50,674 research outputs found
Adaptive self-organization in a realistic neural network model
Information processing in complex systems is often found to be maximally
efficient close to critical states associated with phase transitions. It is
therefore conceivable that also neural information processing operates close to
criticality. This is further supported by the observation of power-law
distributions, which are a hallmark of phase transitions. An important open
question is how neural networks could remain close to a critical point while
undergoing a continual change in the course of development, adaptation,
learning, and more. An influential contribution was made by Bornholdt and
Rohlf, introducing a generic mechanism of robust self-organized criticality in
adaptive networks. Here, we address the question whether this mechanism is
relevant for real neural networks. We show in a realistic model that
spike-time-dependent synaptic plasticity can self-organize neural networks
robustly toward criticality. Our model reproduces several empirical
observations and makes testable predictions on the distribution of synaptic
strength, relating them to the critical state of the network. These results
suggest that the interplay between dynamics and topology may be essential for
neural information processing.Comment: 6 pages, 4 figure
Spectral pre-modulation of training examples enhances the spatial resolution of the Phase Extraction Neural Network (PhENN)
The Phase Extraction Neural Network (PhENN) is a computational architecture,
based on deep machine learning, for lens-less quantitative phase retrieval from
raw intensity data. PhENN is a deep convolutional neural network trained
through examples consisting of pairs of true phase objects and their
corresponding intensity diffraction patterns; thereafter, given a test raw
intensity pattern PhENN is capable of reconstructing the original phase object
robustly, in many cases even for objects outside the database where the
training examples were drawn from. Here, we show that the spatial frequency
content of the training examples is an important factor limiting PhENN's
spatial frequency response. For example, if the training database is relatively
sparse in high spatial frequencies, as most natural scenes are, PhENN's ability
to resolve fine spatial features in test patterns will be correspondingly
limited. To combat this issue, we propose "flattening" the power spectral
density of the training examples before presenting them to PhENN. For phase
objects following the statistics of natural scenes, we demonstrate
experimentally that the spectral pre-modulation method enhances the spatial
resolution of PhENN by a factor of 2.Comment: 12 pages, 10 figure
Discrete Multi-modal Hashing with Canonical Views for Robust Mobile Landmark Search
Mobile landmark search (MLS) recently receives increasing attention for its
great practical values. However, it still remains unsolved due to two important
challenges. One is high bandwidth consumption of query transmission, and the
other is the huge visual variations of query images sent from mobile devices.
In this paper, we propose a novel hashing scheme, named as canonical view based
discrete multi-modal hashing (CV-DMH), to handle these problems via a novel
three-stage learning procedure. First, a submodular function is designed to
measure visual representativeness and redundancy of a view set. With it,
canonical views, which capture key visual appearances of landmark with limited
redundancy, are efficiently discovered with an iterative mining strategy.
Second, multi-modal sparse coding is applied to transform visual features from
multiple modalities into an intermediate representation. It can robustly and
adaptively characterize visual contents of varied landmark images with certain
canonical views. Finally, compact binary codes are learned on intermediate
representation within a tailored discrete binary embedding model which
preserves visual relations of images measured with canonical views and removes
the involved noises. In this part, we develop a new augmented Lagrangian
multiplier (ALM) based optimization method to directly solve the discrete
binary codes. We can not only explicitly deal with the discrete constraint, but
also consider the bit-uncorrelated constraint and balance constraint together.
Experiments on real world landmark datasets demonstrate the superior
performance of CV-DMH over several state-of-the-art methods
The Cyborg Astrobiologist: Testing a Novelty-Detection Algorithm on Two Mobile Exploration Systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah
(ABRIDGED) In previous work, two platforms have been developed for testing
computer-vision algorithms for robotic planetary exploration (McGuire et al.
2004b,2005; Bartolo et al. 2007). The wearable-computer platform has been
tested at geological and astrobiological field sites in Spain (Rivas
Vaciamadrid and Riba de Santiuste), and the phone-camera has been tested at a
geological field site in Malta. In this work, we (i) apply a Hopfield
neural-network algorithm for novelty detection based upon color, (ii) integrate
a field-capable digital microscope on the wearable computer platform, (iii)
test this novelty detection with the digital microscope at Rivas Vaciamadrid,
(iv) develop a Bluetooth communication mode for the phone-camera platform, in
order to allow access to a mobile processing computer at the field sites, and
(v) test the novelty detection on the Bluetooth-enabled phone-camera connected
to a netbook computer at the Mars Desert Research Station in Utah. This systems
engineering and field testing have together allowed us to develop a real-time
computer-vision system that is capable, for example, of identifying lichens as
novel within a series of images acquired in semi-arid desert environments. We
acquired sequences of images of geologic outcrops in Utah and Spain consisting
of various rock types and colors to test this algorithm. The algorithm robustly
recognized previously-observed units by their color, while requiring only a
single image or a few images to learn colors as familiar, demonstrating its
fast learning capability.Comment: 28 pages, 12 figures, accepted for publication in the International
Journal of Astrobiolog
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