Structured sampling of olfactory input by the fly mushroom body

Zheng, Zhihao; Li, Feng; Fisher, Corey B.; Ali, Iqbal J.; Sharifi, Nadiya; Calle-Schuler, Steven A.; Hsu, Joseph; Masoodpanah, Najla; Kmecova, Lucia; Kazimiers, Tom; Perlman, Eric; Nichols, Matthew; Li, Peter H.; Jain, Viren; Bock, Davi D.

doi:10.1016/j.cub.2022.06.031

Cited by 36 publications

(56 citation statements)

References 115 publications

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“…These results are consistent with an activation of 50 KCs being sufficient to induce changes in the firing frequency of MBON-α3 (Figure 1F). The fact that all simulations resulted in a significant depolarization consistent with action potential generation or alterations of the firing frequency together with the small range of variation within this dataset, provides support for the current model that odor encoding in KCs is, at least to a large extent, random (stochastic) (Caron et al, 2013;Zheng et al, 2022;Li et al, 2020;Eichler et al, 2017). Even though the integrated synaptic conductance was changed by the same amount by our manipulation of either synaptic strength or number of activated KCs, we observed small but significant differences in the resultant mean somatic amplitudes (Figure 5F,G).…”

Section: Physiological and Tuned Activation Of Mbon-α3supporting

confidence: 68%

“…PNs are connected in a largely stochastic manner to the approximately 2000 KCs per brain hemisphere, resulting in a unique KC odor representation in individual flies (Litwin-Kumar et al, 2017;Aso et al, 2014a;Caron et al, 2013;Gruntman and Turner, 2013;Lin et al, 2007;Betkiewicz et al, 2020). The exception to this is a partial non-random connectivity of a population of food-responsive PNs (Zheng et al, 2022). The formation of olfactory memories within the MB circuit is assayed through experimental manipulations in which normally neutral odors are associated with either positive (reward learning) or negative (avoidance learning) valences (Livingstone et al, 1984).…”

Section: Introductionmentioning

confidence: 99%

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The cellular architecture of memory modules inDrosophilasupports stochastic input integration

Hafez

Escribano

Ziegler

et al. 2020

Preprint

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The ability to associate neutral stimuli with either positive or negative valence forms the basis for most forms of decision making. Long-term memory formation then enables manifestation of these associations to guide behavioral responses over prolonged periods of time. Despite recent advances in the understanding of the neuronal circuits and cellular mechanisms controlling memory formation, the computational principles at the level of individual information processing modules remain largely unknown. Here we use the Drosophila mushroom body (MB), the learning and memory center of the fly, as a model system to elucidate the cellular basis of memory computation. Recent studies resolved the precise synaptic connectome of the MB and identified the synaptic connections between Kenyon cells (KCs) and mushroom body output neurons (MBONs) as the sites of sensory association. We build a realistic computational model of the MBON-α3 neuron including precise synaptic connectivity to the 948 upstream KCs innervating the αβ MB lobes. To model membrane properties reflecting in vivo parameters we performed patch-clamp recording of MBON-α3. Based on the in vivo data we model synaptic input of individual cholinergic KC-MBON synapses by local conductance changes at the dendritic sections defined by the electron microscopic reconstruction. Modelling of activation of all individual synapses confirms prior results demonstrating that MBON-α3 is electrotonically compact. As a likely consequence of this compactness, activation pattern of individual KCs with identical numbers of synaptic connection but innervating different sections of the MBON-α3 dendritic tree result in highly similar depolarization voltages. Furthermore, we show that KC input patterns reflecting physiological activation by individual odors in vivo are sufficient to robustly drive MBON spiking. Our data suggest that the sparse innervation by KCs can control or modulate MBON activity in an efficient manner, with minimal requirements on the specificity of synaptic localization. This KC-MBON architecture therefore provides a suitable module to incorporate different olfactory associative memories based on stochastically encoded odor-specificity of KCs.

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Section: Physiological and Tuned Activation Of Mbon-α3supporting

confidence: 68%

Section: Introductionmentioning

confidence: 99%

The cellular architecture of memory modules inDrosophilasupports stochastic input integration

Hafez

Escribano

Ziegler

et al. 2020

Preprint

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“…SegCLR cell typing of pre-and post-synaptic partners for large and small fragments In brain circuit analysis, a common goal is to identify the cell types of the thousands of synaptic partners upstream or downstream of a particular cell or circuit of interest [46][47][48] . Due to the incompleteness of current automated reconstructions, contemporary connectomics-based circuit analysis efforts typically require significant manual tracing to extend each partner neurite back toward its soma in order to provide enough morphological structure to enable an expert to propose a cell type identity.…”

Section: Unsupervised Data Exploration Via Segclrmentioning

confidence: 99%

Multi-Layered Maps of Neuropil with Segmentation-Guided Contrastive Learning

Dorkenwald

Berger³

et al. 2022

Preprint

Self Cite

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Maps of the nervous system that identify individual cells along with their type, subcellular components, and connectivity have the potential to reveal fundamental organizational principles of neural circuits. Volumetric nanometer-resolution imaging of brain tissue provides the raw data needed to build such maps, but inferring all the relevant cellular and subcellular annotation layers is challenging. Here, we present Segmentation-Guided Contrastive Learning of Representations ("SegCLR"), a self-supervised machine learning technique that produces highly informative representations of cells directly from 3d electron microscope imagery and segmentations. When applied to volumes of human and mouse cerebral cortex, SegCLR enabled the classification of cellular subcompartments (axon, dendrite, soma, astrocytic process) with 4,000-fold less labeled data compared to fully supervised approaches. Surprisingly, SegCLR also enabled inference of cell types (neuron vs. glia and subtypes of each) from fragments with lengths as small as 10 micrometers, a task that can be difficult for humans to perform and whose feasibility greatly enhances the utility of imaging portions of brains in which many neuron fragments terminate at a volume boundary. These predictions were further augmented via Gaussian process uncertainty estimation to enable analyses restricted to high confidence subsets of the data.

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“…An important question is the degree to which PN connectivity in this brain region is stereotyped (as in the lateral horn) or whether random combinations of PN types synapse on common Kenyon cells. The latter scenario has been suggested to provide an anatomical basis to enhance odour discrimination and/or a template to permit ‘meaning’ to be imparted onto unpredictable olfactory stimuli through learning [ 104 , 113 , 114 ]. Initial low-resolution maps of PN innervations suggested the existence of a degree of zonal organization in the mushroom body [ 99 , 115 , 116 ], while subsequent single cell-resolution surveys of PN–Kenyon cell connectivity [ 114 ] (and related functional studies [ 117 ]) in a subset of the circuitry provided evidence that Kenyon cells receive input from random sets of PNs.…”

Section: Circuitrymentioning

confidence: 99%

“…Initial low-resolution maps of PN innervations suggested the existence of a degree of zonal organization in the mushroom body [ 99 , 115 , 116 ], while subsequent single cell-resolution surveys of PN–Kenyon cell connectivity [ 114 ] (and related functional studies [ 117 ]) in a subset of the circuitry provided evidence that Kenyon cells receive input from random sets of PNs. Recent comprehensive analysis of the PN–Kenyon cell connectome revealed a more nuanced situation, where there is a degree of non-random structure [ 104 ]. For example, PNs transmitting food-related odour signals converge onto Kenyon cells at frequencies above those expected by chance.…”

Section: Circuitrymentioning

confidence: 99%

Drosophila olfaction: past, present and future

Benton

2022

Proc. R. Soc. B.

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Among the many wonders of nature, the sense of smell of the fly Drosophila melanogaster might seem, at first glance, of esoteric interest. Nevertheless, for over a century, the ‘nose’ of this insect has been an extraordinary system to explore questions in animal behaviour, ecology and evolution, neuroscience, physiology and molecular genetics. The insights gained are relevant for our understanding of the sensory biology of vertebrates, including humans, and other insect species, encompassing those detrimental to human health. Here, I present an overview of our current knowledge of D. melanogaster olfaction, from molecules to behaviours, with an emphasis on the historical motivations of studies and illustration of how technical innovations have enabled advances. I also highlight some of the pressing and long-term questions.

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Structured sampling of olfactory input by the fly mushroom body

Cited by 36 publications

References 115 publications

The cellular architecture of memory modules inDrosophilasupports stochastic input integration

The cellular architecture of memory modules inDrosophilasupports stochastic input integration

Multi-Layered Maps of Neuropil with Segmentation-Guided Contrastive Learning

Drosophila olfaction: past, present and future

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