168,365 research outputs found
Tensor Analysis and Fusion of Multimodal Brain Images
Current high-throughput data acquisition technologies probe dynamical systems
with different imaging modalities, generating massive data sets at different
spatial and temporal resolutions posing challenging problems in multimodal data
fusion. A case in point is the attempt to parse out the brain structures and
networks that underpin human cognitive processes by analysis of different
neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the
multimodal, multi-scale nature of neuroimaging data is well reflected by a
multi-way (tensor) structure where the underlying processes can be summarized
by a relatively small number of components or "atoms". We introduce
Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network
notation in order to analyze these models. These diagrams not only clarify
matrix and tensor EEG and fMRI time/frequency analysis and inverse problems,
but also help understand multimodal fusion via Multiway Partial Least Squares
and Coupled Matrix-Tensor Factorization. We show here, for the first time, that
Granger causal analysis of brain networks is a tensor regression problem, thus
allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI
recordings shows the potential of the methods and suggests their use in other
scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE
Spectroscopic monitoring of the Herbig Ae star HD 104237. II. Non-radial pulsations, mode analysis and fundamental stellar parameters
Herbig Ae/Be stars are intermediate-mass pre-main sequence (PMS) stars
showing signs of intense activity and strong stellar winds, whose origin is not
yet understood in the frame of current theoretical models of stellar evolution
for young stars. The evolutionary tracks of the earlier Herbig Ae stars cross a
recently discovered PMS instability strip. Many of these stars exhibit
pulsations of delta Scuti type. HD 104237 is a well-known pulsating Herbig Ae
star. In this article, we reinvestigated an extensive high-resolution
quasi-continuous spectroscopic data set in order to search for very faint
indications of non-radial pulsations in the line profile. To do this, we worked
on dynamical spectra of equivalent photospheric (LSD) profiles of HD 104237. A
2D Fourier analysis (F2D) was performed of the entire profile and the temporal
variation of the central depth of the line was studied with the time-series
analysis tools Period04 and SigSpec. We present a mode identification
corresponding to the detected dominant frequency. We perform a new accurate
determination of the fundamental stellar parameters in view of a forthcoming
asteroseismic modeling. Following the previous studies on this star, our
analysis of the dynamical spectrum of recentered LSD profiles corresponding to
the 22nd -25th of April 1999 nights spectra has confirmed the presence of
multiple oscillation modes of low-degree l in HD 104237 and led to the first
direct detection of a non-radial pulsation mode in this star: the dominant mode
F1 was identified by the Fourier 2D method having a degree l value comprised
between 1 and 2, the symmetry of the pattern variation indicating an azimuthal
order of +1 or -1. The detailed study of the fundamental stellar parameters has
provided a Teff, log g and iron abundance of 8550 +/- 150K, 3.9 +/- 0.3 and
-4.38 +/- 0.19 (i.e. [Fe/H]=+0.16 +/- 0.19), respectively
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The Dynamics of Periodically Forced Systems
The classical and quantum dynamics of a one-dimensional atomic system perturbed by a periodic electric field of frequency, Ω, in the regimes of high and low field frequency is studied.
At high frequencies various ionisation mechanisms are considered in both dynamics. We show that for systems having analytic potentials, and for sufficiently high frequencies, the classical system can ionize through regular orbits, in contradistinction to the driven Coulomb system.
An area-preserving map is constructed which approximates the classical motion well at high frequencies; explicit quantization of this map, in terms of the Fourier components of the classical motion, provides a very efficient means of obtaining approximate solutions to the one-dimensional, time-dependent Schrödinger equation. The Morse oscillator is considered in detail: the classical map is found to agree well with the numerical solution of Hamilton’s equations. Classical and quantal ionization probabilities are compared and circumstances delineated where they agree.
Comparisons of various theoretical models with experimental data for the ionization of excited hydrogen atoms in low frequency microwave fields arc used to distinguish between tunnelling through and classical escape over the slowly oscillating barrier and between one- and many-state dynamical processes. Formulae used to interpret low frequency laser multi-photon ionization data are found not to describe the experimental data which are best reproduced by the new semiclassical model presented here. Ranges of validity of other models are delineated.
A new analytic approximation for the solutions of the two-state equations of motion is obtained and used to predict the positions and widths of each member of the infinite set of resonances between any finite value of Ω and 0. This analysis shows why recent experiments on the microwave ionization of hydrogen atoms by low frequency fields failed to observe any resonances
Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.
The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior
Methods to assess binocular rivalry with periodic stimuli
This is the final version. Available on open access from SpringerOpen via the DOI in this recordAvailability of data and materials:
Source code for the model is available in the GitHub repository farzaneh-darki/Darki2020_methods: https://github.com/farzaneh-darki/Darki2020_methods.Binocular rivalry occurs when the two eyes are presented with incompatible stimuli and perception alternates between these two stimuli. This phenomenon has been investigated in two types of experiments: (1) Traditional experiments where the stimulus is fixed, (2) eye-swap experiments in which the stimulus periodically swaps between eyes many times per second (Logothetis et al. in Nature 380(6575):621–624, 1996). In spite of the rapid swapping between eyes, perception can be stable for many seconds with specific stimulus parameter configurations. Wilson introduced a two-stage, hierarchical model to explain both types of experiments (Wilson in Proc. Natl. Acad. Sci. 100(24):14499–14503, 2003). Wilson’s model and other rivalry models have been only studied with bifurcation analysis for fixed inputs and different types of dynamical behavior that can occur with periodically forcing inputs have not been investigated. Here we report (1) a more complete description of the complex dynamics in the unforced Wilson model, (2) a bifurcation analysis with periodic forcing. Previously, bifurcation analysis of the Wilson model with fixed inputs has revealed three main types of dynamical behaviors: Winner-takes-all (WTA), Rivalry oscillations (RIV), Simultaneous activity (SIM). Our results have revealed richer dynamics including mixed-mode oscillations (MMOs) and a period-doubling cascade, which corresponds to low-amplitude WTA (LAWTA) oscillations. On the other hand, studying rivalry models with numerical continuation shows that periodic forcing with high frequency (e.g. 18 Hz, known as flicker) modulates the three main types of behaviors that occur with fixed inputs with forcing frequency (WTA-Mod, RIV-Mod, SIM-Mod). However, dynamical behavior will be different with low frequency periodic forcing (around 1.5 Hz, so-called swap). In addition to WTA-Mod and SIM-Mod, cycle skipping, multi-cycle skipping and chaotic dynamics are found. This research provides a framework for either assessing binocular rivalry models to check consistency with empirical results, or for better understanding neural dynamics and mechanisms necessary to implement a minimal binocular rivalry model.Engineering and Physical Sciences Research Council (EPSRC
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