101,915 research outputs found
How Correlations Influence Lasso Prediction
We study how correlations in the design matrix influence Lasso prediction.
First, we argue that the higher the correlations are, the smaller the optimal
tuning parameter is. This implies in particular that the standard tuning
parameters, that do not depend on the design matrix, are not favorable.
Furthermore, we argue that Lasso prediction works well for any degree of
correlations if suitable tuning parameters are chosen. We study these two
subjects theoretically as well as with simulations
How behavioral constraints may determine optimal sensory representations
The sensory-triggered activity of a neuron is typically characterized in
terms of a tuning curve, which describes the neuron's average response as a
function of a parameter that characterizes a physical stimulus. What determines
the shapes of tuning curves in a neuronal population? Previous theoretical
studies and related experiments suggest that many response characteristics of
sensory neurons are optimal for encoding stimulus-related information. This
notion, however, does not explain the two general types of tuning profiles that
are commonly observed: unimodal and monotonic. Here, I quantify the efficacy of
a set of tuning curves according to the possible downstream motor responses
that can be constructed from them. Curves that are optimal in this sense may
have monotonic or non-monotonic profiles, where the proportion of monotonic
curves and the optimal tuning curve width depend on the general properties of
the target downstream functions. This dependence explains intriguing features
of visual cells that are sensitive to binocular disparity and of neurons tuned
to echo delay in bats. The numerical results suggest that optimal sensory
tuning curves are shaped not only by stimulus statistics and signal-to-noise
properties, but also according to their impact on downstream neural circuits
and, ultimately, on behavior.Comment: 24 pages, 9 figures (main text + supporting information
Optimization of Planck/LFI on--board data handling
To asses stability against 1/f noise, the Low Frequency Instrument (LFI)
onboard the Planck mission will acquire data at a rate much higher than the
data rate allowed by its telemetry bandwith of 35.5 kbps. The data are
processed by an onboard pipeline, followed onground by a reversing step. This
paper illustrates the LFI scientific onboard processing to fit the allowed
datarate. This is a lossy process tuned by using a set of 5 parameters Naver,
r1, r2, q, O for each of the 44 LFI detectors. The paper quantifies the level
of distortion introduced by the onboard processing, EpsilonQ, as a function of
these parameters. It describes the method of optimizing the onboard processing
chain. The tuning procedure is based on a optimization algorithm applied to
unprocessed and uncompressed raw data provided either by simulations, prelaunch
tests or data taken from LFI operating in diagnostic mode. All the needed
optimization steps are performed by an automated tool, OCA2, which ends with
optimized parameters and produces a set of statistical indicators, among them
the compression rate Cr and EpsilonQ. For Planck/LFI the requirements are Cr =
2.4 and EpsilonQ <= 10% of the rms of the instrumental white noise. To speedup
the process an analytical model is developed that is able to extract most of
the relevant information on EpsilonQ and Cr as a function of the signal
statistics and the processing parameters. This model will be of interest for
the instrument data analysis. The method was applied during ground tests when
the instrument was operating in conditions representative of flight. Optimized
parameters were obtained and the performance has been verified, the required
data rate of 35.5 Kbps has been achieved while keeping EpsilonQ at a level of
3.8% of white noise rms well within the requirements.Comment: 51 pages, 13 fig.s, 3 tables, pdflatex, needs JINST.csl, graphicx,
txfonts, rotating; Issue 1.0 10 nov 2009; Sub. to JINST 23Jun09, Accepted
10Nov09, Pub.: 29Dec09; This is a preprint, not the final versio
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