298 research outputs found
Neuronal avalanches recorded in the awake and sleeping monkey do not show a power law but can be reproduced by a self-organized critical model
Poster presentation: Self-organized critical (SOC) systems are complex dynamical systems that may express cascades of events, called avalanches [1]. The SOC state was proposed to govern brain function, because of its activity fluctuations over many orders of magnitude, its sensitivity to small input and its long term stability [2,3]. In addition, the critical state is optimal for information storage and processing [4]. Both hallmark features of SOC systems, a power law distribution f(s) for the avalanche size s and a branching parameter (bp) of unity, were found for neuronal avalanches recorded in vitro [5]. However, recordings in vivo yielded contradictory results [6]. Electrophysiological recordings in vivo only cover a small fraction of the brain, while criticality analysis assumes that the complete system is sampled. We hypothesized that spatial subsampling might influence the observed avalanche statistics. In addition, SOC models can have different connectivity, but always show a power law for f(s) and bp = 1 when fully sampled. This may not be the case under subsampling, however. Here, we wanted to know whether a state change from awake to asleep could be modeled by changing the connectivity of a SOC model without leaving the critical state. We simulated a SOC model [1] and calculated f(s) and bp obtained from sampling only the activity of a set of 4 Ă— 4 sites, representing the electrode positions in the cortex. We compared these results with results obtained from multielectrode recordings of local field potentials (LFP) in the cortex of behaving monkeys. We calculated f(s) and bp for the LFP activity recorded while the monkey was either awake or asleep and compared these results to results obtained from two subsampled SOC model with different connectivity. f(s) and bp were very similar for both the experiments and the subsampled SOC model, but in contrast to the fully sampled model, f(s) did not show a power law and bp was smaller than unity. With increasing the distance between the sampling sites, f(s) changed from "apparently supercritical" to "apparently subcritical" distributions in both the model and the LFP data. f(s) and bp calculated from LFP recorded during awake and asleep differed. These changes could be explained by altering the connectivity in the SOC model. Our results show that subsampling can prevent the observation of the characteristic power law and bp in SOC systems, and misclassifications of critical systems as sub- or supercritical are possible. In addition, a change in f(s) and bp for different states (awake/asleep) does not necessarily imply a change from criticality to sub- or supercriticality, but can also be explained by a change in the effective connectivity of the network without leaving the critical state
Emergent complex neural dynamics
A large repertoire of spatiotemporal activity patterns in the brain is the
basis for adaptive behaviour. Understanding the mechanism by which the brain's
hundred billion neurons and hundred trillion synapses manage to produce such a
range of cortical configurations in a flexible manner remains a fundamental
problem in neuroscience. One plausible solution is the involvement of universal
mechanisms of emergent complex phenomena evident in dynamical systems poised
near a critical point of a second-order phase transition. We review recent
theoretical and empirical results supporting the notion that the brain is
naturally poised near criticality, as well as its implications for better
understanding of the brain
Holographic three-point functions of giant gravitons
Working within the AdS/CFT correspondence we calculate the three-point
function of two giant gravitons and one pointlike graviton using methods of
semiclassical string theory and considering both the case where the giant
gravitons wrap an S^3 in S^5 and the case where the giant gravitons wrap an S^3
in AdS_5. We likewise calculate the correlation function in N=4 SYM using two
Schur polynomials and a single trace chiral primary. We find that the gauge and
string theory results have structural similarities but do not match perfectly,
and interpret this in terms of the Schur polynomials' inability to interpolate
between dual giant and pointlike gravitons.Comment: 21 page
Unified Image and Video Saliency Modeling
Visual saliency modeling for images and videos is treated as two independent
tasks in recent computer vision literature. While image saliency modeling is a
well-studied problem and progress on benchmarks like SALICON and MIT300 is
slowing, video saliency models have shown rapid gains on the recent DHF1K
benchmark. Here, we take a step back and ask: Can image and video saliency
modeling be approached via a unified model, with mutual benefit? We identify
different sources of domain shift between image and video saliency data and
between different video saliency datasets as a key challenge for effective
joint modelling. To address this we propose four novel domain adaptation
techniques - Domain-Adaptive Priors, Domain-Adaptive Fusion, Domain-Adaptive
Smoothing and Bypass-RNN - in addition to an improved formulation of learned
Gaussian priors. We integrate these techniques into a simple and lightweight
encoder-RNN-decoder-style network, UNISAL, and train it jointly with image and
video saliency data. We evaluate our method on the video saliency datasets
DHF1K, Hollywood-2 and UCF-Sports, and the image saliency datasets SALICON and
MIT300. With one set of parameters, UNISAL achieves state-of-the-art
performance on all video saliency datasets and is on par with the
state-of-the-art for image saliency datasets, despite faster runtime and a 5 to
20-fold smaller model size compared to all competing deep methods. We provide
retrospective analyses and ablation studies which confirm the importance of the
domain shift modeling. The code is available at
https://github.com/rdroste/unisalComment: Presented at the European Conference on Computer Vision (ECCV) 2020.
R. Droste and J. Jiao contributed equally to this work. v3: Updated Fig. 5a)
and added new MTI300 benchmark results to supp. materia
Correlation functions of three heavy operators - the AdS contribution
We consider operators in N=4 SYM theory which are dual, at strong coupling,
to classical strings rotating in S^5. Three point correlation functions of such
operators factorize into a universal contribution coming from the AdS part of
the string sigma model and a state-dependent S^5 contribution. Consequently a
similar factorization arises for the OPE coefficients. In this paper we
evaluate the AdS universal factor of the OPE coefficients which is explicitly
expressed just in terms of the anomalous dimensions of the three operators.Comment: 49 pages, 3 figures; v.2 references corrected; v3: corrected
discussion in section 5, results unchange
Cerebellar Integrity in the Amyotrophic Lateral Sclerosis - Frontotemporal Dementia Continuum
Amyotrophic lateral sclerosis (ALS) and behavioural variant frontotemporal dementia (bvFTD) are multisystem neurodegenerative disorders that manifest overlapping cognitive, neuropsychiatric and motor features. The cerebellum has long been known to be crucial for intact motor function although emerging evidence over the past decade has attributed cognitive and neuropsychiatric processes to this structure. The current study set out i) to establish the integrity of cerebellar subregions in the amyotrophic lateral sclerosis-behavioural variant frontotemporal dementia spectrum (ALS-bvFTD) and ii) determine whether specific cerebellar atrophy regions are associated with cognitive, neuropsychiatric and motor symptoms in the patients. Seventy-eight patients diagnosed with ALS, ALS-bvFTD, behavioural variant frontotemporal dementia (bvFTD), most without C9ORF72 gene abnormalities, and healthy controls were investigated. Participants underwent cognitive, neuropsychiatric and functional evaluation as well as structural imaging using voxel-based morphometry (VBM) to examine the grey matter subregions of the cerebellar lobules, vermis and crus. VBM analyses revealed: i) significant grey matter atrophy in the cerebellum across the whole ALS-bvFTD continuum; ii) atrophy predominantly of the superior cerebellum and crus in bvFTD patients, atrophy of the inferior cerebellum and vermis in ALS patients, while ALS-bvFTD patients had both patterns of atrophy. Post-hoc covariance analyses revealed that cognitive and neuropsychiatric symptoms were particularly associated with atrophy of the crus and superior lobule, while motor symptoms were more associated with atrophy of the inferior lobules. Taken together, these findings indicate an important role of the cerebellum in the ALS-bvFTD disease spectrum, with all three clinical phenotypes demonstrating specific patterns of subregional atrophy that associated with different symptomology
Astrobiological Complexity with Probabilistic Cellular Automata
Search for extraterrestrial life and intelligence constitutes one of the
major endeavors in science, but has yet been quantitatively modeled only rarely
and in a cursory and superficial fashion. We argue that probabilistic cellular
automata (PCA) represent the best quantitative framework for modeling
astrobiological history of the Milky Way and its Galactic Habitable Zone. The
relevant astrobiological parameters are to be modeled as the elements of the
input probability matrix for the PCA kernel. With the underlying simplicity of
the cellular automata constructs, this approach enables a quick analysis of
large and ambiguous input parameters' space. We perform a simple clustering
analysis of typical astrobiological histories and discuss the relevant boundary
conditions of practical importance for planning and guiding actual empirical
astrobiological and SETI projects. In addition to showing how the present
framework is adaptable to more complex situations and updated observational
databases from current and near-future space missions, we demonstrate how
numerical results could offer a cautious rationale for continuation of
practical SETI searches.Comment: 37 pages, 11 figures, 2 tables; added journal reference belo
State-space Manifold and Rotating Black Holes
We study a class of fluctuating higher dimensional black hole configurations
obtained in string theory/ -theory compactifications. We explore the
intrinsic Riemannian geometric nature of Gaussian fluctuations arising from the
Hessian of the coarse graining entropy, defined over an ensemble of brane
microstates. It has been shown that the state-space geometry spanned by the set
of invariant parameters is non-degenerate, regular and has a negative scalar
curvature for the rotating Myers-Perry black holes, Kaluza-Klein black holes,
supersymmetric black holes, - configurations and the
associated BMPV black holes. Interestingly, these solutions demonstrate that
the principal components of the state-space metric tensor admit a positive
definite form, while the off diagonal components do not. Furthermore, the ratio
of diagonal components weakens relatively faster than the off diagonal
components, and thus they swiftly come into an equilibrium statistical
configuration. Novel aspects of the scaling property suggest that the
brane-brane statistical pair correlation functions divulge an asymmetric
nature, in comparison with the others. This approach indicates that all above
configurations are effectively attractive and stable, on an arbitrary
hyper-surface of the state-space manifolds. It is nevertheless noticed that
there exists an intriguing relationship between non-ideal inter-brane
statistical interactions and phase transitions. The ramifications thus
described are consistent with the existing picture of the microscopic CFTs. We
conclude with an extended discussion of the implications of this work for the
physics of black holes in string theory.Comment: 44 pages, Keywords: Rotating Black Holes; State-space Geometry;
Statistical Configurations, String Theory, M-Theory. PACS numbers: 04.70.-s
Physics of black holes; 04.70.Bw Classical black holes; 04.70.Dy Quantum
aspects of black holes, evaporation, thermodynamics; 04.50.Gh
Higher-dimensional black holes, black strings, and related objects. Edited
the bibliograph
Boolean Dynamics with Random Couplings
This paper reviews a class of generic dissipative dynamical systems called
N-K models. In these models, the dynamics of N elements, defined as Boolean
variables, develop step by step, clocked by a discrete time variable. Each of
the N Boolean elements at a given time is given a value which depends upon K
elements in the previous time step.
We review the work of many authors on the behavior of the models, looking
particularly at the structure and lengths of their cycles, the sizes of their
basins of attraction, and the flow of information through the systems. In the
limit of infinite N, there is a phase transition between a chaotic and an
ordered phase, with a critical phase in between.
We argue that the behavior of this system depends significantly on the
topology of the network connections. If the elements are placed upon a lattice
with dimension d, the system shows correlations related to the standard
percolation or directed percolation phase transition on such a lattice. On the
other hand, a very different behavior is seen in the Kauffman net in which all
spins are equally likely to be coupled to a given spin. In this situation,
coupling loops are mostly suppressed, and the behavior of the system is much
more like that of a mean field theory.
We also describe possible applications of the models to, for example, genetic
networks, cell differentiation, evolution, democracy in social systems and
neural networks.Comment: 69 pages, 16 figures, Submitted to Springer Applied Mathematical
Sciences Serie
Subsampling effects in neuronal avalanche distributions recorded in vivo
Background Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s) of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s), and sigma=1, are hallmark features of self-organized critical (SOC) systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s) and sigma by imposing subsampling on three different SOC models. We then compared f(s) and sigma of the subsampled models with those of multielectrode local field potential (LFP) activity recorded in three macaque monkeys performing a short term memory task. Results Neither the LFP nor the subsampled SOC models showed a power law for f(s). Both, f(s) and sigma, depended sensitively on the subsampling geometry and the dynamics of the model. Only one of the SOC models, the Abelian Sandpile Model, exhibited f(s) and sigma similar to those calculated from LFP activity. Conclusions Since subsampling can prevent the observation of the characteristic power law and sigma in SOC systems, misclassifications of critical systems as sub- or supercritical are possible. Nevertheless, the system specific scaling of f(s) and sigma under subsampling conditions may prove useful to select physiologically motivated models of brain function. Models that better reproduce f(s) and sigma calculated from the physiological recordings may be selected over alternatives
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