74,997 research outputs found
High accuracy decoding of dynamical motion from a large retinal population
Motion tracking is a challenge the visual system has to solve by reading out
the retinal population. Here we recorded a large population of ganglion cells
in a dense patch of salamander and guinea pig retinas while displaying a bar
moving diffusively. We show that the bar position can be reconstructed from
retinal activity with a precision in the hyperacuity regime using a linear
decoder acting on 100+ cells. The classical view would have suggested that the
firing rates of the cells form a moving hill of activity tracking the bar's
position. Instead, we found that ganglion cells fired sparsely over an area
much larger than predicted by their receptive fields, so that the neural image
did not track the bar. This highly redundant organization allows for diverse
collections of ganglion cells to represent high-accuracy motion information in
a form easily read out by downstream neural circuits.Comment: 23 pages, 7 figure
Scalable Estimation of Precision Maps in a MapReduce Framework
This paper presents a large-scale strip adjustment method for LiDAR mobile
mapping data, yielding highly precise maps. It uses several concepts to achieve
scalability. First, an efficient graph-based pre-segmentation is used, which
directly operates on LiDAR scan strip data, rather than on point clouds.
Second, observation equations are obtained from a dense matching, which is
formulated in terms of an estimation of a latent map. As a result of this
formulation, the number of observation equations is not quadratic, but rather
linear in the number of scan strips. Third, the dynamic Bayes network, which
results from all observation and condition equations, is partitioned into two
sub-networks. Consequently, the estimation matrices for all position and
orientation corrections are linear instead of quadratic in the number of
unknowns and can be solved very efficiently using an alternating least squares
approach. It is shown how this approach can be mapped to a standard key/value
MapReduce implementation, where each of the processing nodes operates
independently on small chunks of data, leading to essentially linear
scalability. Results are demonstrated for a dataset of one billion measured
LiDAR points and 278,000 unknowns, leading to maps with a precision of a few
millimeters.Comment: ACM SIGSPATIAL'16, October 31-November 03, 2016, Burlingame, CA, US
Optical Flow on Evolving Surfaces with an Application to the Analysis of 4D Microscopy Data
We extend the concept of optical flow to a dynamic non-Euclidean setting.
Optical flow is traditionally computed from a sequence of flat images. It is
the purpose of this paper to introduce variational motion estimation for images
that are defined on an evolving surface. Volumetric microscopy images depicting
a live zebrafish embryo serve as both biological motivation and test data.Comment: The final publication is available at link.springer.co
Learning Ground Traversability from Simulations
Mobile ground robots operating on unstructured terrain must predict which
areas of the environment they are able to pass in order to plan feasible paths.
We address traversability estimation as a heightmap classification problem: we
build a convolutional neural network that, given an image representing the
heightmap of a terrain patch, predicts whether the robot will be able to
traverse such patch from left to right. The classifier is trained for a
specific robot model (wheeled, tracked, legged, snake-like) using simulation
data on procedurally generated training terrains; the trained classifier can be
applied to unseen large heightmaps to yield oriented traversability maps, and
then plan traversable paths. We extensively evaluate the approach in simulation
on six real-world elevation datasets, and run a real-robot validation in one
indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation
Global parameter identification of stochastic reaction networks from single trajectories
We consider the problem of inferring the unknown parameters of a stochastic
biochemical network model from a single measured time-course of the
concentration of some of the involved species. Such measurements are available,
e.g., from live-cell fluorescence microscopy in image-based systems biology. In
addition, fluctuation time-courses from, e.g., fluorescence correlation
spectroscopy provide additional information about the system dynamics that can
be used to more robustly infer parameters than when considering only mean
concentrations. Estimating model parameters from a single experimental
trajectory enables single-cell measurements and quantification of cell--cell
variability. We propose a novel combination of an adaptive Monte Carlo sampler,
called Gaussian Adaptation, and efficient exact stochastic simulation
algorithms that allows parameter identification from single stochastic
trajectories. We benchmark the proposed method on a linear and a non-linear
reaction network at steady state and during transient phases. In addition, we
demonstrate that the present method also provides an ellipsoidal volume
estimate of the viable part of parameter space and is able to estimate the
physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems
Biology
Particle-filtering approaches for nonlinear Bayesian decoding of neuronal spike trains
The number of neurons that can be simultaneously recorded doubles every seven
years. This ever increasing number of recorded neurons opens up the possibility
to address new questions and extract higher dimensional stimuli from the
recordings. Modeling neural spike trains as point processes, this task of
extracting dynamical signals from spike trains is commonly set in the context
of nonlinear filtering theory. Particle filter methods relying on importance
weights are generic algorithms that solve the filtering task numerically, but
exhibit a serious drawback when the problem dimensionality is high: they are
known to suffer from the 'curse of dimensionality' (COD), i.e. the number of
particles required for a certain performance scales exponentially with the
observable dimensions. Here, we first briefly review the theory on filtering
with point process observations in continuous time. Based on this theory, we
investigate both analytically and numerically the reason for the COD of
weighted particle filtering approaches: Similarly to particle filtering with
continuous-time observations, the COD with point-process observations is due to
the decay of effective number of particles, an effect that is stronger when the
number of observable dimensions increases. Given the success of unweighted
particle filtering approaches in overcoming the COD for continuous- time
observations, we introduce an unweighted particle filter for point-process
observations, the spike-based Neural Particle Filter (sNPF), and show that it
exhibits a similar favorable scaling as the number of dimensions grows.
Further, we derive rules for the parameters of the sNPF from a maximum
likelihood approach learning. We finally employ a simple decoding task to
illustrate the capabilities of the sNPF and to highlight one possible future
application of our inference and learning algorithm
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