5,981 research outputs found
Role of homeostasis in learning sparse representations
Neurons in the input layer of primary visual cortex in primates develop
edge-like receptive fields. One approach to understanding the emergence of this
response is to state that neural activity has to efficiently represent sensory
data with respect to the statistics of natural scenes. Furthermore, it is
believed that such an efficient coding is achieved using a competition across
neurons so as to generate a sparse representation, that is, where a relatively
small number of neurons are simultaneously active. Indeed, different models of
sparse coding, coupled with Hebbian learning and homeostasis, have been
proposed that successfully match the observed emergent response. However, the
specific role of homeostasis in learning such sparse representations is still
largely unknown. By quantitatively assessing the efficiency of the neural
representation during learning, we derive a cooperative homeostasis mechanism
that optimally tunes the competition between neurons within the sparse coding
algorithm. We apply this homeostasis while learning small patches taken from
natural images and compare its efficiency with state-of-the-art algorithms.
Results show that while different sparse coding algorithms give similar coding
results, the homeostasis provides an optimal balance for the representation of
natural images within the population of neurons. Competition in sparse coding
is optimized when it is fair. By contributing to optimizing statistical
competition across neurons, homeostasis is crucial in providing a more
efficient solution to the emergence of independent components
Nearfield Acoustic Holography using sparsity and compressive sampling principles
Regularization of the inverse problem is a complex issue when using
Near-field Acoustic Holography (NAH) techniques to identify the vibrating
sources. This paper shows that, for convex homogeneous plates with arbitrary
boundary conditions, new regularization schemes can be developed, based on the
sparsity of the normal velocity of the plate in a well-designed basis, i.e. the
possibility to approximate it as a weighted sum of few elementary basis
functions. In particular, these new techniques can handle discontinuities of
the velocity field at the boundaries, which can be problematic with standard
techniques. This comes at the cost of a higher computational complexity to
solve the associated optimization problem, though it remains easily tractable
with out-of-the-box software. Furthermore, this sparsity framework allows us to
take advantage of the concept of Compressive Sampling: under some conditions on
the sampling process (here, the design of a random array, which can be
numerically and experimentally validated), it is possible to reconstruct the
sparse signals with significantly less measurements (i.e., microphones) than
classically required. After introducing the different concepts, this paper
presents numerical and experimental results of NAH with two plate geometries,
and compares the advantages and limitations of these sparsity-based techniques
over standard Tikhonov regularization.Comment: Journal of the Acoustical Society of America (2012
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