4 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
Towards an integrated understanding of neural networks
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mathematics, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 123-136).Neural networks underpin both biological intelligence and modern Al systems, yet there is relatively little theory for how the observed behavior of these networks arises. Even the connectivity of neurons within the brain remains largely unknown, and popular deep learning algorithms lack theoretical justification or reliability guarantees. This thesis aims towards a more rigorous understanding of neural networks. We characterize and, where possible, prove essential properties of neural algorithms: expressivity, learning, and robustness. We show how observed emergent behavior can arise from network dynamics, and we develop algorithms for learning more about the network structure of the brain.by David Rolnick.Ph. D