98,619 research outputs found
An Efficient Coding Theory for a Dynamic Trajectory Predicts non-Uniform Allocation of Grid Cells to Modules in the Entorhinal Cortex
Grid cells in the entorhinal cortex encode the position of an animal in its
environment using spatially periodic tuning curves of varying periodicity.
Recent experiments established that these cells are functionally organized in
discrete modules with uniform grid spacing. Here we develop a theory for
efficient coding of position, which takes into account the temporal statistics
of the animal's motion. The theory predicts a sharp decrease of module
population sizes with grid spacing, in agreement with the trends seen in the
experimental data. We identify a simple scheme for readout of the grid cell
code by neural circuitry, that can match in accuracy the optimal Bayesian
decoder of the spikes. This readout scheme requires persistence over varying
timescales, ranging from ~1ms to ~1s, depending on the grid cell module. Our
results suggest that the brain employs an efficient representation of position
which takes advantage of the spatiotemporal statistics of the encoded variable,
in similarity to the principles that govern early sensory coding.Comment: 23 pages, 5 figures. Supplemental Information available from the
authors on request. A previous version of this work appeared in abstract form
(Program No. 727.02. 2015 Neuroscience Meeting Planner. Chicago, IL: Society
for Neuroscience, 2015. Online.
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
Learning Temporal Alignment Uncertainty for Efficient Event Detection
In this paper we tackle the problem of efficient video event detection. We
argue that linear detection functions should be preferred in this regard due to
their scalability and efficiency during estimation and evaluation. A popular
approach in this regard is to represent a sequence using a bag of words (BOW)
representation due to its: (i) fixed dimensionality irrespective of the
sequence length, and (ii) its ability to compactly model the statistics in the
sequence. A drawback to the BOW representation, however, is the intrinsic
destruction of the temporal ordering information. In this paper we propose a
new representation that leverages the uncertainty in relative temporal
alignments between pairs of sequences while not destroying temporal ordering.
Our representation, like BOW, is of a fixed dimensionality making it easily
integrated with a linear detection function. Extensive experiments on CK+,
6DMG, and UvA-NEMO databases show significant performance improvements across
both isolated and continuous event detection tasks.Comment: Appeared in DICTA 2015, 8 page
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