3 research outputs found
Robust neural circuit reconstruction from serial electron microscopy with convolutional recurrent networks
Recent successes in deep learning have started to impact neuroscience. Of
particular significance are claims that current segmentation algorithms achieve
"super-human" accuracy in an area known as connectomics. However, as we will
show, these algorithms do not effectively generalize beyond the particular
source and brain tissues used for training -- severely limiting their usability
by the broader neuroscience community. To fill this gap, we describe a novel
connectomics challenge for source- and tissue-agnostic reconstruction of
neurons (STAR), which favors broad generalization over fitting specific
datasets. We first demonstrate that current state-of-the-art approaches to
neuron segmentation perform poorly on the challenge. We further describe a
novel convolutional recurrent neural network module that combines short-range
horizontal connections within a processing stage and long-range top-down
connections between stages. The resulting architecture establishes the state of
the art on the STAR challenge and represents a significant step towards widely
usable and fully-automated connectomics analysis
Stable and expressive recurrent vision models
Primate vision depends on recurrent processing for reliable perception
(Gilbert & Li, 2013). At the same time, there is a growing body of literature
demonstrating that recurrent connections improve the learning efficiency and
generalization of vision models on classic computer vision challenges. Why
then, are current large-scale challenges dominated by feedforward networks? We
posit that the effectiveness of recurrent vision models is bottlenecked by the
widespread algorithm used for training them, "back-propagation through time"
(BPTT), which has O(N) memory-complexity for training an N step model. Thus,
recurrent vision model design is bounded by memory constraints, forcing a
choice between rivaling the enormous capacity of leading feedforward models or
trying to compensate for this deficit through granular and complex dynamics.
Here, we develop a new learning algorithm, "contractor recurrent
back-propagation" (C-RBP), which alleviates these issues by achieving constant
O(1) memory-complexity with steps of recurrent processing. We demonstrate that
recurrent vision models trained with C-RBP can detect long-range spatial
dependencies in a synthetic contour tracing task that BPTT-trained models
cannot. We further demonstrate that recurrent vision models trained with C-RBP
to solve the large-scale Panoptic Segmentation MS-COCO challenge outperform the
leading feedforward approach. C-RBP is a general-purpose learning algorithm for
any application that can benefit from expansive recurrent dynamics. Code and
data are available at https://github.com/c-rbp
Recurrent neural circuits for contour detection
We introduce a deep recurrent neural network architecture that approximates
visual cortical circuits. We show that this architecture, which we refer to as
the gamma-net, learns to solve contour detection tasks with better sample
efficiency than state-of-the-art feedforward networks, while also exhibiting a
classic perceptual illusion, known as the orientation-tilt illusion. Correcting
this illusion significantly reduces gamma-net contour detection accuracy by
driving it to prefer low-level edges over high-level object boundary contours.
Overall, our study suggests that the orientation-tilt illusion is a byproduct
of neural circuits that help biological visual systems achieve robust and
efficient contour detection, and that incorporating these circuits in
artificial neural networks can improve computer vision.Comment: Published in ICLR 202