14 research outputs found
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
Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy
Neural circuits can be reconstructed from brain images acquired by serial
section electron microscopy. Image analysis has been performed by manual labor
for half a century, and efforts at automation date back almost as far.
Convolutional nets were first applied to neuronal boundary detection a dozen
years ago, and have now achieved impressive accuracy on clean images. Robust
handling of image defects is a major outstanding challenge. Convolutional nets
are also being employed for other tasks in neural circuit reconstruction:
finding synapses and identifying synaptic partners, extending or pruning
neuronal reconstructions, and aligning serial section images to create a 3D
image stack. Computational systems are being engineered to handle petavoxel
images of cubic millimeter brain volumes
A Pipeline for Volume Electron Microscopy of the Caenorhabditis elegans Nervous System.
The "connectome," a comprehensive wiring diagram of synaptic connectivity, is achieved through volume electron microscopy (vEM) analysis of an entire nervous system and all associated non-neuronal tissues. White et al. (1986) pioneered the fully manual reconstruction of a connectome using Caenorhabditis elegans. Recent advances in vEM allow mapping new C. elegans connectomes with increased throughput, and reduced subjectivity. Current vEM studies aim to not only fill the remaining gaps in the original connectome, but also address fundamental questions including how the connectome changes during development, the nature of individuality, sexual dimorphism, and how genetic and environmental factors regulate connectivity. Here we describe our current vEM pipeline and projected improvements for the study of the C. elegans nervous system and beyond