12,295 research outputs found
Matter-antimatter asymmetry restrains the dimensionality of neural representations: quantum decryption of large-scale neural coding
Projections from the study of the human universe onto the study of the
self-organizing brain are herein leveraged to address certain concerns raised
in latest neuroscience research, namely (i) the extent to which neural codes
are multidimensional; (ii) the functional role of neural dark matter; (iii) the
challenge to traditional model frameworks posed by the needs for accurate
interpretation of large-scale neural recordings linking brain and behavior. On
the grounds of (hyper-)self-duality under (hyper-)mirror supersymmetry,
inter-relativistic quantum principles are introduced, whose consolidation, as
spin-geometrical pillars of a network- and game-theoretical construction, is
conducive to (i) the high-precision reproduction and reinterpretation of core
experimental observations on neural coding in the self-organizing brain, with
the instantaneous geometric dimensionality of neural representations of a
spontaneous behavioral state being proven to be at most 16, unidirectionally;
(ii) a possible role for spinor (co-)representations, as the latent building
blocks of self-organizing cortical circuits subserving (co-)behavioral states;
(iii) an early crystallization of pertinent multidimensional synaptic
(co-)architectures, whereby Lorentz (co-)partitions are in principle
verifiable; and, ultimately, (iv) potentially inverse insights into
matter-antimatter asymmetry. New avenues for the decryption of large-scale
neural coding in health and disease are being discussed.Comment: 33 pages;3 figures;1 table;minor edit
Hybrid quantum-classical unsupervised data clustering based on the Self-Organizing Feature Map
Unsupervised machine learning is one of the main techniques employed in
artificial intelligence. Quantum computers offer opportunities to speed up such
machine learning techniques. Here, we introduce an algorithm for quantum
assisted unsupervised data clustering using the self-organizing feature map, a
type of artificial neural network. We make a proof-of-concept realization of
one of the central components on the IBM Q Experience and show that it allows
us to reduce the number of calculations in a number of clusters. We compare the
results with the classical algorithm on a toy example of unsupervised text
clustering
Neural Network Aided Glitch-Burst Discrimination and Glitch Classification
We investigate the potential of neural-network based classifiers for
discriminating gravitational wave bursts (GWBs) of a given canonical family
(e.g. core-collapse supernova waveforms) from typical transient instrumental
artifacts (glitches), in the data of a single detector. The further
classification of glitches into typical sets is explored.In order to provide a
proof of concept,we use the core-collapse supernova waveform catalog produced
by H. Dimmelmeier and co-Workers, and the data base of glitches observed in
laser interferometer gravitational wave observatory (LIGO) data maintained by
P. Saulson and co-Workers to construct datasets of (windowed) transient
waveforms (glitches and bursts) in additive (Gaussian and compound-Gaussian)
noise with different signal-tonoise ratios (SNR). Principal component analysis
(PCA) is next implemented for reducing data dimensionality, yielding results
consistent with, and extending those in the literature. Then, a multilayer
perceptron is trained by a backpropagation algorithm (MLP-BP) on a data subset,
and used to classify the transients as glitch or burst. A Self-Organizing Map
(SOM) architecture is finally used to classify the glitches. The glitch/burst
discrimination and glitch classification abilities are gauged in terms of the
related truth tables. Preliminary results suggest that the approach is
effective and robust throughout the SNR range of practical interest.
Perspective applications pertain both to distributed (network, multisensor)
detection of GWBs, where someintelligenceat the single node level can be
introduced, and instrument diagnostics/optimization, where spurious transients
can be identified, classified and hopefully traced back to their entry point
Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity
Using a large database (~ 215 000 records) of relevant articles, we
empirically study the "complex systems" field and its claims to find universal
principles applying to systems in general. The study of references shared by
the papers allows us to obtain a global point of view on the structure of this
highly interdisciplinary field. We show that its overall coherence does not
arise from a universal theory but instead from computational techniques and
fruitful adaptations of the idea of self-organization to specific systems. We
also find that communication between different disciplines goes through
specific "trading zones", ie sub-communities that create an interface around
specific tools (a DNA microchip) or concepts (a network).Comment: Journal of the American Society for Information Science and
Technology (2012) 10.1002/asi.2264
Computational physics of the mind
In the XIX century and earlier such physicists as Newton, Mayer, Hooke, Helmholtz and Mach were actively engaged in the research on psychophysics, trying to relate psychological sensations to intensities of physical stimuli. Computational physics allows to simulate complex neural processes giving a chance to answer not only the original psychophysical questions but also to create models of mind. In this paper several approaches relevant to modeling of mind are outlined. Since direct modeling of the brain functions is rather limited due to the complexity of such models a number of approximations is introduced. The path from the brain, or computational neurosciences, to the mind, or cognitive sciences, is sketched, with emphasis on higher cognitive functions such as memory and consciousness. No fundamental problems in understanding of the mind seem to arise. From computational point of view realistic models require massively parallel architectures
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