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Multi-Layered Maps of Neuropil with Segmentation Guided Contrastive Learning
Casey M. Schneider-Mizell
Agnes L. Bodor
Nuno Maçarico da Costa
Jeff W. Lichtman
Forrest Collman
Daniel R. Berger
Sven Dorkenwald
Nature Methods (2023)
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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 elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.
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Early machine-learning systems were inspired by neural networks — now AI might allow neuroscientists to get to grips with the brain’s unique complexities.
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SyConn2: dense synaptic connectivity inference for volume electron microscopy
Joergen Kornfeld
Fabian Svara
Andrei Mancu
Michale S. Fee
Sven Dorkenwald
Hashir Ahmad
Philipp J. Schubert
Jonathan Klimesch
Nature Methods, 19 (2022), 1367–1370
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The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries.
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Structured sampling of olfactory input by the fly mushroom body
Corey Fisher
Tom Kazimiers
Davi Bock
Zhihao Zheng
Iqbal J. Ali
Lucia Kmecova
Nadiya Sharifi
Najla Masoodpanah
Matthew Nichols
Steven Calle-Schuler
Eric Perlman
Feng Li
Joseph Hsu
Current Biology, 32 (2022), pp. 3334-3349
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Associative memory formation and recall in the fruit fly Drosophila melanogaster is subserved by the mushroom body (MB). Upon arrival in the MB, sensory information undergoes a profound transformation from broadly tuned and stereotyped odorant responses in the olfactory projection neuron (PN) layer to narrowly tuned and nonstereotyped responses in the Kenyon cells (KCs). Theory and experiment suggest that this transformation is implemented by random connectivity between KCs and PNs. However, this hypothesis has been challenging to test, given the difficulty of mapping synaptic connections between large numbers of brain-spanning neurons. Here, we used a recent whole-brain electron microscopy volume of the adult fruit fly to map PN-to-KC connectivity at synaptic resolution. The PN-KC connectome revealed unexpected structure, with preponderantly food-responsive PN types converging at above-chance levels on downstream KCs. Axons of the overconvergent PN types tended to arborize near one another in the MB main calyx, making local KC dendrites more likely to receive input from those types. Overconvergent PN types preferentially co-arborize and connect with dendrites of αβ and α′β′ KC subtypes. Computational simulation of the observed network showed degraded discrimination performance compared with a random network, except when all signal flowed through the overconvergent, primarily food-responsive PN types. Additional theory and experiment will be needed to fully characterize the impact of the observed non-random network structure on associative memory formation and recall.
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Denoising-based Image Compression for Connectomics
Jeff W. Lichtman
Alex Shapson-Coe
Richard L. Schalek
Johannes Ballé
bioRxiv (2021)
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Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of reconstruction accuracy.
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A connectomic study of a petascale fragment of human cerebral cortex
Daniel R. Berger
Alex Shapson-Coe
Dongil Lee
Neha Karlupia
Benjamin Field
Hanspeter Pfister
Yuelong Wu
Rohin Kar
Shuohong Wang
Jeff W. Lichtman
Evelina Sjostedt
Richard L. Schalek
David Aley
Donglai Wei
Zudi Lin
Adi Peleg
Julian Wagner-Carena
Hank Wu
Angerica Fitzmaurice
Joanna Lau
Luke Bailey
Sven Dorkenwald
bioRxiv (2021)
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We acquired a rapidly preserved human surgical sample from the temporal lobe of the cerebral cortex. We stained a 1 mm3 volume with heavy metals, embedded it in resin, cut more than 5000 slices at ∼30 nm and imaged these sections using a high-speed multibeam scanning electron microscope. We used computational methods to render the three-dimensional structure containing 57,216 cells, hundreds of millions of neurites and 133.7 million synaptic connections. The 1.4 petabyte electron microscopy volume, the segmented cells, cell parts, blood vessels, myelin, inhibitory and excitatory synapses, and 104 manually proofread cells are available to peruse online. Many interesting and unusual features were evident in this dataset. Glia outnumbered neurons 2:1 and oligodendrocytes were the most common cell type in the volume. Excitatory spiny neurons comprised 69% of the neuronal population, and excitatory synapses also were in the majority (76%). The synaptic drive onto spiny neurons was biased more strongly toward excitation (70%) than was the case for inhibitory interneurons (48%). Despite incompleteness of the automated segmentation caused by split and merge errors, we could automatically generate (and then validate) connections between most of the excitatory and inhibitory neuron types both within and between layers. In studying these neurons we found that deep layer excitatory cell types can be classified into new subsets, based on structural and connectivity differences, and that chandelier interneurons not only innervate excitatory neuron initial segments as previously described, but also each other’s initial segments. Furthermore, among the thousands of weak connections established on each neuron, there exist rarer highly powerful axonal inputs that establish multi-synaptic contacts (up to ∼20 synapses) with target neurons. Our analysis indicates that these strong inputs are specific, and allow small numbers of axons to have an outsized role in the activity of some of their postsynaptic partners.
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The Mind of a Mouse
Narayanan Kasthuri
Bruce R. Rosen
John H.R. Maunsell
Davi D. Bock
David C. Van Essen
R. Clay Reid
Yann LeCun
Kristen M. Harris
Winfried Denk
Gerald M. Rubin
Adrienne L. Fairhall
David W. Tank
Doris Tsao
Catherine Dulac,
Edward M. Callaway
Liqun Luo
H. Sebastian Seung
Jeff W. Lichtman
Peter B. Littlewood
Larry F. Abbott
Moritz Helmstaedter
Terrence J. Sejnowski
Ila Fiete
Karel Svoboda
Cell, 182 (2020)
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Large scientific projects in genomics and astronomy are influential not because they answer any single ques- tion but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain’s synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.
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An anatomical substrate of credit assignment in reinforcement learning
Michale S. Fee
Jorgen Kornfeld
Philipp Schubert
Winfried Denk
bioRxiv (2020)
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How is experience used to improve performance? In both biological and artificial systems,
the optimization of parameters that affect behavior requires a process that determines
whether a parameter affects the outcome and then modifies the parameter accordingly.
Central to the recent bloom of artificial intelligence has been the error-backpropagation
algorithm(Rumelhart, Hinton, and Williams 1986) , which computationally retraces the signal
from the output to each synapse (weight) and allows a large number of parameters to be
optimized in parallel at high learning rates. Biological systems, however, lack an obvious
mechanism to retrace the signal path. Here we show, by combining high-throughput volume
electron microscopy (Denk and Horstmann 2004) and automated connectomic
analysis(Januszewski et al. 2018; Dorkenwald et al. 2017; Schubert et al. 2019) , that the
synaptic architecture of songbird basal ganglia supports a form of local credit assessment
proposed in a model of songbird reinforcement learning (M. S. Fee and Goldberg 2011). We
show that three of this model’s major predictions hold true: first, cortical axons that encode
exploratory motor variability terminate predominantly on dendritic shafts of spiny neurons.
Second, cortical axons that encode timing seek out spines, which enable calcium-based
coincidence detection (R. Yuste and Denk 1995) and appear to be capable of creating and
storing eligibility traces (Yagishita et al. 2014). Third, synapse pairs that presynaptically share
a cortical timing axon and post-synaptically a medium spiny dendrite are substantially more
similar in size than expected, indicating a history of Hebbian plasticity (Bartol et al. 2015;
Kasthuri et al. 2015) . Combined with numerical simulations these data provide strong
evidence for a model of basal ganglia learning with a biologically plausible credit assignment
mechanism.
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Recent advances in 3d electron microscopy are yielding ever larger reconstructions of brain tissue, encompassing thousands of individual neurons interconnected by millions of synapses. Interpreting reconstructions at this scale demands advances in the automated analysis of neuronal morphologies, for example by identifying morphological and functional subcompartments within neurons. We present a method that for the first time uses full 3d input (voxels) to automatically classify reconstructed neuron fragments as axon, dendrite, or somal subcompartments. Based on 3d convolutional neural networks, this method achieves a mean f1-score of 0.972, exceeding the previous state of the art of 0.955. The resulting predictions can support multiple analysis and proofreading applications. In particular, we leverage finely localized subcompartment predictions for automated detection and correction of merge errors in the volume reconstruction, successfully detecting 90.6% of inter-class merge errors with a false positive rate of only 2.7%.
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A connectome and analysis of the adult Drosophila central brain
Stephen M Plaza
Anne K Scott
Masayoshi Ito
Gregory SXE Jefferis
Tansy Yang
Marisa Dreher
Sari McLin
Sean M Ryan
Feng Li
Samantha Finley
Robert Svirskas
Alexander S Bates
Christopher Ordish
Christopher J Knecht
Jens Goldammer
Miatta Ndama
Jon Thomson Rymer
Nicole Neubarth
C Shan Xu
Christopher M Patrick
Jackie Swift
Dorota Tarnogorska
Gary B Huang
Shin-ya Takemura
Ashley L Scott
Satoko Takemura
Nicole A Kirk
Kenneth J Hayworth
Natalie L Smith
Michael Cook
Jolanta A Borycz
Louis K Scheffer
Gerald M Rubin
Patricia K Rivlin
Iris Talebi
SungJin Kim
Caitlin Ribeiro
Ting Zhao
Neha Rampally
Nicholas Padilla
Stephan Saalfeld
Nora Forknall
Claire Smith
Aya Shinomiya
Tanya Wolff
Vivek Jayaraman
Donald J Olbris
Marta Costa
Madelaine K Robertson
Nneoma Okeoma
Audrey Francis
Brandon S Canino
Natasha Cheatham
Alia Suleiman
Caroline Mooney
Lowell Umayam
Ian Meinertzhagen
Tyler Paterson
Khaled A Khairy
Samantha Ballinger
Reed George
Omotara Ogundeyi
Alanna Lohff
Margaret A Sobeski
Jody Clements
Bryon Eubanks
Harald F Hess
Dagmar Kainmueller
Kelsey Smith
Emily M Phillips
Kazunori Shinomiya
Philip M Hubbard
Emily Tenshaw
Dennis A Bailey
Ruchi Parekh
Eric T Trautman
Megan Sammons
William T Katz
Julie Kovalyak
Hideo Otsuna
John J Walsh
Tom Dolafi
Charli Maldonado
Kei Ito
Gary Patrick Hopkins
Jane Anne Horne
Erika Neace
Emily M Joyce
Temour Tokhi
Kelli Fairbanks
Zhiyuan Lu
Elliott E Phillips
Emily A Manley
Stuart Berg
Takashi Kawase
Chelsea X Alvarado
Shirley Lauchie
Philipp Schlegel
David Ackerman
John Bogovic
Octave Duclos
Larry Lindsey
eLife, 9 (2020)
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The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.
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