Connectomics
Our goal is to leverage Google expertise and resources to advance understanding of the structure and function of the brain.
Connectomics
About our team
A major hypothesis in modern neuroscience is that neuron-to-neuron connectivity structure in the brain can be linked to function -- how the brain encodes memories, extracts features from perceptual stimuli, and makes decisions. However, the structure of these brain networks has remained largely unknown, due to technical difficulties involved in imaging and reconstructing the brain in 3D.
New microscopy techniques have begun to address the challenge of imaging the brain in 3D at nanometer resolution, and 4D at cellular resolution, but this has led to a huge bottleneck in the subsequent step of data analysis. Our goal is to help solve some of these data analysis problems and thus enable a high-throughput approach to studying the network architecture of the brain.
In order to facilitate this work we collaborate with the Max Planck Institute, HHMI Janelia Research Campus, Harvard University, and other organizations.
Team focus summaries
We develop algorithms and software for automating the process of aligning, segmenting, and annotating petabyte-scale 3d images of brain tissue.
We develop software such as TensorStore and Neuroglancer which helps store, process, and visualize large n-dimensional images and volumes.
Featured publications
A connectome and analysis of the adult Drosophila central brain
Louis K Scheffer
C Shan Xu
Zhiyuan Lu
Shin-ya Takemura
Kenneth J Hayworth
Gary B Huang
Kazunori Shinomiya
Stuart Berg
Jody Clements
Philip M Hubbard
William T Katz
Lowell Umayam
Ting Zhao
David Ackerman
John Bogovic
Tom Dolafi
Dagmar Kainmueller
Takashi Kawase
Khaled A Khairy
Larry Lindsey
Nicole Neubarth
Donald J Olbris
Hideo Otsuna
Eric T Trautman
Masayoshi Ito
Alexander S Bates
Jens Goldammer
Tanya Wolff
Robert Svirskas
Philipp Schlegel
Erika Neace
Christopher J Knecht
Chelsea X Alvarado
Dennis A Bailey
Samantha Ballinger
Jolanta A Borycz
Brandon S Canino
Natasha Cheatham
Michael Cook
Marisa Dreher
Octave Duclos
Bryon Eubanks
Kelli Fairbanks
Samantha Finley
Nora Forknall
Audrey Francis
Gary Patrick Hopkins
Emily M Joyce
SungJin Kim
Nicole A Kirk
Julie Kovalyak
Shirley Lauchie
Alanna Lohff
Charli Maldonado
Emily A Manley
Sari McLin
Caroline Mooney
Miatta Ndama
Omotara Ogundeyi
Nneoma Okeoma
Christopher Ordish
Nicholas Padilla
Christopher M Patrick
Tyler Paterson
Elliott E Phillips
Emily M Phillips
Neha Rampally
Caitlin Ribeiro
Madelaine K Robertson
Jon Thomson Rymer
Sean M Ryan
Megan Sammons
Anne K Scott
Ashley L Scott
Aya Shinomiya
Claire Smith
Kelsey Smith
Natalie L Smith
Margaret A Sobeski
Alia Suleiman
Jackie Swift
Satoko Takemura
Iris Talebi
Dorota Tarnogorska
Emily Tenshaw
Temour Tokhi
John J Walsh
Tansy Yang
Jane Anne Horne
Feng Li
Ruchi Parekh
Patricia K Rivlin
Vivek Jayaraman
Marta Costa
Gregory SXE Jefferis
Kei Ito
Stephan Saalfeld
Reed George
Ian Meinertzhagen
Gerald M Rubin
Harald F Hess
Stephen M Plaza
eLife, vol. 9 (2020)
Preview abstract
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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Denoising-based Image Compression for Connectomics
Alex Shapson-Coe
Richard L. Schalek
Jeff W. Lichtman
bioRxiv (2021)
Preview abstract
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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The Mind of a Mouse
Larry F. Abbott
Davi D. Bock
Edward M. Callaway
Winfried Denk
Catherine Dulac,
Adrienne L. Fairhall
Ila Fiete
Kristen M. Harris
Moritz Helmstaedter
Narayanan Kasthuri
Yann LeCun
Jeff W. Lichtman
Peter B. Littlewood
Liqun Luo
John H.R. Maunsell
R. Clay Reid
Bruce R. Rosen
Gerald M. Rubin
Terrence J. Sejnowski
H. Sebastian Seung
Karel Svoboda
David W. Tank
Doris Tsao
David C. Van Essen
Cell, vol. 182 (2020)
Preview abstract
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
Jorgen Kornfeld
Michale S. Fee
Philipp Schubert
Winfried Denk
bioRxiv (2020)
Preview abstract
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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Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment
Larry Lindsey
Zhihao Zheng
Alexander Shakeel Bates
István Taisz
Matthew Nichols
Feng Li
Eric Perlman
Gregory S.X.E. Jefferis
Davi Bock
bioRxiv (2019)
Preview abstract
Reconstruction of neural circuitry at single-synapse resolution is an attractive target for improving understanding of the nervous system in health and disease. Serial section transmission electron microscopy (ssTEM) is among the most prolific imaging methods employed in pursuit of such reconstructions. We demonstrate how Flood-Filling Networks (FFNs) can be used to computationally segment a forty-teravoxel whole-brain Drosophila ssTEM volume. To compensate for data irregularities and imperfect global alignment, FFNs were combined with procedures that locally re-align serial sections as well as dynamically adjust and synthesize image content. The proposed approach produced a largely merger-free segmentation of the entire ssTEM Drosophila brain, which we make freely available. As compared to manual tracing using an efficient skeletonization strategy, the segmentation enabled circuit reconstruction and analysis workflows that were an order of magnitude faster.
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High-Precision Automated Reconstruction of Neurons with Flood-Filling Networks
Jörgen Kornfeld
Larry Lindsey
Winfried Denk
Nature Methods (2018)
Preview abstract
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.
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Preview abstract
For the past decade, convolutional networks have been used for 3D reconstruction
of neurons from electron microscopic (EM) brain images. Recent years have seen
great improvements in accuracy, as evidenced by submissions to the SNEMI3D
benchmark challenge. Here we report the first submission to surpass the estimate
of human accuracy provided by the SNEMI3D leaderboard. A variant of 3D UNet
is trained on a primary task of predicting affinities between nearest neighbor
voxels, and an auxiliary task of predicting long-range affinities. The training data is
augmented by simulated image defects. The nearest neighbor affinities are used to
create an oversegmentation, and then supervoxels are greedily agglomerated based
on mean affinity. The resulting SNEMI3D score exceeds the estimate of human
accuracy by a large margin. While one should be cautious about extrapolating
from the SNEMI3D benchmark to real-world accuracy of large-scale neural circuit
reconstruction, our submission inspires optimism that the goal of full automation
may be realizable in the future.
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Adversarial Image Alignment and Interpolation
arXiv, vol. abs/1707.00067 (2017)
Preview abstract
Volumetric (3d) images are acquired for many scientific and biomedical purposes using imaging methods such as serial section microscopy, CT scans, and MRI. A frequent step in the analysis and reconstruction of such data is the alignment and registration of images that were acquired in succession along a spatial or temporal dimension. For example, in serial section electron microscopy, individual 2d sections are imaged via electron microscopy and then must be aligned to one another in order to produce a coherent 3d volume. State of the art approaches find image correspondences derived from patch matching and invariant feature detectors, and then solve optimization problems that rigidly or elastically deform series of images into an aligned volume. Here we show how fully convolutional neural networks trained with an adversarial loss function can be used for two tasks: (1) synthesis of missing or damaged image data from adjacent sections, and (2) fine-scale alignment of block-face electron microscopy data. Finally, we show how these two capabilities can be combined in order to produce artificial isotropic volumes from anisotropic image volumes using a super-resolution adversarial alignment and interpolation approach.
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Combinatorial Energy Learning for Image Segmentation
Pieter Abbeel
Advances in Neural Information Processing Systems 29 (2016)
Preview abstract
We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image. Our approach, combinatorial energy learning for image segmentation (CELIS) places a particular emphasis on modeling the inherent combinatorial nature of dense image segmentation problems. We propose efficient algorithms for learning deep neural networks to model the energy function, and for local optimization of this energy in the space of supervoxel agglomerations. We extensively evaluate our method on a publicly available 3-D microscopy dataset with 25 billion voxels of ground truth data. On an 11 billion voxel test set, we find that our method improves volumetric reconstruction accuracy by more than 20% as compared to two state-of-the-art baseline methods: graph-based segmentation of the output of a 3-D convolutional neural network trained to predict boundaries, as well as a random forest classifier trained to agglomerate supervoxels that were generated by a 3-D convolutional neural network.
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Highlighted work
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A Browsable Petascale Reconstruction of the Human CortexIn collaboration with the Lichtman Laboratory at Harvard University, we are releasing the “H01” dataset, a 1.4 petabyte rendering of a small sample of human brain tissue, along with a companion paper, “A connectomic study of a petascale fragment of human cerebral cortex. -
The Drosophila Hemibrain Connectome: The Largest Synapse-Resolution Map of Brain ConnectivityIn collaboration with the FlyEM team at HHMI’s Janelia Research Campus and several other research partners, we released the "hemibrain" connectome, a highly detailed map of neuronal connectivity in the fly brain, along with a suite of tools for visualization and analysis. -
Flood-Filling Networks: Improving Connectomics by an Order of MagnitudeIn collaboration with colleagues at the Max Planck Institute of Neurobiology, we published High-Precision Automated Reconstruction of Neurons with Flood-Filling Networks in Nature Methods, which shows how a new type of recurrent neural network can improve the accuracy of automated interpretation of connectomics data by an order of magnitude over previous deep learning techniques. -
NeuroglancerNeuroglancer is a WebGL-based viewer for petascale multidimensional data. It is capable of displaying arbitrary (non axis-aligned) cross-sectional views of volumetric data, as well as 3-D meshes and line-segment based models (skeletons). -
TensorStoreCloud-first library for reading and writing large multi-dimensional arrays, with advanced concurrency and indexing capabilities.
Some of our locations
Some of our people
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Tim Blakely
- General Science
- Machine Intelligence
- Robotics
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Viren Jain
- General Science
- Machine Perception
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Michał Januszewski
- Distributed Systems and Parallel Computing
- General Science
- Machine Intelligence
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Laramie Leavitt
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Peter H. Li
- General Science
- Machine Intelligence
- Machine Perception
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Jeremy Maitin-Shepard
- Machine Intelligence
- Machine Perception
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Art Pope
- Machine Intelligence
- Machine Perception
