278 research outputs found
Reading the Book of Memory: Sparse Sampling versus Dense Mapping of Connectomes
Many theories of neural networks assume rules of connection between pairs of neurons that are based on their cell types or functional properties. It is finally becoming feasible to test such pairwise models of connectivity, due to emerging advances in neuroanatomical techniques. One method will be to measure the functional properties of connected pairs of neurons, sparsely sampling pairs from many specimens. Another method will be to find a “connectome,” a dense map of all connections in a single specimen, and infer functional properties of neurons through computational analysis. For the latter method, the most exciting prospect would be to decode the memories that are hypothesized to be stored in connectomes
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection
Efforts to automate the reconstruction of neural circuits from 3D electron
microscopic (EM) brain images are critical for the field of connectomics. An
important computation for reconstruction is the detection of neuronal
boundaries. Images acquired by serial section EM, a leading 3D EM technique,
are highly anisotropic, with inferior quality along the third dimension. For
such images, the 2D max-pooling convolutional network has set the standard for
performance at boundary detection. Here we achieve a substantial gain in
accuracy through three innovations. Following the trend towards deeper networks
for object recognition, we use a much deeper network than previously employed
for boundary detection. Second, we incorporate 3D as well as 2D filters, to
enable computations that use 3D context. Finally, we adopt a recursively
trained architecture in which a first network generates a preliminary boundary
map that is provided as input along with the original image to a second network
that generates a final boundary map. Backpropagation training is accelerated by
ZNN, a new implementation of 3D convolutional networks that uses multicore CPU
parallelism for speed. Our hybrid 2D-3D architecture could be more generally
applicable to other types of anisotropic 3D images, including video, and our
recursive framework for any image labeling problem
DDGM: Solving inverse problems by Diffusive Denoising of Gradient-based Minimization
Inverse problems generally require a regularizer or prior for a good
solution. A recent trend is to train a convolutional net to denoise images, and
use this net as a prior when solving the inverse problem. Several proposals
depend on a singular value decomposition of the forward operator, and several
others backpropagate through the denoising net at runtime. Here we propose a
simpler approach that combines the traditional gradient-based minimization of
reconstruction error with denoising. Noise is also added at each step, so the
iterative dynamics resembles a Langevin or diffusion process. Both the level of
added noise and the size of the denoising step decay exponentially with time.
We apply our method to the problem of tomographic reconstruction from electron
micrographs acquired at multiple tilt angles. With empirical studies using
simulated tilt views, we find parameter settings for our method that produce
good results. We show that high accuracy can be achieved with as few as 50
denoising steps. We also compare with DDRM and DPS, more complex diffusion
methods of the kinds mentioned above. These methods are less accurate (as
measured by MSE and SSIM) for our tomography problem, even after the generation
hyperparameters are optimized. Finally we extend our method to reconstruction
of arbitrary-sized images and show results on 128 1568 pixel imagesComment: Solving inverse problems using gradient minimization coupled with a
diffusion prio
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