 99%

Dynamic contrast enhancement and flexible odor codes

Nizampatnam

Saha

Chandak

et al. 2017

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Sensory stimuli evoke spiking activities patterned across neurons and time that are hypothesized to encode information about their identity. Since the same stimulus can be encountered in a multitude of ways, how stable or flexible are these stimulus-evoked responses? Here, we examined this issue in the locust olfactory system. In the antennal lobe, we found that both spatial and temporal features of odor-evoked responses varied in a stimulus-history dependent manner. The response variations were not random, but allowed the antennal lobe circuit to enhance the uniqueness of the current stimulus. Nevertheless, information about the odorant identity became confounded due to this contrast-enhancement computation. Notably, a linear logical classifier (OR-of-ANDs) that can decode information distributed in flexible subsets of neurons generated predictions that matched results from our behavioral experiments. In sum, our results reveal a simple computational logic for achieving the stability vs. flexibility tradeoff in sensory coding. INTRODUCTIONThe key task of a sensory system is to transduce and represent information about environmental cues as electrical neural activities so that the organism may generate an appropriate behavioral response. The precise format in which neural activities represents stimulus specific information i.e. 'the neural code' has been a topic of great debate in neuroscience

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“…Second, lateral inhibition mediated by the LNs in the AL (Wilson, 2013) facilitates decorrelation of odor representations (Wilson and Laurent, 2005;Schmuker et al, 2011;Wilson, 2013;Campbell et al, 2013) and contributes to population sparseness (Luo et al, 2010;Betkiewicz et al, 2020). The sparse code in the KC population has been shown to reduce the overlap between different odor representations (Luo et al, 2010;Lin et al, 2014;Inada et al, 2017) and consequently population sparseness is an important property of olfactory learning and plasticity models in insects (Huerta et al, 2004;Huerta and Nowotny, 2009;Wessnitzer et al, 2012;Ardin et al, 2016;Peng and Chittka, 2017;Müller et al, 2018).…”

Section: Sparse Coding In Space and Timementioning

confidence: 99%

“…(Aso et al, 2014a;Caron et al, 2013;Xu et al, 2020)) and neurophysiology (e.g. (Ito et al, 2008;Kazama and Wilson, 2009;Demmer and Kloppenburg, 2009;Szyszka et al, 2014;Inada et al, 2017;Egea-Weiss et al, 2018)) of insect olfaction and basic computational features (Litwin-Kumar et al, 2017;Kloppenburg and Nawrot, 2014;Betkiewicz et al, 2020). We follow the idea of compositionality, a widely used concept in mathematics, semantics and linguistics.…”

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confidence: 99%

Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects

Rapp

Nawrot

2020

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Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying nervous systems and abstract computational principles we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning and motor control. We focus on cast & surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a synaptic plasticity rule the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. Without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short timescales generates cast & surge motor commands. Our systems approach is generic and predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. IntroductionNavigating towards a food source during foraging requires dynamical sensory processing, accumulation of sensory evidence and appropriate high level motor control. Navigation based on an animals' olfactory sense is a challenging task due to the complex spatiotemporal landscape of odor molecules. A core aspect of foraging is the acquisition of sensory cue samples in the natural environment where odor concentrations vary rapidly and steeply across space. Experimental 5 access to the neural substrate is challenging in freely behaving insects. Biologically realistic models thus play a key role in investigating the relevant computational mechanisms. Consequently, recent efforts at understanding foraging behavior have focused on identifying viable computational strategies for making navigation decisions (Vergassola et al.

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Origins of Cell-Type-Specific Olfactory Processing in the Drosophila Mushroom Body Circuit

Cited by 60 publications

References 70 publications

Localized inhibition in theDrosophilamushroom body

Localized inhibition in theDrosophilamushroom body

Dynamic contrast enhancement and flexible odor codes

Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects

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