10 research outputs found
Fully-Automatic Synapse Prediction and Validation on a Large Data Set
Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of connections (synapses) between neurons. As manual extraction of this information is very time-consuming, there has been extensive research efforts to automatically segment the neurons to help guide and eventually replace manual tracing. Until recently, there has been comparatively little research on automatic detection of the actual synapses between neurons. This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite for the first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections. However, recent research has demonstrated that contact area alone is not a sufficient predictor of a synaptic connection. Moreover, as segmentation improved, we observed that synapse annotation consumes a more significant fraction of overall reconstruction time (upwards of 50% of total effort). This ratio will only get worse as segmentation improves, gating the overall possible speed-up. Therefore, we address this problem by developing algorithms that automatically detect presynaptic neurons and their postsynaptic partners. In particular, presynaptic structures are detected using a U-Net convolutional neural network (CNN), and postsynaptic partners are detected using a multilayer perceptron (MLP) with features conditioned on the local segmentation. This work is novel because it requires minimal amount of training, leverages advances in image segmentation directly, and provides a complete solution for polyadic synapse detection. We further introduce novel metrics to evaluate our algorithm on connectomes of meaningful size. When applied to the output of our method on EM data from Drosphila, these metrics demonstrate that a completely automatic prediction can be used to effectively characterize most of the connectivity correctly
Synaptic partner prediction from point annotations in insect brains
High-throughput electron microscopy allows recording of lar- ge stacks of
neural tissue with sufficient resolution to extract the wiring diagram of the
underlying neural network. Current efforts to automate this process focus
mainly on the segmentation of neurons. However, in order to recover a wiring
diagram, synaptic partners need to be identi- fied as well. This is especially
challenging in insect brains like Drosophila melanogaster, where one
presynaptic site is associated with multiple post- synaptic elements. Here we
propose a 3D U-Net architecture to directly identify pairs of voxels that are
pre- and postsynaptic to each other. To that end, we formulate the problem of
synaptic partner identification as a classification problem on long-range edges
between voxels to encode both the presence of a synaptic pair and its
direction. This formulation allows us to directly learn from synaptic point
annotations instead of more ex- pensive voxel-based synaptic cleft or vesicle
annotations. We evaluate our method on the MICCAI 2016 CREMI challenge and
improve over the current state of the art, producing 3% fewer errors than the
next best method
Synaptic Partner Assignment Using Attentional Voxel Association Networks
Connectomics aims to recover a complete set of synaptic connections within a
dataset imaged by volume electron microscopy. Many systems have been proposed
for locating synapses, and recent research has included a way to identify the
synaptic partners that communicate at a synaptic cleft. We re-frame the problem
of identifying synaptic partners as directly generating the mask of the
synaptic partners from a given cleft. We train a convolutional network to
perform this task. The network takes the local image context and a binary mask
representing a single cleft as input. It is trained to produce two binary
output masks: one which labels the voxels of the presynaptic partner within the
input image, and another similar labeling for the postsynaptic partner. The
cleft mask acts as an attentional gating signal for the network. We find that
an implementation of this approach performs well on a dataset of mouse
somatosensory cortex, and evaluate it as part of a combined system to predict
both clefts and connections
A connectome of the adult drosophila central brain
The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions. Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain
A connectome and analysis of the adult Drosophila central brain
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
A connectome and analysis of the adult Drosophila central brain
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