Application of self-organizing competition artificial neural network to logging data explanation of sandstone-hosted uranium deposits

International Nuclear Information System (INIS)

Xu Jianguo; Xu Xianli; Wang Weiguo

2008-01-01

The article describes the model construction of self-organizing competition artificial neural network, its principle and automatic recognition process of borehole lithology in detail, and then proves the efficiency of the neural network model for automatically recognizing the borehole lithology with some cases. The self-organizing competition artificial neural network has the ability of self- organization, self-adjustment and high permitting errors. Compared with the BP algorithm, it takes less calculation quantity and more rapidly converges. Furthermore, it can automatically confirm the category without the known sample information. Trial results based on contrasting the identification results of the borehole lithology with geological documentations, indicate that self-organizing artificial neural network can be well applied to automatically performing the category of borehole lithology, during the logging data explanation of sandstone-hosted uranium deposits. (authors)

Self-organized critical pinball machine

DEFF Research Database (Denmark)

Flyvbjerg, H.

2004-01-01

The nature of self-organized criticality (SOC) is pin-pointed with a simple mechanical model: a pinball machine. Its phase space is fully parameterized by two integer variables, one describing the state of an on-going game, the other describing the state of the machine. This is the simplest...

Extending Particle Swarm Optimisers with Self-Organized Criticality

DEFF Research Database (Denmark)

Løvbjerg, Morten; Krink, Thiemo

2002-01-01

Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions.......Particle swarm optimisers (PSOs) show potential in function optimisation, but still have room for improvement. Self-organized criticality (SOC) can help control the PSO and add diversity. Extending the PSO with SOC seems promising reaching faster convergence and better solutions....

Self: an adaptive pressure arising from self-organization, chaotic dynamics, and neural Darwinism.

Science.gov (United States)

Bruzzo, Angela Alessia; Vimal, Ram Lakhan Pandey

2007-12-01

In this article, we establish a model to delineate the emergence of "self" in the brain making recourse to the theory of chaos. Self is considered as the subjective experience of a subject. As essential ingredients of subjective experiences, our model includes wakefulness, re-entry, attention, memory, and proto-experiences. The stability as stated by chaos theory can potentially describe the non-linear function of "self" as sensitive to initial conditions and can characterize it as underlying order from apparently random signals. Self-similarity is discussed as a latent menace of a pathological confusion between "self" and "others". Our test hypothesis is that (1) consciousness might have emerged and evolved from a primordial potential or proto-experience in matter, such as the physical attractions and repulsions experienced by electrons, and (2) "self" arises from chaotic dynamics, self-organization and selective mechanisms during ontogenesis, while emerging post-ontogenically as an adaptive pressure driven by both volume and synaptic-neural transmission and influencing the functional connectivity of neural nets (structure).

On self-organized criticality in nonconserving systems

International Nuclear Information System (INIS)

Socolar, J.E.S.; Grinstein, G.; Jayaprakash, C.

1993-01-01

Two models with nonconserving dynamics and slow continuous deterministic driving, a stick-slip model (SSM) of earthquake dynamics and a toy forest-fire model (FFM), have recently been argued to show numerical evidence of self-organized criticality (generic, scale-invariant steady states). To determine whether the observed criticality is indeed generic, we study these models as a function of a parameter γ which was implicitly tuned to a special value, γ=1, in their original definitions. In both cases, the maximum Lyapunov exponent vanishes at γ=1. We find that the FFM does not exhibit self-organized criticality for any γ, including γ=1; nor does the SSM with periodic boundary conditions. Both models show evidence of macroscopic periodic oscillations in time for some range of γ values. We suggest that such oscillations may provide a mechanism for the generation of scale-invariant structure in nonconserving systems, and, in particular, that they underlie the criticality previously observed in the SSM with open boundary conditions

SORN: a self-organizing recurrent neural network

Directory of Open Access Journals (Sweden)

Andreea Lazar

2009-10-01

Full Text Available Understanding the dynamics of recurrent neural networks is crucial for explaining how the brain processes information. In the neocortex, a range of different plasticity mechanisms are shaping recurrent networks into effective information processing circuits that learn appropriate representations for time-varying sensory stimuli. However, it has been difficult to mimic these abilities in artificial neural network models. Here we introduce SORN, a self-organizing recurrent network. It combines three distinct forms of local plasticity to learn spatio-temporal patterns in its input while maintaining its dynamics in a healthy regime suitable for learning. The SORN learns to encode information in the form of trajectories through its high-dimensional state space reminiscent of recent biological findings on cortical coding. All three forms of plasticity are shown to be essential for the network's success.

A self-organized criticality model for plasma transport

International Nuclear Information System (INIS)

Carreras, B.A.; Newman, D.; Lynch, V.E.

1996-01-01

Many models of natural phenomena manifest the basic hypothesis of self-organized criticality (SOC). The SOC concept brings together the self-similarity on space and time scales that is common to many of these phenomena. The application of the SOC modelling concept to the plasma dynamics near marginal stability opens new possibilities of understanding issues such as Bohm scaling, profile consistency, broad band fluctuation spectra with universal characteristics and fast time scales. A model realization of self-organized criticality for plasma transport in a magnetic confinement device is presented. The model is based on subcritical resistive pressure-gradient-driven turbulence. Three-dimensional nonlinear calculations based on this model show the existence of transport under subcritical conditions. This model that includes fluctuation dynamics leads to results very similar to the running sandpile paradigm

Image Fusion Based on the Self-Organizing Feature Map Neural Networks

Institute of Scientific and Technical Information of China (English)

ZHANG Zhaoli; SUN Shenghe

2001-01-01

This paper presents a new image datafusion scheme based on the self-organizing featuremap (SOFM) neural networks.The scheme consists ofthree steps:(1) pre-processing of the images,whereweighted median filtering removes part of the noisecomponents corrupting the image,(2) pixel clusteringfor each image using two-dimensional self-organizingfeature map neural networks,and (3) fusion of the im-ages obtained in Step (2) utilizing fuzzy logic,whichsuppresses the residual noise components and thusfurther improves the image quality.It proves thatsuch a three-step combination offers an impressive ef-fectiveness and performance improvement,which isconfirmed by simulations involving three image sen-sors (each of which has a different noise structure).

Nonlinear dynamics analysis of a self-organizing recurrent neural network: chaos waning.

Science.gov (United States)

Eser, Jürgen; Zheng, Pengsheng; Triesch, Jochen

2014-01-01

Self-organization is thought to play an important role in structuring nervous systems. It frequently arises as a consequence of plasticity mechanisms in neural networks: connectivity determines network dynamics which in turn feed back on network structure through various forms of plasticity. Recently, self-organizing recurrent neural network models (SORNs) have been shown to learn non-trivial structure in their inputs and to reproduce the experimentally observed statistics and fluctuations of synaptic connection strengths in cortex and hippocampus. However, the dynamics in these networks and how they change with network evolution are still poorly understood. Here we investigate the degree of chaos in SORNs by studying how the networks' self-organization changes their response to small perturbations. We study the effect of perturbations to the excitatory-to-excitatory weight matrix on connection strengths and on unit activities. We find that the network dynamics, characterized by an estimate of the maximum Lyapunov exponent, becomes less chaotic during its self-organization, developing into a regime where only few perturbations become amplified. We also find that due to the mixing of discrete and (quasi-)continuous variables in SORNs, small perturbations to the synaptic weights may become amplified only after a substantial delay, a phenomenon we propose to call deferred chaos.

Morphological self-organizing feature map neural network with applications to automatic target recognition

Science.gov (United States)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Self-organized criticality as a paradigm for transport processes in magnetically confined plasma

International Nuclear Information System (INIS)

Karreras, B.A.; N'yuman, D.; Linch, V.E.

1996-01-01

Many models of natural events prove the basic hypotheses of self-organized critically. The concept on self-organized criticality combines self similarity on a spatial and time scale, characteristic of many such events. Application of the self-organized criticality concept to plasma dynamics close to the stability limit opens new possibilities for comprehension of such events as the Bom scaling, profile selfconsistency, wide band fluctuation spectra with universal characteristics and small time scales. Refs. 51, figs. 17

Self-Organized Criticality of Rainfall in Central China

Directory of Open Access Journals (Sweden)

Zhiliang Wang

2012-01-01

Full Text Available Rainfall is a complexity dynamics process. In this paper, our objective is to find the evidence of self-organized criticality (SOC for rain datasets in China by employing the theory and method of SOC. For this reason, we analyzed the long-term rain records of five meteorological stations in Henan, a central province of China. Three concepts, that is, rain duration, drought duration, accumulated rain amount, are proposed to characterize these rain events processes. We investigate their dynamics property by using scale invariant and found that the long-term rain processes in central China indeed exhibit the feature of self-organized criticality. The proposed theory and method may be suitable to analyze other datasets from different climate zones in China.

Self-organization of the critical state in Josephson lattices and granulated superconductors

International Nuclear Information System (INIS)

Ginzburg, S.L.

1994-01-01

A number of models of a Josephson medium and granulated superconductors are studied. It is shown that an important parameter is the quantity V∼j c a 3 /Φ 0 , where j c is the Josephson-current density, a is the granule size, and Φ 0 is the quantum of flux. In the limit V>>1 the continuum approximation is inapplicable. In this case the Josephson medium is transformed into a system in which pinning is realized on elementary loops that incorporate Josephson junctions. Here, nonlinear properties of these junctions obtain. The equations obtained for the currents of the Josephson lattice are identical to the standard formulation in the problem of self-organized criticality, while in granulated superconductors a problem of self-organized criticality with a different symmetry arises-a problem not of sites, but of loop. From the point of view of the critical state in granulated superconductors the concept of self-organized criticality radically changes the entire customary picture. The usual equations of the critical state describe only the average values of the magnetic field in the hydrodynamic approximation. However, it follows from the concept of self-organized criticality that the critical state has an extremely complicated structure, much more complicated than that which follows from the equation of the critical state. In particular, the fluctuations of various quantities in the critical state are much stronger than the ordinary statistical fluctuations, since there are large-scale fluctuations of the currents and fields, with a power-law (scaling) behavior that extends up to scales of the order of the size of the system, as in a turbulent medium. On the other hand, the basic equations in it reflect all the features of pinning - hysteresis and threshold behavior. Therefore, the self-organization of the critical state of a superconductor is a natural realization of this extremely general problem. 15 refs., 4 figs

RM-SORN: a reward-modulated self-organizing recurrent neural network.

Science.gov (United States)

Aswolinskiy, Witali; Pipa, Gordon

2015-01-01

Neural plasticity plays an important role in learning and memory. Reward-modulation of plasticity offers an explanation for the ability of the brain to adapt its neural activity to achieve a rewarded goal. Here, we define a neural network model that learns through the interaction of Intrinsic Plasticity (IP) and reward-modulated Spike-Timing-Dependent Plasticity (STDP). IP enables the network to explore possible output sequences and STDP, modulated by reward, reinforces the creation of the rewarded output sequences. The model is tested on tasks for prediction, recall, non-linear computation, pattern recognition, and sequence generation. It achieves performance comparable to networks trained with supervised learning, while using simple, biologically motivated plasticity rules, and rewarding strategies. The results confirm the importance of investigating the interaction of several plasticity rules in the context of reward-modulated learning and whether reward-modulated self-organization can explain the amazing capabilities of the brain.

Workplace Accidents and Self-Organized Criticality

OpenAIRE

Mauro, John C.; Diehl, Brett; Marcellin, Richard F.; Vaughn, Daniel J.

2018-01-01

The occurrence of workplace accidents is described within the context of self-organized criticality, a theory from statistical physics that governs a wide range of phenomena across physics, biology, geosciences, economics, and the social sciences. Workplace accident data from the U.S. Bureau of Labor Statistics reveal a power-law relationship between the number of accidents and their severity as measured by the number of days lost from work. This power-law scaling is indicative of workplace a...

Cinematic Operation of the Cerebral Cortex Interpreted via Critical Transitions in Self-Organized Dynamic Systems.

Science.gov (United States)

Kozma, Robert; Freeman, Walter J

2017-01-01

Measurements of local field potentials over the cortical surface and the scalp of animals and human subjects reveal intermittent bursts of beta and gamma oscillations. During the bursts, narrow-band metastable amplitude modulation (AM) patters emerge for a fraction of a second and ultimately dissolve to the broad-band random background activity. The burst process depends on previously learnt conditioned stimuli (CS), thus different AM patterns may emerge in response to different CS. This observation leads to our cinematic theory of cognition when perception happens in discrete steps manifested in the sequence of AM patterns. Our article summarizes findings in the past decades on experimental evidence of cinematic theory of cognition and relevant mathematical models. We treat cortices as dissipative systems that self-organize themselves near a critical level of activity that is a non-equilibrium metastable state. Criticality is arguably a key aspect of brains in their rapid adaptation, reconfiguration, high storage capacity, and sensitive response to external stimuli. Self-organized criticality (SOC) became an important concept to describe neural systems. We argue that transitions from one AM pattern to the other require the concept of phase transitions, extending beyond the dynamics described by SOC. We employ random graph theory (RGT) and percolation dynamics as fundamental mathematical approaches to model fluctuations in the cortical tissue. Our results indicate that perceptions are formed through a phase transition from a disorganized (high entropy) to a well-organized (low entropy) state, which explains the swiftness of the emergence of the perceptual experience in response to learned stimuli.

Lifelong learning of human actions with deep neural network self-organization.

Science.gov (United States)

Parisi, German I; Tani, Jun; Weber, Cornelius; Wermter, Stefan

2017-12-01

Lifelong learning is fundamental in autonomous robotics for the acquisition and fine-tuning of knowledge through experience. However, conventional deep neural models for action recognition from videos do not account for lifelong learning but rather learn a batch of training data with a predefined number of action classes and samples. Thus, there is the need to develop learning systems with the ability to incrementally process available perceptual cues and to adapt their responses over time. We propose a self-organizing neural architecture for incrementally learning to classify human actions from video sequences. The architecture comprises growing self-organizing networks equipped with recurrent neurons for processing time-varying patterns. We use a set of hierarchically arranged recurrent networks for the unsupervised learning of action representations with increasingly large spatiotemporal receptive fields. Lifelong learning is achieved in terms of prediction-driven neural dynamics in which the growth and the adaptation of the recurrent networks are driven by their capability to reconstruct temporally ordered input sequences. Experimental results on a classification task using two action benchmark datasets show that our model is competitive with state-of-the-art methods for batch learning also when a significant number of sample labels are missing or corrupted during training sessions. Additional experiments show the ability of our model to adapt to non-stationary input avoiding catastrophic interference. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Self-organized dynamical complexity in human wakefulness and sleep: Different critical brain-activity feedback for conscious and unconscious states

Science.gov (United States)

Allegrini, Paolo; Paradisi, Paolo; Menicucci, Danilo; Laurino, Marco; Piarulli, Andrea; Gemignani, Angelo

2015-09-01

Criticality reportedly describes brain dynamics. The main critical feature is the presence of scale-free neural avalanches, whose auto-organization is determined by a critical branching ratio of neural-excitation spreading. Other features, directly associated to second-order phase transitions, are: (i) scale-free-network topology of functional connectivity, stemming from suprathreshold pairwise correlations, superimposable, in waking brain activity, with that of ferromagnets at Curie temperature; (ii) temporal long-range memory associated to renewal intermittency driven by abrupt fluctuations in the order parameters, detectable in human brain via spatially distributed phase or amplitude changes in EEG activity. Herein we study intermittent events, extracted from 29 night EEG recordings, including presleep wakefulness and all phases of sleep, where different levels of mentation and consciousness are present. We show that while critical avalanching is unchanged, at least qualitatively, intermittency and functional connectivity, present during conscious phases (wakefulness and REM sleep), break down during both shallow and deep non-REM sleep. We provide a theory for fragmentation-induced intermittency breakdown and suggest that the main difference between conscious and unconscious states resides in the backwards causation, namely on the constraints that the emerging properties at large scale induce to the lower scales. In particular, while in conscious states this backwards causation induces a critical slowing down, preserving spatiotemporal correlations, in dreamless sleep we see a self-organized maintenance of moduli working in parallel. Critical avalanches are still present, and establish transient auto-organization, whose enhanced fluctuations are able to trigger sleep-protecting mechanisms that reinstate parallel activity. The plausible role of critical avalanches in dreamless sleep is to provide a rapid recovery of consciousness, if stimuli are highly arousing.

Self-organized criticality revisited: non-local transport by turbulent amplification

DEFF Research Database (Denmark)

Milovanov, Alexander V.; Rasmussen, Jens Juul

2015-01-01

We revise the applications of self-organized criticality (SOC) as a paradigmatic model for tokamak plasma turbulence. The work, presented here, is built around the idea that some systems do not develop a pure critical state associable with SOC, since their dynamical evolution involves as a compet......We revise the applications of self-organized criticality (SOC) as a paradigmatic model for tokamak plasma turbulence. The work, presented here, is built around the idea that some systems do not develop a pure critical state associable with SOC, since their dynamical evolution involves...... as a competing key factor an inverse cascade of the energy in reciprocal space. Then relaxation of slowly increasing stresses will give rise to intermittent bursts of transport in real space and outstanding transport events beyond the range of applicability of the 'conventional' SOC. Also, we are concerned...... with the causes and origins of non-local transport in magnetized plasma, and show that this type of transport occurs naturally in self-consistent strong turbulence via a complexity coupling to the inverse cascade. We expect these coupling phenomena to occur in the parameter range of strong nonlinearity and time...

Invertebrate diversity classification using self-organizing map neural network: with some special topological functions

Directory of Open Access Journals (Sweden)

WenJun Zhang

2014-06-01

Full Text Available In present study we used self-organizing map (SOM neural network to conduct the non-supervisory clustering of invertebrate orders in rice field. Four topological functions, i.e., cossintopf, sincostopf, acossintopf, and expsintopf, established on the template in toolbox of Matlab, were used in SOM neural network learning. Results showed that clusters were different when using different topological functions because different topological functions will generate different spatial structure of neurons in neural network. We may chose these functions and results based on comparison with the practical situation.

Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

International Nuclear Information System (INIS)

Peng Yafu; Hsu, C.-F.

2009-01-01

This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

The origin of power-law distributions in self-organized criticality

International Nuclear Information System (INIS)

Yang, C B

2004-01-01

The origin of power-law distributions in self-organized criticality is investigated by treating the variation of the number of active sites in the system as a stochastic process. An avalanche is then regarded as a first-return random-walk process in a one-dimensional lattice. Power-law distributions of the lifetime and spatial size are found when the random walk is unbiased with equal probability to move in opposite directions. This shows that power-law distributions in self-organized criticality may be caused by the balance of competitive interactions. At the mean time, the mean spatial size for avalanches with the same lifetime is found to increase in a power law with the lifetime. (letter to the editor)

Do earthquakes exhibit self-organized criticality?

International Nuclear Information System (INIS)

Yang Xiaosong; Ma Jin; Du Shuming

2004-01-01

If earthquakes are phenomena of self-organized criticality (SOC), statistical characteristics of the earthquake time series should be invariant after the sequence of events in an earthquake catalog are randomly rearranged. In this Letter we argue that earthquakes are unlikely phenomena of SOC because our analysis of the Southern California Earthquake Catalog shows that the first-return-time probability P M (T) is apparently changed after the time series is rearranged. This suggests that the SOC theory should not be used to oppose the efforts of earthquake prediction

How nature works the science of self-organized criticality

CERN Document Server

Bak, Per

1996-01-01

This is an acclaimed book intended for the general reader who is interested in science. The author is a physicist who is well-known for his development of the property called "self-organized criticality", a property or phenomenon that lies at the heart of large dynamical systems. It can be used to analyse systems that are complicated, and which are part of the new science of complexity. It is a unifying concept that can be used to study phenomena in fields as diverse as economics, astronomy, the earth sciences, and physics. The author discusses his discovery of self-organized criticality; its relation to the world of classical physics; computer simulations and experiments which aid scientists' understanding of the property; and the relation of the subject to popular areas such as fractal geometry and power laws; cellular automata, and a wide range of practical applications.

Organized versus self-organized criticality in the abelian sandpile model

OpenAIRE

Fey-den Boer, AC Anne; Redig, FHJ Frank

2005-01-01

We define stabilizability of an infinite volume height configuration and of a probability measure on height configurations. We show that for high enough densities, a probability measure cannot be stabilized. We also show that in some sense the thermodynamic limit of the uniform measures on the recurrent configurations of the abelian sandpile model (ASM) is a maximal element of the set of stabilizable measures. In that sense the self-organized critical behavior of the ASM can be understood in ...

Self-Organized Criticality and $1/f$ Noise in Traffic

OpenAIRE

Paczuski, Maya; Nagel, Kai

1996-01-01

Phantom traffic jams may emerge ``out of nowhere'' from small fluctuations rather than being triggered by large, exceptional events. We show how phantom jams arise in a model of single lane highway traffic, which mimics human driving behavior. Surprisingly, the optimal state of highest efficiency, with the largest throughput, is a critical state with traffic jams of all sizes. We demonstrate that open systems self-organize to the most efficient state. In the model we study, this critical stat...

A Data-Driven, Integrated Flare Model Based on Self-Organized Criticality

Science.gov (United States)

Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M.

2013-09-01

We interpret solar flares as events originating in solar active regions having reached the self-organized critical state, by alternatively using two versions of an "integrated flare model" - one static and one dynamic. In both versions the initial conditions are derived from observations aiming to investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. In the static model, we first apply a nonlinear force-free extrapolation that reconstructs the three-dimensional magnetic fields from two-dimensional vector magnetograms. We then locate magnetic discontinuities exceeding a threshold in the Laplacian of the magnetic field. These discontinuities are relaxed in local diffusion events, implemented in the form of cellular-automaton evolution rules. Subsequent loading and relaxation steps lead the system to self-organized criticality, after which the statistical properties of the simulated events are examined. In the dynamic version we deploy an enhanced driving mechanism, which utilizes the observed evolution of active regions, making use of sequential vector magnetograms. We first apply the static cellular automaton model to consecutive solar vector magnetograms until the self-organized critical state is reached. We then evolve the magnetic field inbetween these processed snapshots through spline interpolation, acting as a natural driver in the dynamic model. The identification of magnetically unstable sites as well as their relaxation follow the same rules as in the static model after each interpolation step. Subsequent interpolation/driving and relaxation steps cover all transitions until the end of the sequence. Physical requirements, such as the divergence-free condition for the magnetic field vector, are approximately satisfied in both versions of the model. We obtain robust power laws in the distribution functions of the modelled

A simple rank-based Markov chain with self-organized criticality

Czech Academy of Sciences Publication Activity Database

Swart, Jan M.

2017-01-01

Roč. 23, č. 1 (2017), s. 87-102 ISSN 1024-2953 R&D Projects: GA ČR GAP201/12/2613; GA ČR(CZ) GA15-08819S Institutional support: RVO:67985556 Keywords : self-reinforcement * self-organized criticality * canyon Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics Impact factor: 0.397, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/swart-0476009.pdf

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network.

Science.gov (United States)

Han, Hong-Gui; Zhang, Lu; Hou, Ying; Qiao, Jun-Fei

2016-02-01

A nonlinear model predictive control (NMPC) scheme is developed in this paper based on a self-organizing recurrent radial basis function (SR-RBF) neural network, whose structure and parameters are adjusted concurrently in the training process. The proposed SR-RBF neural network is represented in a general nonlinear form for predicting the future dynamic behaviors of nonlinear systems. To improve the modeling accuracy, a spiking-based growing and pruning algorithm and an adaptive learning algorithm are developed to tune the structure and parameters of the SR-RBF neural network, respectively. Meanwhile, for the control problem, an improved gradient method is utilized for the solution of the optimization problem in NMPC. The stability of the resulting control system is proved based on the Lyapunov stability theory. Finally, the proposed SR-RBF neural network-based NMPC (SR-RBF-NMPC) is used to control the dissolved oxygen (DO) concentration in a wastewater treatment process (WWTP). Comparisons with other existing methods demonstrate that the SR-RBF-NMPC can achieve a considerably better model fitting for WWTP and a better control performance for DO concentration.

Self-organized criticality paradigm

International Nuclear Information System (INIS)

Duran, I.; Stoeckel, J.; Hron, M.; Horacek, J.; Jakubka, K.; Kryska, L.

2000-01-01

According to the paradigm of the Self-Organized Criticality (SOC), the anomalous transport in tokamaks is caused by fast transient processes - avalanches. One of the manifestations of these phenomena should be 1/f decay of electrostatic fluctuations power spectra in a certain frequency range. In this paper, the frequency spectra of floating potential, density and fluctuation-induced flux, measured by poloidal and radial arrays of Langmuir probes on the CASTOR tokamak, are presented. The floating potential and the fluctuation-induced flux decay from 30 kHz up to 100 kHz as f -1 . The plasma density decays as f -1 in a more narrow band, 20 to 40 kHz. The possible limitation of SOC behavior for frequencies higher than 100 kHz due to intermittency is stressed. For this reason the Probability Distribution Functions (PDFs) of floating potential fluctuations were computed at different time scales using wavelet transform. A clear departure of the computed PDFs from Gaussianity, which is a classical signature of intermittency, is observed at time scales under 10 μs (100 kHz). (author)

Discerning Thermodynamic Basis of Self-Organization in Critical Zone Structure and Function

Science.gov (United States)

Richardson, M.; Kumar, P.

2017-12-01

Self-organization characterizes the spontaneous emergence of order. Self-organization in the Critical Zone, the region of Earth's skin from below the groundwater table to the top of the vegetation canopy, involves the interaction of biotic and abiotic processes occurring through a hierarchy of temporal and spatial scales. The self-organization is sustained through input of energy and material in an open system framework, and the resulting formations are called dissipative structures. Why do these local states of organization form and how are they thermodynamically favorable? We hypothesize that structure formation is linked to energy conversion and matter throughput rates across driving gradients. Furthermore, we predict that structures in the Critical Zone evolve based on local availability of nutrients, water, and energy. By considering ecosystems as open thermodynamic systems, we model and study the throughput signatures on short times scales to determine origins and characteristics of ecosystem structure. This diagnostic approach allows us to use fluxes of matter and energy to understand the thermodynamic drivers of the system. By classifying the fluxes and dynamics in a system, we can identify patterns to determine the thermodynamic drivers for organized states. Additionally, studying the partitioning of nutrients, water, and energy throughout ecosystems through dissipative structures will help identify reasons for structure shapes and how these shapes impact major Critical Zone functions.

Self-organized criticality and urban development

Directory of Open Access Journals (Sweden)

Michael Batty

1999-01-01

Full Text Available Urban society is undergoing as profound a spatial transformation as that associated with the emergence of the industrial city two centuries ago. To describe and measure this transition, we introduce a new theory based on the concept that large-scale, complex systems composed of many interacting elements, show a surprising degree of resilience to change, holding themselves at critical levels for long periods until conditions emerge which move the system, often abruptly, to a new threshold. This theory is called ‘self-organized criticality’; it is consistent with systems in which global patterns emerge from local action which is the hallmark of self-organization, and it is consistent with developments in system dynamics and their morphology which find expression in fractal geometry and weak chaos theory. We illustrate the theory using a unique space–time series of urban development for Buffalo, Western New York, which contains the locations of over one quarter of a million sites coded by their year of construction and dating back to 1773, some 60 years before the city began to develop. We measure the emergence and growth of the city using urban density functions from which measures of fractal dimension are used to construct growth paths of the way the city has grown to fill its region. These phase portraits suggest the existence of transitions between the frontier, the settled agricultural region, the centralized industrial city and the decentralized postindustrial city, and our analysis reveals that Buffalo has maintained itself at a critical threshold since the emergence of the automobile city some 70 years ago. Our implied speculation is: how long will this kind of urban form be maintained in the face of seemingly unstoppable technological change?

Self-organized critical model for protein folding

Science.gov (United States)

Moret, M. A.

2011-09-01

The major factor that drives a protein toward collapse and folding is the hydrophobic effect. At the folding process a hydrophobic core is shielded by the solvent-accessible surface area of the protein. We study the fractal behavior of 5526 protein structures present in the Brookhaven Protein Data Bank. Power laws of protein mass, volume and solvent-accessible surface area are measured independently. The present findings indicate that self-organized criticality is an alternative explanation for the protein folding. Also we note that the protein packing is an independent and constant value because the self-similar behavior of the volumes and protein masses have the same fractal dimension. This power law guarantees that a protein is a complex system. From the analyzed data, q-Gaussian distributions seem to fit well this class of systems.

Self-Organized Criticality and Mass Extinction in Evolutionary Algorithms

DEFF Research Database (Denmark)

Krink, Thiemo; Thomsen, Rene

2001-01-01

The gaps in the fossil record gave rise to the hypothesis that evolution proceeded in long periods of stasis, which alternated with occasional, rapid changes that yielded evolutionary progress. One mechanism that could cause these punctuated bursts is the re-colonbation of changing and deserted...... at a critical state between chaos and order, known as self-organized criticality (SOC). Based on this background, we used SOC to control the size of spatial extinction zones in a diffusion model. The SOC selection process was easy to implement and implied only negligible computational costs. Our results show...

Development of objective flow regime identification method using self-organizing neural network

International Nuclear Information System (INIS)

Lee, Jae Young; Kim, Nam Seok; Kwak, Nam Yee

2004-01-01

Two-phase flow shows various flow patterns according to the amount of the void and its relative velocity to the liquid flow. This variation directly affect the interfacial transfer which is the key factor for the design or analysis of the phase change systems. Especially the safety analysis of the nuclear power plant has been performed based on the numerical code furnished with the proper constitutive relations depending highly upon the flow regimes. Heavy efforts have been focused to identify the flow regime and at this moment we stand on relative very stable engineering background compare to the other research field. However, the issues related to objectiveness and transient flow regime are still open to study. Lee et al. and Ishii developed the method for the objective and instantaneous flow regime identification based on the neural network and new index of probability distribution of the flow regime which allows just one second observation for the flow regime identification. In the present paper, we developed the self-organized neural network for more objective approach to this problem. Kohonen's Self-Organizing Map (SOM) has been used for clustering, visualization, and abstraction. The SOM is trained through unsupervised competitive learning using a 'winner takes it all' policy. Therefore, its unsupervised training character delete the possible interference of the regime developer to the neural network training. After developing the computer code, we evaluate the performance of the code with the vertically upward two-phase flow in the pipes of 25.4 and 50.4 cmm I.D. Also, the sensitivity of the number of the clusters to the flow regime identification was made

Self-organizing neural networks for automatic detection and classification of contrast-enhancing lesions in dynamic MR-mammography

International Nuclear Information System (INIS)

Vomweg, T.W.; Teifke, A.; Kauczor, H.U.; Achenbach, T.; Rieker, O.; Schreiber, W.G.; Heitmann, K.R.; Beier, T.; Thelen, M.

2005-01-01

Purpose: Investigation and statistical evaluation of 'Self-Organizing Maps', a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. Material and Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. Results: A well balanced neural network achieved a sensitivity of 90.5% and a specificity of 72.2% in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a 'typical malignant' signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. Discussion: Self-Organizing Maps are capable of classifying a dynamic signal/time curve as 'typical benign' or 'typical malignant'. Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future. (orig.)

Firm Size, a Self-Organized Critical Phenomenon: Evidence from the Dynamical Systems Theory

Science.gov (United States)

Chandra, Akhilesh

This research draws upon a recent innovation in the dynamical systems literature called the theory of self -organized criticality (SOC) (Bak, Tang, and Wiesenfeld 1988) to develop a computational model of a firm's size by relating its internal and the external sub-systems. As a holistic paradigm, the theory of SOC implies that a firm as a composite system of many degrees of freedom naturally evolves to a critical state in which a minor event starts a chain reaction that can affect either a part or the system as a whole. Thus, the global features of a firm cannot be understood by analyzing its individual parts separately. The causal framework builds upon a constant capital resource to support a volume of production at the existing level of efficiency. The critical size is defined as the production level at which the average product of a firm's factors of production attains its maximum value. The non -linearity is inferred by a change in the nature of relations at the border of criticality, between size and the two performance variables, viz., the operating efficiency and the financial efficiency. The effect of breaching the critical size is examined on the stock price reactions. Consistent with the theory of SOC, it is hypothesized that the temporal response of a firm breaching the level of critical size should behave as a flicker noise (1/f) process. The flicker noise is characterized by correlations extended over a wide range of time scales, indicating some sort of cooperative effect among a firm's degrees of freedom. It is further hypothesized that a firm's size evolves to a spatial structure with scale-invariant, self-similar (fractal) properties. The system is said to be self-organized inasmuch as it naturally evolves to the state of criticality without any detailed specifications of the initial conditions. In this respect, the critical state is an attractor of the firm's dynamics. Another set of hypotheses examines the relations between the size and the

Dynamic data-driven integrated flare model based on self-organized criticality

Science.gov (United States)

Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M. K.

2013-05-01

Context. We interpret solar flares as events originating in active regions that have reached the self-organized critical state. We describe them with a dynamic integrated flare model whose initial conditions and driving mechanism are derived from observations. Aims: We investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. Methods: To investigate whether the distribution functions of total energy, peak energy, and event duration follow the expected scaling laws, we first applied the previously reported static cellular automaton model to a time series of seven solar vector magnetograms of the NOAA active region 8210 recorded by the Imaging Vector Magnetograph on May 1 1998 between 18:59 UT and 23:16 UT until the self-organized critical state was reached. We then evolved the magnetic field between these processed snapshots through spline interpolation, mimicking a natural driver in our dynamic model. We identified magnetic discontinuities that exceeded a threshold in the Laplacian of the magnetic field after each interpolation step. These discontinuities were relaxed in local diffusion events, implemented in the form of cellular automaton evolution rules. Subsequent interpolation and relaxation steps covered all transitions until the end of the processed magnetograms' sequence. We additionally advanced each magnetic configuration that has reached the self-organized critical state (SOC configuration) by the static model until 50 more flares were triggered, applied the dynamic model again to the new sequence, and repeated the same process sufficiently often to generate adequate statistics. Physical requirements, such as the divergence-free condition for the magnetic field, were approximately imposed. Results: We obtain robust power laws in the distribution functions of the modeled flaring events with scaling indices that agree well

Self-Organizing Maps Neural Networks Applied to the Classification of Ethanol Samples According to the Region of Commercialization

Directory of Open Access Journals (Sweden)

Aline Regina Walkoff

2017-10-01

Full Text Available Physical-chemical analysis data were collected, from 998 ethanol samples of automotive ethanol commercialized in the northern, midwestern and eastern regions of the state of Paraná. The data presented self-organizing maps (SOM neural networks, which classified them according to those regions. The self-organizing maps best configuration had a 45 x 45 topology and 5000 training epochs, with a final learning rate of 6.7x10-4, a final neighborhood relationship of 3x10-2 and a mean quantization error of 2x10-2. This neural network provided a topological map depicting three separated groups, each one corresponding to samples of a same region of commercialization. Four maps of weights, one for each parameter, were presented. The network established the pH was the most important variable for classification and electrical conductivity the least one. The self-organizing maps application allowed the segmentation of alcohol samples, therefore identifying them according to the region of commercialization. DOI: http://dx.doi.org/10.17807/orbital.v9i4.982

Self-organized criticality in a sheared granular stick-slip system

International Nuclear Information System (INIS)

Dalton, Fergal; Corcoran, David

2001-01-01

We present an analysis of results obtained from a mechanical apparatus consisting of an annular plate shearing over a granular bed. The size, energy dissipation, and duration of slips in the system exhibit power-law distributions and a 1/f 2 power spectrum, in accordance with self-organized criticality. We draw similarities with earthquakes

Scaling, phase transitions, and nonuniversality in a self-organized critical cellular-automaton model

International Nuclear Information System (INIS)

Christensen, K.; Olami, Z.

1992-01-01

We present a two-dimensional continuous cellular automaton that is equivalent to a driven spring-block model. Both the conservation and the anisotropy in the model are controllable quantities. Above a critical level of conservation, the model exhibits self-organized criticality. The self-organization of this system and hence the critical exponents depend on the conservation and the boundary conditions. In the critical isotropic nonconservative phase, the exponents change continuously as a function of conservation. Furthermore, the exponents vary continuously when changing the boundary conditions smoothly. Consequently, there is no universality of the critical exponents. We discuss the relevance of this for earthquakes. Introducing anisotropy changes the scaling of the distribution function, but not the power-law exponent. We explore the phase diagram of this model. We find that at low conservation levels a localization transition occurs. We see two additional phase transitions. The first is seen when moving from the conservative into the nonconservative model. The second appears when passing from the anisotropic two-dimensional system to the purely one-dimensional system

Transformation-invariant visual representations in self-organizing spiking neural networks.

Science.gov (United States)

Evans, Benjamin D; Stringer, Simon M

2012-01-01

The ventral visual pathway achieves object and face recognition by building transformation-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transformation-invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT) learning. However, it has not previously been investigated how transformation-invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP) where the change in synaptic strength is dependent on the relative times of the spikes emitted by the presynaptic and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF) neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model parameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

Transform-invariant visual representations in self-organizing spiking neural networks

Directory of Open Access Journals (Sweden)

Benjamin eEvans

2012-07-01

Full Text Available The ventral visual pathway achieves object and face recognition by building transform-invariant representations from elementary visual features. In previous computer simulation studies with rate-coded neural networks, the development of transform invariant representations has been demonstrated using either of two biologically plausible learning mechanisms, Trace learning and Continuous Transformation (CT learning. However, it has not previously been investigated how transform invariant representations may be learned in a more biologically accurate spiking neural network. A key issue is how the synaptic connection strengths in such a spiking network might self-organize through Spike-Time Dependent Plasticity (STDP where the change in synaptic strength is dependent on the relative times of the spikes emitted by the pre- and postsynaptic neurons rather than simply correlated activity driving changes in synaptic efficacy. Here we present simulations with conductance-based integrate-and-fire (IF neurons using a STDP learning rule to address these gaps in our understanding. It is demonstrated that with the appropriate selection of model pa- rameters and training regime, the spiking network model can utilize either Trace-like or CT-like learning mechanisms to achieve transform-invariant representations.

Critical neural networks with short- and long-term plasticity

Science.gov (United States)

Michiels van Kessenich, L.; Luković, M.; de Arcangelis, L.; Herrmann, H. J.

2018-03-01

In recent years self organized critical neuronal models have provided insights regarding the origin of the experimentally observed avalanching behavior of neuronal systems. It has been shown that dynamical synapses, as a form of short-term plasticity, can cause critical neuronal dynamics. Whereas long-term plasticity, such as Hebbian or activity dependent plasticity, have a crucial role in shaping the network structure and endowing neural systems with learning abilities. In this work we provide a model which combines both plasticity mechanisms, acting on two different time scales. The measured avalanche statistics are compatible with experimental results for both the avalanche size and duration distribution with biologically observed percentages of inhibitory neurons. The time series of neuronal activity exhibits temporal bursts leading to 1 /f decay in the power spectrum. The presence of long-term plasticity gives the system the ability to learn binary rules such as xor, providing the foundation of future research on more complicated tasks such as pattern recognition.

Spontaneous neuronal activity as a self-organized critical phenomenon

Science.gov (United States)

de Arcangelis, L.; Herrmann, H. J.

2013-01-01

Neuronal avalanches are a novel mode of activity in neuronal networks, experimentally found in vitro and in vivo, and exhibit a robust critical behaviour. Avalanche activity can be modelled within the self-organized criticality framework, including threshold firing, refractory period and activity-dependent synaptic plasticity. The size and duration distributions confirm that the system acts in a critical state, whose scaling behaviour is very robust. Next, we discuss the temporal organization of neuronal avalanches. This is given by the alternation between states of high and low activity, named up and down states, leading to a balance between excitation and inhibition controlled by a single parameter. During these periods both the single neuron state and the network excitability level, keeping memory of past activity, are tuned by homeostatic mechanisms. Finally, we verify if a system with no characteristic response can ever learn in a controlled and reproducible way. Learning in the model occurs via plastic adaptation of synaptic strengths by a non-uniform negative feedback mechanism. Learning is a truly collective process and the learning dynamics exhibits universal features. Even complex rules can be learned provided that the plastic adaptation is sufficiently slow.

A Self-Organizing Incremental Neural Network based on local distribution learning.

Science.gov (United States)

Xing, Youlu; Shi, Xiaofeng; Shen, Furao; Zhou, Ke; Zhao, Jinxi

2016-12-01

In this paper, we propose an unsupervised incremental learning neural network based on local distribution learning, which is called Local Distribution Self-Organizing Incremental Neural Network (LD-SOINN). The LD-SOINN combines the advantages of incremental learning and matrix learning. It can automatically discover suitable nodes to fit the learning data in an incremental way without a priori knowledge such as the structure of the network. The nodes of the network store rich local information regarding the learning data. The adaptive vigilance parameter guarantees that LD-SOINN is able to add new nodes for new knowledge automatically and the number of nodes will not grow unlimitedly. While the learning process continues, nodes that are close to each other and have similar principal components are merged to obtain a concise local representation, which we call a relaxation data representation. A denoising process based on density is designed to reduce the influence of noise. Experiments show that the LD-SOINN performs well on both artificial and real-word data. Copyright © 2016 Elsevier Ltd. All rights reserved.

Self-organized critical behavior in pinned flux lattices

International Nuclear Information System (INIS)

Pla, O.; Nori, F.

1991-01-01

We study the response of pinned fluxed lattices, under small perturbations in the driving force, below and close to the pinning-depinning transition. For driving Lorentz forces below F c (the depinning force at which the whole flux lattice slides), the system has instabilities against small force increases, with a power-law distribution characteristic of self-organized criticality. Specifically, D(d)∼d -1,3 , where d is the displacement of a flux line after a very small force increase. We also study the initial stages of the motion of the lattice once the driving force overcomes the pinning forces

Enhancement of biomembrane functions under phase-separated conditions: A self-organized criticality phenomenon?

International Nuclear Information System (INIS)

Eze, M.O.; Chela Flores, J.

1993-12-01

Self-organized criticality (SOC) is hereby proposed as a possible physical basis for explaining observations in the temperature-dependence of the rates of biological membrane-associated events. The biomembrane undergoes a reversible, cooperative, thermotropic gel-to-liquid crystalline phase transition which is broad, and involves lateral phase separation. The lateral phase separated (rather than the totally gel-, or the totally liquid crystalline-) membrane state has been observed to be the state in which vital membrane functions are facilitated. The membrane in this unique state is viewed, for our purposes here, as a dynamical, extended dissipative system with spatial and temporal degrees of freedom, exhibiting power law behaviour, typical of the self-organized critical state. Experiments are suggested for verifying this hypothesis. (author). 30 refs

Self-organizing sensing and actuation for automatic control

Science.gov (United States)

Cheng, George Shu-Xing

2017-07-04

A Self-Organizing Process Control Architecture is introduced with a Sensing Layer, Control Layer, Actuation Layer, Process Layer, as well as Self-Organizing Sensors (SOS) and Self-Organizing Actuators (SOA). A Self-Organizing Sensor for a process variable with one or multiple input variables is disclosed. An artificial neural network (ANN) based dynamic modeling mechanism as part of the Self-Organizing Sensor is described. As a case example, a Self-Organizing Soft-Sensor for CFB Boiler Bed Height is presented. Also provided is a method to develop a Self-Organizing Sensor.

Self-organized Criticality Model for Ocean Internal Waves

International Nuclear Information System (INIS)

Wang Gang; Hou Yijun; Lin Min; Qiao Fangli

2009-01-01

In this paper, we present a simple spring-block model for ocean internal waves based on the self-organized criticality (SOC). The oscillations of the water blocks in the model display power-law behavior with an exponent of -2 in the frequency domain, which is similar to the current and sea water temperature spectra in the actual ocean and the universal Garrett and Munk deep ocean internal wave model [Geophysical Fluid Dynamics 2 (1972) 225; J. Geophys. Res. 80 (1975) 291]. The influence of the ratio of the driving force to the spring coefficient to SOC behaviors in the model is also discussed. (general)

Covalent growth factor tethering to direct neural stem cell differentiation and self-organization.

Science.gov (United States)

Ham, Trevor R; Farrag, Mahmoud; Leipzig, Nic D

2017-04-15

Tethered growth factors offer exciting new possibilities for guiding stem cell behavior. However, many of the current methods present substantial drawbacks which can limit their application and confound results. In this work, we developed a new method for the site-specific covalent immobilization of azide-tagged growth factors and investigated its utility in a model system for guiding neural stem cell (NSC) behavior. An engineered interferon-γ (IFN-γ) fusion protein was tagged with an N-terminal azide group, and immobilized to two different dibenzocyclooctyne-functionalized biomimetic polysaccharides (chitosan and hyaluronan). We successfully immobilized azide-tagged IFN-γ under a wide variety of reaction conditions, both in solution and to bulk hydrogels. To understand the interplay between surface chemistry and protein immobilization, we cultured primary rat NSCs on both materials and showed pronounced biological effects. Expectedly, immobilized IFN-γ increased neuronal differentiation on both materials. Expression of other lineage markers varied depending on the material, suggesting that the interplay of surface chemistry and protein immobilization plays a large role in nuanced cell behavior. We also investigated the bioactivity of immobilized IFN-γ in a 3D environment in vivo and found that it sparked the robust formation of neural tube-like structures from encapsulated NSCs. These findings support a wide range of potential uses for this approach and provide further evidence that adult NSCs are capable of self-organization when exposed to the proper microenvironment. For stem cells to be used effectively in regenerative medicine applications, they must be provided with the appropriate cues and microenvironment so that they integrate with existing tissue. This study explores a new method for guiding stem cell behavior: covalent growth factor tethering. We found that adding an N-terminal azide-tag to interferon-γ enabled stable and robust Cu-free 'click

Self-organized criticality in asymmetric exclusion model with noise for freeway traffic

Science.gov (United States)

Nagatani, Takashi

1995-02-01

The one-dimensional asymmetric simple-exclusion model with open boundaries for parallel update is extended to take into account temporary stopping of particles. The model presents the traffic flow on a highway with temporary deceleration of cars. Introducing temporary stopping into the asymmetric simple-exclusion model drives the system asymptotically into a steady state exhibiting a self-organized criticality. In the self-organized critical state, start-stop waves (or traffic jams) appear with various sizes (or lifetimes). The typical interval between consecutive jams scales as ≃ Lv with v = 0.51 ± 0.05 where L is the system size. It is shown that the cumulative jam-interval distribution Ns( L) satisfies the finite-size scaling form ( Ns( L) ≃ L- vf( s/ Lv). Also, the typical lifetime ≃ Lv‧ with v‧ = 0.52 ± 0.05. The cumulative distribution Nm( L) of lifetimes satisfies the finite-size scaling form Nm( L)≃ L-1g( m/ Lv‧).

Crossover to self-organized criticality in an inertial sandpile model

OpenAIRE

Head, DA; Rodgers, GJ

1996-01-01

We introduce a one-dimensional sandpile model which incorporates particle inertia. The inertial dynamics are governed by a new parameter which, as it passes through a threshold value, alters the toppling dynamics in such a way that the system no longer evolves to a self-organized critical state. A range of mean-field theories based on a kinetic equation approach is presented which confirm the numerical findings. We conclude by considering the physical applications of this model, particularly ...

Self-organized Criticality in a Modified Evolution Model on Generalized Barabasi-Albert Scale-Free Networks

International Nuclear Information System (INIS)

Lin Min; Wang Gang; Chen Tianlun

2007-01-01

A modified evolution model of self-organized criticality on generalized Barabasi-Albert (GBA) scale-free networks is investigated. In our model, we find that spatial and temporal correlations exhibit critical behaviors. More importantly, these critical behaviors change with the parameter b, which weights the distance in comparison with the degree in the GBA network evolution.

Self-Organized Criticality Theory Model of Thermal Sandpile

International Nuclear Information System (INIS)

Peng Xiao-Dong; Qu Hong-Peng; Xu Jian-Qiang; Han Zui-Jiao

2015-01-01

A self-organized criticality model of a thermal sandpile is formulated for the first time to simulate the dynamic process with interaction between avalanche events on the fast time scale and diffusive transports on the slow time scale. The main characteristics of the model are that both particle and energy avalanches of sand grains are considered simultaneously. Properties of intermittent transport and improved confinement are analyzed in detail. The results imply that the intermittent phenomenon such as blobs in the low confinement mode as well as edge localized modes in the high confinement mode observed in tokamak experiments are not only determined by the edge plasma physics, but also affected by the core plasma dynamics. (paper)

Modeling financial markets by self-organized criticality

Science.gov (United States)

Biondo, Alessio Emanuele; Pluchino, Alessandro; Rapisarda, Andrea

2015-10-01

We present a financial market model, characterized by self-organized criticality, that is able to generate endogenously a realistic price dynamics and to reproduce well-known stylized facts. We consider a community of heterogeneous traders, composed by chartists and fundamentalists, and focus on the role of informative pressure on market participants, showing how the spreading of information, based on a realistic imitative behavior, drives contagion and causes market fragility. In this model imitation is not intended as a change in the agent's group of origin, but is referred only to the price formation process. We introduce in the community also a variable number of random traders in order to study their possible beneficial role in stabilizing the market, as found in other studies. Finally, we also suggest some counterintuitive policy strategies able to dampen fluctuations by means of a partial reduction of information.

Self-organised criticality via retro-synaptic signals

Science.gov (United States)

Hernandez-Urbina, Victor; Herrmann, J. Michael

2016-12-01

The brain is a complex system par excellence. In the last decade the observation of neuronal avalanches in neocortical circuits suggested the presence of self-organised criticality in brain networks. The occurrence of this type of dynamics implies several benefits to neural computation. However, the mechanisms that give rise to critical behaviour in these systems, and how they interact with other neuronal processes such as synaptic plasticity are not fully understood. In this paper, we present a long-term plasticity rule based on retro-synaptic signals that allows the system to reach a critical state in which clusters of activity are distributed as a power-law, among other observables. Our synaptic plasticity rule coexists with other synaptic mechanisms such as spike-timing-dependent plasticity, which implies that the resulting synaptic modulation captures not only the temporal correlations between spiking times of pre- and post-synaptic units, which has been suggested as requirement for learning and memory in neural systems, but also drives the system to a state of optimal neural information processing.

Implementation of self-organizing neural networks for visuo-motor control of an industrial robot.

Science.gov (United States)

Walter, J A; Schulten, K I

1993-01-01

The implementation of two neural network algorithms for visuo-motor control of an industrial robot (Puma 562) is reported. The first algorithm uses a vector quantization technique, the ;neural-gas' network, together with an error correction scheme based on a Widrow-Hoff-type learning rule. The second algorithm employs an extended self-organizing feature map algorithm. Based on visual information provided by two cameras, the robot learns to position its end effector without an external teacher. Within only 3000 training steps, the robot-camera system is capable of reducing the positioning error of the robot's end effector to approximately 0.1% of the linear dimension of the work space. By employing adaptive feedback the robot succeeds in compensating not only slow calibration drifts, but also sudden changes in its geometry. Hardware aspects of the robot-camera system are discussed.

Neural constructivism or self-organization?

NARCIS (Netherlands)

van der Maas, H.L.J.; Molenaar, P.C.M.

2000-01-01

Comments on the article by S. R. Quartz et al (see record 1998-00749-001) which discussed the constructivist perspective of interaction between cognition and neural processes during development and consequences for theories of learning. Three arguments are given to show that neural constructivism

Critical Branching Neural Networks

Science.gov (United States)

Kello, Christopher T.

2013-01-01

It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

On Origin of Power-Law Distributions in Self-Organized Criticality from Random Walk Treatment

International Nuclear Information System (INIS)

Cao Xiaofeng; Deng Zongwei; Yang Chunbin

2008-01-01

The origin of power-law distributions in self-organized criticality is investigated by treating the variation of the number of active sites in the system as a stochastic process. An avalanche is then regarded as a first-return random walk process in a one-dimensional lattice. We assume that the variation of the number of active sites has three possibilities in each update: to increase by 1 with probability f 1 , to decrease by 1 with probability f 2 , or remain unchanged with probability 1-f 1 -f 2 . This mimics the dynamics in the system. Power-law distributions of the lifetime are found when the random walk is unbiased with equal probability to move in opposite directions. This shows that power-law distributions in self-organized criticality may be caused by the balance of competitive interactions.

Self-organizing networks for extracting jet features

International Nuclear Information System (INIS)

Loennblad, L.; Peterson, C.; Pi, H.; Roegnvaldsson, T.

1991-01-01

Self-organizing neural networks are briefly reviewed and compared with supervised learning algorithms like back-propagation. The power of self-organization networks is in their capability of displaying typical features in a transparent manner. This is successfully demonstrated with two applications from hadronic jet physics; hadronization model discrimination and separation of b.c. and light quarks. (orig.)

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro; Narabayashi, Tadashi

2008-01-01

In BWR stability monitoring damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; In this method, measured fluctuating signal is decomposed into some independent components and the signal component directly related to stability is extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal component efficiently. The self-organizing map (SOM) is one of the artificial neural networks and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal component more quickly and more accurately, and the availability was confirmed through the feasibility study. (author)

Power laws and self-organized criticality in theory and nature

International Nuclear Information System (INIS)

Marković, Dimitrije; Gros, Claudius

2014-01-01

Power laws and distributions with heavy tails are common features of many complex systems. Examples are the distribution of earthquake magnitudes, solar flare intensities and the sizes of neuronal avalanches. Previously, researchers surmised that a single general concept may act as an underlying generative mechanism, with the theory of self organized criticality being a weighty contender. The power-law scaling observed in the primary statistical analysis is an important, but by far not the only feature characterizing experimental data. The scaling function, the distribution of energy fluctuations, the distribution of inter-event waiting times, and other higher order spatial and temporal correlations, have seen increased consideration over the last years. Leading to realization that basic models, like the original sandpile model, are often insufficient to adequately describe the complexity of real-world systems with power-law distribution. Consequently, a substantial amount of effort has gone into developing new and extended models and, hitherto, three classes of models have emerged. The first line of models is based on a separation between the time scales of an external drive and an internal dissipation, and includes the original sandpile model and its extensions, like the dissipative earthquake model. Within this approach the steady state is close to criticality in terms of an absorbing phase transition. The second line of models is based on external drives and internal dynamics competing on similar time scales and includes the coherent noise model, which has a non-critical steady state characterized by heavy-tailed distributions. The third line of models proposes a non-critical self-organizing state, being guided by an optimization principle, such as the concept of highly optimized tolerance. We present a comparative overview regarding distinct modeling approaches together with a discussion of their potential relevance as underlying generative models for real

Power laws and self-organized criticality in theory and nature

Energy Technology Data Exchange (ETDEWEB)

Marković, Dimitrije, E-mail: markovic@cbs.mpg.de [Institute for Theoretical Physics, Goethe University Frankfurt (Germany); Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig (Germany); Biomagnetic Center, Hans Berger Clinic for Neurology, University Hospital Jena, Jena (Germany); Gros, Claudius, E-mail: gros@itp.uni-frankfurt.de [Institute for Theoretical Physics, Goethe University Frankfurt (Germany)

2014-03-01

Power laws and distributions with heavy tails are common features of many complex systems. Examples are the distribution of earthquake magnitudes, solar flare intensities and the sizes of neuronal avalanches. Previously, researchers surmised that a single general concept may act as an underlying generative mechanism, with the theory of self organized criticality being a weighty contender. The power-law scaling observed in the primary statistical analysis is an important, but by far not the only feature characterizing experimental data. The scaling function, the distribution of energy fluctuations, the distribution of inter-event waiting times, and other higher order spatial and temporal correlations, have seen increased consideration over the last years. Leading to realization that basic models, like the original sandpile model, are often insufficient to adequately describe the complexity of real-world systems with power-law distribution. Consequently, a substantial amount of effort has gone into developing new and extended models and, hitherto, three classes of models have emerged. The first line of models is based on a separation between the time scales of an external drive and an internal dissipation, and includes the original sandpile model and its extensions, like the dissipative earthquake model. Within this approach the steady state is close to criticality in terms of an absorbing phase transition. The second line of models is based on external drives and internal dynamics competing on similar time scales and includes the coherent noise model, which has a non-critical steady state characterized by heavy-tailed distributions. The third line of models proposes a non-critical self-organizing state, being guided by an optimization principle, such as the concept of highly optimized tolerance. We present a comparative overview regarding distinct modeling approaches together with a discussion of their potential relevance as underlying generative models for real

Plasticity of ductile metallic glasses: a self-organized critical state.

Science.gov (United States)

Sun, B A; Yu, H B; Jiao, W; Bai, H Y; Zhao, D Q; Wang, W H

2010-07-16

We report a close correlation between the dynamic behavior of serrated flow and the plasticity in metallic glasses (MGs) and show that the plastic deformation of ductile MGs can evolve into a self-organized critical state characterized by the power-law distribution of shear avalanches. A stick-slip model considering the interaction of multiple shear bands is presented to reveal complex scale-free intermittent shear-band motions in ductile MGs and quantitatively reproduce the experimental observations. Our studies have implications for understanding the precise plastic deformation mechanism of MGs.

Self-organized criticality, long-time correlations, and the standard transport paradigm

International Nuclear Information System (INIS)

Krommes, J.A.

2000-01-01

Some aspects of low-frequency, long-wavelength fluctuations are considered. A stochastic model is used to show that power-law time correlations need not arise from self-organized criticality. A formula for the frequency spectrum of uncorrelated, overlapping avalanches is shown to be a special case of the spectral balance equation of renormalized statistical turbulence theory. It is argued that there need be no contradiction between the presence of long-time correlations and the existence of local transport coefficients

Impact of network topology on self-organized criticality

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Hoffmann, Heiko

2018-02-01

The general mechanisms behind self-organized criticality (SOC) are still unknown. Several microscopic and mean-field theory approaches have been suggested, but they do not explain the dependence of the exponents on the underlying network topology of the SOC system. Here, we first report the phenomena that in the Bak-Tang-Wiesenfeld (BTW) model, sites inside an avalanche area largely return to their original state after the passing of an avalanche, forming, effectively, critically arranged clusters of sites. Then, we hypothesize that SOC relies on the formation process of these clusters, and present a model of such formation. For low-dimensional networks, we show theoretically and in simulation that the exponent of the cluster-size distribution is proportional to the ratio of the fractal dimension of the cluster boundary and the dimensionality of the network. For the BTW model, in our simulations, the exponent of the avalanche-area distribution matched approximately our prediction based on this ratio for two-dimensional networks, but deviated for higher dimensions. We hypothesize a transition from cluster formation to the mean-field theory process with increasing dimensionality. This work sheds light onto the mechanisms behind SOC, particularly, the impact of the network topology.

Growing hierarchical probabilistic self-organizing graphs.

Science.gov (United States)

López-Rubio, Ezequiel; Palomo, Esteban José

2011-07-01

Since the introduction of the growing hierarchical self-organizing map, much work has been done on self-organizing neural models with a dynamic structure. These models allow adjusting the layers of the model to the features of the input dataset. Here we propose a new self-organizing model which is based on a probabilistic mixture of multivariate Gaussian components. The learning rule is derived from the stochastic approximation framework, and a probabilistic criterion is used to control the growth of the model. Moreover, the model is able to adapt to the topology of each layer, so that a hierarchy of dynamic graphs is built. This overcomes the limitations of the self-organizing maps with a fixed topology, and gives rise to a faithful visualization method for high-dimensional data.

Self-organized criticality as a paradigm for transport in magnetically confined plasmas

International Nuclear Information System (INIS)

Carreras, B.A.; Newman, D.; Lynch, V.E.; Diamond, P.H.

1996-01-01

Many models of natural phenomena manifest the basic hypothesis of self-organized criticality (SOC) [P. Bak, C. Tang, and K. Weisenfeld, Phys. Rev. Lett., 1987, vol. 59, p. 381]. The SOC concept brings together the self-similarity on space and time scales that are common to many of these phenomena. The application of the SOC modeling concept to the plasma dynamics near marginal stability opens new possibilities of understanding issues such as Bohm scaling, profile consistency, broad-band fluctuation spectra with universal characteristics, and fast time scales. In this paper, we review the SOC concept and its possible applications to the study of transport in magnetically confined plasmas

Usage of self-organizing neural networks in evaluation of consumer behaviour

Directory of Open Access Journals (Sweden)

Jana Weinlichová

2010-01-01

Full Text Available This article deals with evaluation of consumer data by Artificial Intelligence methods. In methodical part there are described learning algorithms for Kohonen maps on the principle of supervised learning, unsupervised learning and semi-supervised learning. The principles of supervised learning and unsupervised learning are compared. On base of binding conditions of these principles there is pointed out an advantage of semi-supervised learning. Three algorithms are described for the semi-supervised learning: label propagation, self-training and co-training. Especially usage of co-training in Kohonen map learning seems to be promising point of other research. In concrete application of Kohonen neural network on consumer’s expense the unsupervised learning method has been chosen – the self-organization. So the features of data are evaluated by clustering method called Kohonen maps. These input data represents consumer expenses of households in countries of European union and are characterised by 12-dimension vector according to commodity classification. The data are evaluated in several years, so we can see their distribution, similarity or dissimilarity and also their evolution. In the article we discus other usage of this method for this type of data and also comparison of our results with results reached by hierarchical cluster analysis.

The Ramifications of Meddling with Systems Governed by Self-organized Critical Dynamics

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Carreras, B. A.; Newman, D. E.; Dobson, I.

2002-12-01

Complex natural, well as man-made, systems often exhibit characteristics similar to those seen in self-organized critical (SOC) systems. The concept of self-organized criticality brings together ideas of self-organization of nonlinear dynamical systems with the often-observed near critical behavior of many natural phenomena. These phenomena exhibit self-similarities over extended ranges of spatial and temporal scales. In those systems, scale lengths may be described by fractal geometry and time scales that lead to 1/f-like power spectra. Natural applications include modeling the motion of tectonics plates, forest fires, magnetospheric dynamics, spin glass systems, and turbulent transport. In man-made systems, applications have included traffic dynamics, power and communications networks, and financial markets among many others. Simple cellular automata models such as the running sandpile model have been very useful in reproducing the complexity and characteristics of these systems. One characteristic property of the SOC systems is that they relax through what we call events. These events can happen over all scales of the system. Examples of these events are: earthquakes in the case of plate tectonic; fires in forest evolution extinction in the co evolution of biological species; and blackouts in power transmission systems. In a time-averaged sense, these systems are subcritical (that is, they lie in an average state that should not trigger any events) and the relaxation events happen intermittently. The time spent in a subcritical state relative to the time of the events varies from one system to another. For instance, the chance of finding a forest on fire is very low with the frequency of fires being on the order of one fire every few years and with many of these fires small and inconsequential. Very large fires happen over time periods of decades or even centuries. However, because of their consequences, these large but infrequent events are the important ones

Natural hazards and self-organized criticality

International Nuclear Information System (INIS)

Krenn, R.

2012-01-01

Several natural hazards exhibit power-law behavior on their frequency-size distributions. Self-organized criticality has become a promising candidate that could offer a more in-depth understanding of the origin of temporal and spatial scaling in dissipative nonequilibrium systems. The outcomes of this thesis are presented in three scientific papers followed by a concluding summary and an appendix.In paper (A) we present a semi-phenomenological approach to explain the complex scaling behavior of the Drossel-Schwabl forest-fire model (DS-FFM) in two dimensions. We derive the scaling exponent solely from the scaling exponent of the clusters' accessible perimeter. Furthermore, the unusual transition to an exponential decay is explained both qualitatively and quantitatively. The exponential decay itself could be reproduced at least qualitatively. In paper (B) we extend the DS-FFM towards anthropogenic ignition factors. The main outcomes are an increase of the scaling exponent with decreasing lightning probability as well as a splitting of the partial frequency-size distributions of lightning induced and man made fires. Lightning is identified as the dominant mechanism in the regime of the largest fires. The results could be validated through an analysis of the Canadian Large Fire Database.In paper (C) we obtain an almost complete theory of the Olami-Feder-Christensen (OFC) model's complex spatio-temporal behavior. Synchronization pushes the system towards a critical state and generates the Gutenberg-Richter law. Desynchronization prevents the system from becoming overcritical and generates foreshocks and aftershocks. Our approach also provides a simple explanation of Omori's law. Beyond this, it explains the phenomena of foreshock migration and aftershock diffusion and the occurrence of large earthquakes without any foreshocks. A novel integer algorithm for the numerics is presented in appendix (A).(author) [de

Self-Organized Criticality and Scaling in Lifetime of Traffic Jams

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Nagatani, Takashi

1995-01-01

The deterministic cellular automaton 184 (the one-dimensional asymmetric simple-exclusion model with parallel dynamics) is extended to take into account injection or extraction of particles. The model presents the traffic flow on a highway with inflow or outflow of cars.Introducing injection or extraction of particles into the asymmetric simple-exclusion model drives the system asymptotically into a steady state exhibiting a self-organized criticality. The typical lifetime of traffic jams scales as \\cong Lν with ν=0.65±0.04. It is shown that the cumulative distribution Nm (L) of lifetimes satisfies the finite-size scaling form Nm (L) \\cong L-1 f(m/Lν).

Neural Responses to Heartbeats in the Default Network Encode the Self in Spontaneous Thoughts

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Babo-Rebelo, Mariana; Richter, Craig G.

2016-01-01

The default network (DN) has been consistently associated with self-related cognition, but also to bodily state monitoring and autonomic regulation. We hypothesized that these two seemingly disparate functional roles of the DN are functionally coupled, in line with theories proposing that selfhood is grounded in the neural monitoring of internal organs, such as the heart. We measured with magnetoencephalograhy neural responses evoked by heartbeats while human participants freely mind-wandered. When interrupted by a visual stimulus at random intervals, participants scored the self-relatedness of the interrupted thought. They evaluated their involvement as the first-person perspective subject or agent in the thought (“I”), and on another scale to what degree they were thinking about themselves (“Me”). During the interrupted thought, neural responses to heartbeats in two regions of the DN, the ventral precuneus and the ventromedial prefrontal cortex, covaried, respectively, with the “I” and the “Me” dimensions of the self, even at the single-trial level. No covariation between self-relatedness and peripheral autonomic measures (heart rate, heart rate variability, pupil diameter, electrodermal activity, respiration rate, and phase) or alpha power was observed. Our results reveal a direct link between selfhood and neural responses to heartbeats in the DN and thus directly support theories grounding selfhood in the neural monitoring of visceral inputs. More generally, the tight functional coupling between self-related processing and cardiac monitoring observed here implies that, even in the absence of measured changes in peripheral bodily measures, physiological and cognitive functions have to be considered jointly in the DN. SIGNIFICANCE STATEMENT The default network (DN) has been consistently associated with self-processing but also with autonomic regulation. We hypothesized that these two functions could be functionally coupled in the DN, inspired by

Using fuzzy self-organising maps for safety critical systems

International Nuclear Information System (INIS)

Kurd, Zeshan; Kelly, Tim P.

2007-01-01

This paper defines a type of constrained artificial neural network (ANN) that enables analytical certification arguments whilst retaining valuable performance characteristics. Previous work has defined a safety lifecycle for ANNs without detailing a specific neural model. Building on this previous work, the underpinning of the devised model is based upon an existing neuro-fuzzy system called the fuzzy self-organising map (FSOM). The FSOM is type of 'hybrid' ANN which allows behaviour to be described qualitatively and quantitatively using meaningful expressions. Safety of the FSOM is argued through adherence to safety requirements-derived from hazard analysis and expressed using safety constraints. The approach enables the construction of compelling (product-based) arguments for mitigation of potential failure modes associated with the FSOM. The constrained FSOM has been termed a 'safety critical artificial neural network' (SCANN). The SCANN can be used for non-linear function approximation and allows certified learning and generalisation for high criticality roles. A discussion of benefits for real-world applications is also presented

Study on self organized criticality of China power grid blackouts

Energy Technology Data Exchange (ETDEWEB)

Zhao, Xingyong; Zhang, Xiubin; He, Bin [Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240 (China)

2009-03-15

Based on the complex system theory and the concept of self organized criticality (SOC) theory, the mechanism of China power grid blackout is studied by analyzing the blackout data in the China power system from 1981 to 2002. The probability distribution functions of various measures of blackout size have a power tail. The analysis of scaled window variance and rescaled range statistics of the time series show moderate long time correlations. The blackout data seem consistent with SOC; the results obtained show that SOC dynamics may play an important role in the dynamics of power systems blackouts. It would be possible to propose novel approaches for understanding and controlling power systems blackouts. (author)

Study on self organized criticality of China power grid blackouts

Energy Technology Data Exchange (ETDEWEB)

Zhao Xingyong [Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240 (China)], E-mail: zhaoxingyong@sjtu.edu.cn; Zhang Xiubin; He Bin [Department of Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240 (China)

2009-03-15

Based on the complex system theory and the concept of self organized criticality (SOC) theory, the mechanism of China power grid blackout is studied by analyzing the blackout data in the China power system from 1981 to 2002. The probability distribution functions of various measures of blackout size have a power tail. The analysis of scaled window variance and rescaled range statistics of the time series show moderate long time correlations. The blackout data seem consistent with SOC; the results obtained show that SOC dynamics may play an important role in the dynamics of power systems blackouts. It would be possible to propose novel approaches for understanding and controlling power systems blackouts.

Self-Organized Criticality in an Anisotropic Earthquake Model

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Li, Bin-Quan; Wang, Sheng-Jun

2018-03-01

We have made an extensive numerical study of a modified model proposed by Olami, Feder, and Christensen to describe earthquake behavior. Two situations were considered in this paper. One situation is that the energy of the unstable site is redistributed to its nearest neighbors randomly not averagely and keeps itself to zero. The other situation is that the energy of the unstable site is redistributed to its nearest neighbors randomly and keeps some energy for itself instead of reset to zero. Different boundary conditions were considered as well. By analyzing the distribution of earthquake sizes, we found that self-organized criticality can be excited only in the conservative case or the approximate conservative case in the above situations. Some evidence indicated that the critical exponent of both above situations and the original OFC model tend to the same result in the conservative case. The only difference is that the avalanche size in the original model is bigger. This result may be closer to the real world, after all, every crust plate size is different. Supported by National Natural Science Foundation of China under Grant Nos. 11675096 and 11305098, the Fundamental Research Funds for the Central Universities under Grant No. GK201702001, FPALAB-SNNU under Grant No. 16QNGG007, and Interdisciplinary Incubation Project of SNU under Grant No. 5

Artificial neural network with self-organizing mapping for reactor stability monitoring

International Nuclear Information System (INIS)

Okumura, Motofumi; Tsuji, Masashi; Shimazu, Yoichiro

2009-01-01

In boiling water reactor (BWR) stability monitoring, damping ratio has been used as a stability index. A method for estimating the damping ratio by applying Principal Component Analysis (PCA) to neutron detector signals measured with local power range monitors (LPRMs) had been developed; in this method, measured fluctuating signal is decomposed into some independent components and the signal components directly related to stability are extracted among them to determine the damping ratio. For online monitoring, it is necessary to select stability related signal components efficiently. The self-organizing map (SOM) is one of the artificial neural networks (ANNs) and has the characteristics such that online learning is possible without supervised learning within a relatively short time. In the present study, the SOM was applied to extract the relevant signal components more quickly and more accurately, and the availability was confirmed through the feasibility study. For realizing online stability monitoring only with ANNs, another type of ANN that performs online processing of PCA was combined with SOM. And stability monitoring performance was investigated. (author)

Self-Organized Criticality in Astrophysics The Statistics of Nonlinear Processes in the Universe

CERN Document Server

Aschwanden, Markus

2011-01-01

The concept of ‘self-organized criticality’ (SOC) has been applied to a variety of problems, ranging from population growth and traffic jams to earthquakes, landslides and forest fires. The technique is now being applied to a wide range of phenomena in astrophysics, such as planetary magnetospheres, solar flares, cataclysmic variable stars, accretion disks, black holes and gamma-ray bursts, and also to phenomena in galactic physics and cosmology. Self-organized Criticality in Astrophysics introduces the concept of SOC and shows that, due to its universality and ubiquity, it is a law of nature. The theoretical framework and specific physical models are described, together with a range of applications in various aspects of astrophyics. The mathematical techniques, including the statistics of random processes, time series analysis, time scale and waiting time distributions, are presented and the results are applied to specific observations of astrophysical phenomena.

Mechanisms of self-organized criticality in social processes of knowledge creation.

Science.gov (United States)

Tadić, Bosiljka; Dankulov, Marija Mitrović; Melnik, Roderick

2017-09-01

In online social dynamics, a robust scale invariance appears as a key feature of collaborative efforts that lead to new social value. The underlying empirical data thus offers a unique opportunity to study the origin of self-organized criticality (SOC) in social systems. In contrast to physical systems in the laboratory, various human attributes of the actors play an essential role in the process along with the contents (cognitive, emotional) of the communicated artifacts. As a prototypical example, we consider the social endeavor of knowledge creation via Questions and Answers (Q&A). Using a large empirical data set from one of such Q&A sites and theoretical modeling, we reveal fundamental characteristics of SOC by investigating the temporal correlations at all scales and the role of cognitive contents to the avalanches of the knowledge-creation process. Our analysis shows that the universal social dynamics with power-law inhomogeneities of the actions and delay times provides the primary mechanism for self-tuning towards the critical state; it leads to the long-range correlations and the event clustering in response to the external driving by the arrival of new users. In addition, the involved cognitive contents (systematically annotated in the data and observed in the model) exert important constraints that identify unique classes of the knowledge-creation avalanches. Specifically, besides determining a fine structure of the developing knowledge networks, they affect the values of scaling exponents and the geometry of large avalanches and shape the multifractal spectrum. Furthermore, we find that the level of the activity of the communities that share the knowledge correlates with the fluctuations of the innovation rate, implying that the increase of innovation may serve as the active principle of self-organization. To identify relevant parameters and unravel the role of the network evolution underlying the process in the social system under consideration, we

Mechanisms of self-organized criticality in social processes of knowledge creation

Science.gov (United States)

Tadić, Bosiljka; Dankulov, Marija Mitrović; Melnik, Roderick

2017-09-01

In online social dynamics, a robust scale invariance appears as a key feature of collaborative efforts that lead to new social value. The underlying empirical data thus offers a unique opportunity to study the origin of self-organized criticality (SOC) in social systems. In contrast to physical systems in the laboratory, various human attributes of the actors play an essential role in the process along with the contents (cognitive, emotional) of the communicated artifacts. As a prototypical example, we consider the social endeavor of knowledge creation via Questions and Answers (Q&A). Using a large empirical data set from one of such Q&A sites and theoretical modeling, we reveal fundamental characteristics of SOC by investigating the temporal correlations at all scales and the role of cognitive contents to the avalanches of the knowledge-creation process. Our analysis shows that the universal social dynamics with power-law inhomogeneities of the actions and delay times provides the primary mechanism for self-tuning towards the critical state; it leads to the long-range correlations and the event clustering in response to the external driving by the arrival of new users. In addition, the involved cognitive contents (systematically annotated in the data and observed in the model) exert important constraints that identify unique classes of the knowledge-creation avalanches. Specifically, besides determining a fine structure of the developing knowledge networks, they affect the values of scaling exponents and the geometry of large avalanches and shape the multifractal spectrum. Furthermore, we find that the level of the activity of the communities that share the knowledge correlates with the fluctuations of the innovation rate, implying that the increase of innovation may serve as the active principle of self-organization. To identify relevant parameters and unravel the role of the network evolution underlying the process in the social system under consideration, we

Self-organized criticality occurs in non-conservative neuronal networks during `up' states

Science.gov (United States)

Millman, Daniel; Mihalas, Stefan; Kirkwood, Alfredo; Niebur, Ernst

2010-10-01

During sleep, under anaesthesia and in vitro, cortical neurons in sensory, motor, association and executive areas fluctuate between so-called up and down states, which are characterized by distinct membrane potentials and spike rates. Another phenomenon observed in preparations similar to those that exhibit up and down states-such as anaesthetized rats, brain slices and cultures devoid of sensory input, as well as awake monkey cortex-is self-organized criticality (SOC). SOC is characterized by activity `avalanches' with a branching parameter near unity and size distribution that obeys a power law with a critical exponent of about -3/2. Recent work has demonstrated SOC in conservative neuronal network models, but critical behaviour breaks down when biologically realistic `leaky' neurons are introduced. Here, we report robust SOC behaviour in networks of non-conservative leaky integrate-and-fire neurons with short-term synaptic depression. We show analytically and numerically that these networks typically have two stable activity levels, corresponding to up and down states, that the networks switch spontaneously between these states and that up states are critical and down states are subcritical.

Self-organization via active exploration in robotic applications

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Ogmen, H.; Prakash, R. V.

1992-01-01

We describe a neural network based robotic system. Unlike traditional robotic systems, our approach focussed on non-stationary problems. We indicate that self-organization capability is necessary for any system to operate successfully in a non-stationary environment. We suggest that self-organization should be based on an active exploration process. We investigated neural architectures having novelty sensitivity, selective attention, reinforcement learning, habit formation, flexible criteria categorization properties and analyzed the resulting behavior (consisting of an intelligent initiation of exploration) by computer simulations. While various computer vision researchers acknowledged recently the importance of active processes (Swain and Stricker, 1991), the proposed approaches within the new framework still suffer from a lack of self-organization (Aloimonos and Bandyopadhyay, 1987; Bajcsy, 1988). A self-organizing, neural network based robot (MAVIN) has been recently proposed (Baloch and Waxman, 1991). This robot has the capability of position, size rotation invariant pattern categorization, recognition and pavlovian conditioning. Our robot does not have initially invariant processing properties. The reason for this is the emphasis we put on active exploration. We maintain the point of view that such invariant properties emerge from an internalization of exploratory sensory-motor activity. Rather than coding the equilibria of such mental capabilities, we are seeking to capture its dynamics to understand on the one hand how the emergence of such invariances is possible and on the other hand the dynamics that lead to these invariances. The second point is crucial for an adaptive robot to acquire new invariances in non-stationary environments, as demonstrated by the inverting glass experiments of Helmholtz. We will introduce Pavlovian conditioning circuits in our future work for the precise objective of achieving the generation, coordination, and internalization

Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

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Rahmani, S.; Teimoorinia, H.; Barmby, P.

2018-05-01

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

Self-organization of spatial patterning in human embryonic stem cells

Science.gov (United States)

Deglincerti, Alessia; Etoc, Fred; Ozair, M. Zeeshan; Brivanlou, Ali H.

2017-01-01

The developing embryo is a remarkable example of self-organization, where functional units are created in a complex spatio-temporal choreography. Recently, human embryonic stem cells (ESCs) have been used to recapitulate in vitro the self-organization programs that are executed in the embryo in vivo. This represents a unique opportunity to address self-organization in humans that is otherwise not addressable with current technologies. In this essay, we review the recent literature on self-organization of human ESCs, with a particular focus on two examples: formation of embryonic germ layers and neural rosettes. Intriguingly, both activation and elimination of TGFβ signaling can initiate self-organization, albeit with different molecular underpinnings. We discuss the mechanisms underlying the formation of these structures in vitro and explore future challenges in the field. PMID:26970615

A NEW RECOGNITION TECHNIQUE NAMED SOMP BASED ON PALMPRINT USING NEURAL NETWORK BASED SELF ORGANIZING MAPS

Directory of Open Access Journals (Sweden)

A. S. Raja

2012-08-01

Full Text Available The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Palmprint has become a new class of human biometrics for passive identification with uniqueness and stability. This is considered to be reliable due to the lack of expressions and the lesser effect of aging. In this manuscript a new Palmprint based biometric system based on neural networks self organizing maps (SOM is presented. The method is named as SOMP. The paper shows that the proposed SOMP method improves the performance and robustness of recognition. The proposed method is applied to a variety of datasets and the results are shown.

9th Workshop on Self-Organizing Maps

CERN Document Server

Príncipe, José; Zegers, Pablo

2013-01-01

Self-organizing maps (SOMs) were developed by Teuvo Kohonen in the early eighties. Since then more than 10,000 works have been based on SOMs. SOMs are unsupervised neural networks useful for clustering and visualization purposes. Many SOM applications have been developed in engineering and science, and other fields. This book contains refereed papers presented at the 9th Workshop on Self-Organizing Maps (WSOM 2012) held at the Universidad de Chile, Santiago, Chile, on December 12-14, 2012. The workshop brought together researchers and practitioners in the field of self-organizing systems. Among the book chapters there are excellent examples of the use of SOMs in agriculture, computer science, data visualization, health systems, economics, engineering, social sciences, text and image analysis, and time series analysis. Other chapters present the latest theoretical work on SOMs as well as Learning Vector Quantization (LVQ) methods.

Self-Organization of Spatial Patterning in Human Embryonic Stem Cells.

Science.gov (United States)

Deglincerti, Alessia; Etoc, Fred; Ozair, M Zeeshan; Brivanlou, Ali H

2016-01-01

The developing embryo is a remarkable example of self-organization, where functional units are created in a complex spatiotemporal choreography. Recently, human embryonic stem cells (ESCs) have been used to recapitulate in vitro the self-organization programs that are executed in the embryo in vivo. This represents an unique opportunity to address self-organization in humans that is otherwise not addressable with current technologies. In this chapter, we review the recent literature on self-organization of human ESCs, with a particular focus on two examples: formation of embryonic germ layers and neural rosettes. Intriguingly, both activation and elimination of TGFβ signaling can initiate self-organization, albeit with different molecular underpinnings. We discuss the mechanisms underlying the formation of these structures in vitro and explore future challenges in the field. © 2016 Elsevier Inc. All rights reserved.

Neural Mechanism for Mirrored Self-face Recognition.

Science.gov (United States)

Sugiura, Motoaki; Miyauchi, Carlos Makoto; Kotozaki, Yuka; Akimoto, Yoritaka; Nozawa, Takayuki; Yomogida, Yukihito; Hanawa, Sugiko; Yamamoto, Yuki; Sakuma, Atsushi; Nakagawa, Seishu; Kawashima, Ryuta

2015-09-01

Self-face recognition in the mirror is considered to involve multiple processes that integrate 2 perceptual cues: temporal contingency of the visual feedback on one's action (contingency cue) and matching with self-face representation in long-term memory (figurative cue). The aim of this study was to examine the neural bases of these processes by manipulating 2 perceptual cues using a "virtual mirror" system. This system allowed online dynamic presentations of real-time and delayed self- or other facial actions. Perception-level processes were identified as responses to only a single perceptual cue. The effect of the contingency cue was identified in the cuneus. The regions sensitive to the figurative cue were subdivided by the response to a static self-face, which was identified in the right temporal, parietal, and frontal regions, but not in the bilateral occipitoparietal regions. Semantic- or integration-level processes, including amodal self-representation and belief validation, which allow modality-independent self-recognition and the resolution of potential conflicts between perceptual cues, respectively, were identified in distinct regions in the right frontal and insular cortices. The results are supportive of the multicomponent notion of self-recognition and suggest a critical role for contingency detection in the co-emergence of self-recognition and empathy in infants. © The Author 2014. Published by Oxford University Press.

Criticality and avalanches in neural networks

International Nuclear Information System (INIS)

Zare, Marzieh; Grigolini, Paolo

2013-01-01

Highlights: • Temporal criticality is used as criticality indicator. • The Mittag–Leffler function is proposed as a proper form of temporal complexity. • The distribution of avalanche size becomes scale free in the supercritical state. • The scale-free distribution of avalanche sizes is an epileptic manifestation. -- Abstract: Experimental work, both in vitro and in vivo, reveals the occurrence of neural avalanches with an inverse power law distribution in size and time duration. These properties are interpreted as an evident manifestation of criticality, thereby suggesting that the brain is an operating near criticality complex system: an attractive theoretical perspective that according to Gerhard Werner may help to shed light on the origin of consciousness. However, a recent experimental observation shows no clear evidence for power-law scaling in awake and sleeping brain of mammals, casting doubts on the assumption that the brain works at criticality. This article rests on a model proposed by our group in earlier publications to generate neural avalanches with the time duration and size distribution matching the experimental results on neural networks. We now refine the analysis of the time distance between consecutive firing bursts and observe the deviation of the corresponding distribution from the Poisson statistics, as the system moves from the non-cooperative to the cooperative regime. In other words, we make the assumption that the genuine signature of criticality may emerge from temporal complexity rather than from the size and time duration of avalanches. We argue that the Mittag–Leffler (ML) exponential function is a satisfactory indicator of temporal complexity, namely of the occurrence of non-Poisson and renewal events. The assumption that the onset of criticality corresponds to the birth of renewal non-Poisson events establishes a neat distinction between the ML function and the power law avalanches generating regime. We find that

Effects of Vertex Activity and Self-organized Criticality Behavior on a Weighted Evolving Network

International Nuclear Information System (INIS)

Zhang Guiqing; Yang Qiuying; Chen Tianlun

2008-01-01

Effects of vertex activity have been analyzed on a weighted evolving network. The network is characterized by the probability distribution of vertex strength, each edge weight and evolution of the strength of vertices with different vertex activities. The model exhibits self-organized criticality behavior. The probability distribution of avalanche size for different network sizes is also shown. In addition, there is a power law relation between the size and the duration of an avalanche and the average of avalanche size has been studied for different vertex activities

Neural basis of individualistic and collectivistic views of self.

Science.gov (United States)

Chiao, Joan Y; Harada, Tokiko; Komeda, Hidetsugu; Li, Zhang; Mano, Yoko; Saito, Daisuke; Parrish, Todd B; Sadato, Norihiro; Iidaka, Tetsuya

2009-09-01

Individualism and collectivism refer to cultural values that influence how people construe themselves and their relation to the world. Individualists perceive themselves as stable entities, autonomous from other people and their environment, while collectivists view themselves as dynamic entities, continually defined by their social context and relationships. Despite rich understanding of how individualism and collectivism influence social cognition at a behavioral level, little is known about how these cultural values modulate neural representations underlying social cognition. Using cross-cultural functional magnetic resonance imaging (fMRI), we examined whether the cultural values of individualism and collectivism modulate neural activity within medial prefrontal cortex (MPFC) during processing of general and contextual self judgments. Here, we show that neural activity within the anterior rostral portion of the MPFC during processing of general and contextual self judgments positively predicts how individualistic or collectivistic a person is across cultures. These results reveal two kinds of neural representations of self (eg, a general self and a contextual self) within MPFC and demonstrate how cultural values of individualism and collectivism shape these neural representations. 2008 Wiley-Liss, Inc.

Ultrametricity and memory in a solvable model of self-organized criticality

International Nuclear Information System (INIS)

Boettcher, S.; Paczuski, M.

1996-01-01

Slowly driven dissipative systems may evolve to a critical state where long periods of apparent equilibrium are punctuated by intermittent avalanches of activity. We present a self-organized critical model of punctuated equilibrium behavior in the context of biological evolution, and solve it in the limit that the number of independent traits for each species diverges. We derive an exact equation of motion for the avalanche dynamics from the microscopic rules. In the continuum limit, avalanches propagate via a diffusion equation with a nonlocal, history dependent potential representing memory. This nonlocal potential gives rise to a non-Gaussian (fat) tail for the subdiffusive spreading of activity. The probability for the activity to spread beyond a distance r in time s decays as √(24/π)s -3/2 x 1/3 exp[-3/4x 1/3 ] for x=r 4 /s>1. The potential represents a hierarchy of time scales that is dynamically generated by the ultrametric structure of avalanches, which can be quantified in terms of open-quote open-quote backward close-quote close-quote avalanches. In addition, a number of other correlation functions characterizing the punctuated equilibrium dynamics are determined exactly

Slip-Size Distribution and Self-Organized Criticality in Block-Spring Models with Quenched Randomness

Science.gov (United States)

Sakaguchi, Hidetsugu; Kadowaki, Shuntaro

2017-07-01

We study slowly pulling block-spring models in random media. Second-order phase transitions exist in a model pulled by a constant force in the case of velocity-strengthening friction. If external forces are slowly increased, nearly critical states are self-organized. Slips of various sizes occur, and the probability distributions of slip size roughly obey power laws. The exponent is close to that in the quenched Edwards-Wilkinson model. Furthermore, the slip-size distributions are investigated in cases of Coulomb friction, velocity-weakening friction, and two-dimensional block-spring models.

Religious beliefs influence neural substrates of self-reflection in Tibetans.

Science.gov (United States)

Wu, Yanhong; Wang, Cheng; He, Xi; Mao, Lihua; Zhang, Li

2010-06-01

Previous transcultural neuroimaging studies have shown that the neural substrates of self-reflection can be shaped by different cultures. There are few studies, however, on the neural activity of self-reflection where religion is viewed as a form of cultural expression. The present study examined the self-processing of two Chinese ethnic groups (Han and Tibetan) to investigate the significant role of religion on the functional anatomy of self-representation. We replicated the previous results in Han participants with the ventral medial prefrontal cortex and left anterior cingulate cortex showing stronger activation in self-processing when compared with other-processing conditions. However, no typical self-reference pattern was identified in Tibetan participants on behavioral or neural levels. This could be explained by the minimal subjective sense of 'I-ness' in Tibetan Buddhists. Our findings lend support to the presumed role of culture and religion in shaping the neural substrate of self.

Self-organization, Networks, Future

Directory of Open Access Journals (Sweden)

T. S. Akhromeyeva

2013-01-01

Full Text Available This paper presents an analytical review of a conference on the great scientist, a brilliant professor, an outstanding educator Sergei Kapitsa, held in November 2012. In the focus of this forum were problems of self-organization and a paradigm of network structures. The use of networks in the context of national defense, economics, management of mass consciousness was discussed. The analysis of neural networks in technical systems, the structure of the brain, as well as in the space of knowledge, information, and behavioral strategies plays an important role. One of the conference purposes was to an online organize community in Russia and to identify the most promising directions in this field. Some of them are presented in this paper.

Self-regulated dynamical criticality in human ECoG

Directory of Open Access Journals (Sweden)

Guillermo eSolovey

2012-07-01

Full Text Available Mounting experimental and theoretical results indicate that neural systems are poised near a critical state. In human subjects, however, most evidence comes from functional MRI studies, an indirect measurement of neuronal activity with poor temporal resolution. Electrocorticography (ECoG provides a unique window into human brain activity: each electrode records, with high temporal resolution, the activity resulting from the sum of the local field potentials of sim 10^5 neurons. We show that the human brain ECoG recordings display features of self-regulated dynamical criticality: dynamical modes of activation drift around the critical stability threshold, moving in and out of the unstable region and equilibrating the global dynamical state at a very fast time scale. Moreover, the analysis also reveals differences between the resting state and a motor task, associated with increased stability of a fraction of the dynamical modes.

Self-control with spiking and non-spiking neural networks playing games.

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Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

2010-01-01

Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the

Recent results on analytical plasma turbulence theory: Realizability, intermittency, submarginal turbulence, and self-organized criticality

International Nuclear Information System (INIS)

Krommes, J.A.

2000-01-01

Recent results and future challenges in the systematic analytical description of plasma turbulence are described. First, the importance of statistical realizability is stressed, and the development and successes of the Realizable Markovian Closure are briefly reviewed. Next, submarginal turbulence (linearly stable but nonlinearly self-sustained fluctuations) is considered and the relevance of nonlinear instability in neutral-fluid shear flows to submarginal turbulence in magnetized plasmas is discussed. For the Hasegawa-Wakatani equations, a self-consistency loop that leads to steady-state vortex regeneration in the presence of dissipation is demonstrated and a partial unification of recent work of Drake (for plasmas) and of Waleffe (for neutral fluids) is given. Brief remarks are made on the difficulties facing a quantitatively accurate statistical description of submarginal turbulence. Finally, possible connections between intermittency, submarginal turbulence, and self-organized criticality (SOC) are considered and outstanding questions are identified

Crisis Behavior in Autism Spectrum Disorders: A Self-Organized Criticality Approach

Directory of Open Access Journals (Sweden)

Lucio Tonello

2018-01-01

Full Text Available The Autism Spectrum Disorder (ASD represents a set of life-long disorders. In particular, subjects with ASD can display momentary behaviors of acute agitation and aggressiveness called crisis behaviors. These events are problematic for the subject and care providers but little is known about their occurrence, namely, possible relations among intensity, frequency, and duration. A group of ASD subjects (n=33 has been observed for 12 months reporting data on each crisis (n=1137 crises. Statistical analysis did not find significant results, while the relation between crisis duration and frequency showed a good fit to a “power law” curve, suggesting the application of Self-Organized Criticality (SOC model. The SOC is used to describe natural phenomena as earthquakes, bank failures of rivers, mass extinctions, and other systems where a type of “catastrophic events” is necessary to maintain a critical equilibrium. In a sense, subjects at risk of crisis behavior seem to fit the same model as seismic zones at risk of earthquakes. The employment of the same strategies, as those successfully developed for known SOC systems, could lead to important insights for ASD management. Moreover, the SOC model offers possible interpretations of crisis behavior dynamics suggesting that they are unpredictable and, in a sense, necessary.

Self esteem, dependency, self-efficacy and self-criticism in social anxiety disorder.

Science.gov (United States)

Iancu, Iulian; Bodner, Ehud; Ben-Zion, Itzhak Z

2015-04-01

Social anxiety disorder (SAD) is characterized by fear and avoidance in social situations where one perceives being in danger of scrutiny by others. Low self-esteem, low self-efficacy, high self-criticism and high dependency are additional potential features of SAD, and thus their examination is warranted, as is the elucidation of their inter-relationship. Thirty-two SAD subjects diagnosed with the Mini-International Neuropsychiatric Interview and 30 healthy controls, were administered the Liebowitz Social Anxiety Scale (LSAS), the Rosenberg Self Esteem Scale, the Depressive Experiences Questionnaire (DEQ) that assesses self-criticism, dependency and self-efficacy, and a socio-demographic questionnaire. We hypothesized that the SAD group would present higher scores of dependency and self-criticism and lower self-esteem and self-efficacy. We also hypothesized that low self-esteem, low self-efficacy, high self-criticism and high dependency will predict the severity of SAD. In line with the hypotheses, SAD patients had higher scores of self-criticism and dependency and lower scores of self-esteem. The social anxiety score correlated negatively with self-esteem and self-efficacy, and positively with dependency and self-criticism. Self-criticism, but not the other measures, predicted the total LSAS score. Self-esteem, self-criticism, dependency and self-efficacy are related to SAD and their relations should be examined in future studies that will employ larger samples. It is suggested to search for ways to affect these factors through cognitive-behavioral interventions and additional psychotherapeutic treatments. Research should also focus on the specific role of self-criticism in SAD. Copyright © 2014 Elsevier Inc. All rights reserved.

Obtaining parton distribution functions from self-organizing maps

International Nuclear Information System (INIS)

Honkanen, H.; Liuti, S.; Loitiere, Y.C.; Brogan, D.; Reynolds, P.

2007-01-01

We present an alternative algorithm to global fitting procedures to construct Parton Distribution Functions parametrizations. The proposed algorithm uses Self-Organizing Maps which at variance with the standard Neural Networks, are based on competitive-learning. Self-Organizing Maps generate a non-uniform projection from a high dimensional data space onto a low dimensional one (usually 1 or 2 dimensions) by clustering similar PDF representations together. The SOMs are trained on progressively narrower selections of data samples. The selection criterion is that of convergence towards a neighborhood of the experimental data. All available data sets on deep inelastic scattering in the kinematical region of 0.001 ≤ x ≤ 0.75, and 1 ≤ Q 2 ≤ 100 GeV 2 , with a cut on the final state invariant mass, W 2 ≥ 10 GeV 2 were implemented. The proposed fitting procedure, at variance with standard neural network approaches, allows for an increased control of the systematic bias by enabling the user to directly control the data selection procedure at various stages of the process. (author)

Instantons in Self-Organizing Logic Gates

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Bearden, Sean R. B.; Manukian, Haik; Traversa, Fabio L.; Di Ventra, Massimiliano

2018-03-01

Self-organizing logic is a recently suggested framework that allows the solution of Boolean truth tables "in reverse"; i.e., it is able to satisfy the logical proposition of gates regardless to which terminal(s) the truth value is assigned ("terminal-agnostic logic"). It can be realized if time nonlocality (memory) is present. A practical realization of self-organizing logic gates (SOLGs) can be done by combining circuit elements with and without memory. By employing one such realization, we show, numerically, that SOLGs exploit elementary instantons to reach equilibrium points. Instantons are classical trajectories of the nonlinear equations of motion describing SOLGs and connect topologically distinct critical points in the phase space. By linear analysis at those points, we show that these instantons connect the initial critical point of the dynamics, with at least one unstable direction, directly to the final fixed point. We also show that the memory content of these gates affects only the relaxation time to reach the logically consistent solution. Finally, we demonstrate, by solving the corresponding stochastic differential equations, that, since instantons connect critical points, noise and perturbations may change the instanton trajectory in the phase space but not the initial and final critical points. Therefore, even for extremely large noise levels, the gates self-organize to the correct solution. Our work provides a physical understanding of, and can serve as an inspiration for, models of bidirectional logic gates that are emerging as important tools in physics-inspired, unconventional computing.

The neural sociometer: brain mechanisms underlying state self-esteem.

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Eisenberger, Naomi I; Inagaki, Tristen K; Muscatell, Keely A; Byrne Haltom, Kate E; Leary, Mark R

2011-11-01

On the basis of the importance of social connection for survival, humans may have evolved a "sociometer"-a mechanism that translates perceptions of rejection or acceptance into state self-esteem. Here, we explored the neural underpinnings of the sociometer by examining whether neural regions responsive to rejection or acceptance were associated with state self-esteem. Participants underwent fMRI while viewing feedback words ("interesting," "boring") ostensibly chosen by another individual (confederate) to describe the participant's previously recorded interview. Participants rated their state self-esteem in response to each feedback word. Results demonstrated that greater activity in rejection-related neural regions (dorsal ACC, anterior insula) and mentalizing regions was associated with lower-state self-esteem. Additionally, participants whose self-esteem decreased from prescan to postscan versus those whose self-esteem did not showed greater medial prefrontal cortical activity, previously associated with self-referential processing, in response to negative feedback. Together, the results inform our understanding of the origin and nature of our feelings about ourselves.

Ink4a and Arf differentially affect cell proliferation and neural stem cell self-renewal in Bmi1-deficient mice

NARCIS (Netherlands)

Bruggeman, SWM; Valk-Lingbeek, ME; van der Stoop, PPM; Jacobs, JJL; Kieboom, K; Tanger, E; Hulsman, D; Leung, C; Arsenijevic, Y; Marino, S; van Lohuizen, M

2005-01-01

The Polycomb group (PcG) gene Bmi1 promotes cell proliferation and stem cell self-renewal by repressing the Ink4a/Arf locus. We used a genetic approach to investigate whether Ink4a or Arf is more critical for relaying Bmi1 function in lymphoid cells, neural progenitors, and neural stem cells. We

Self-organizing map models of language acquisition

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Li, Ping; Zhao, Xiaowei

2013-01-01

Connectionist models have had a profound impact on theories of language. While most early models were inspired by the classic parallel distributed processing architecture, recent models of language have explored various other types of models, including self-organizing models for language acquisition. In this paper, we aim at providing a review of the latter type of models, and highlight a number of simulation experiments that we have conducted based on these models. We show that self-organizing connectionist models can provide significant insights into long-standing debates in both monolingual and bilingual language development. We suggest future directions in which these models can be extended, to better connect with behavioral and neural data, and to make clear predictions in testing relevant psycholinguistic theories. PMID:24312061

A continuum self organized critically model of turbulent heat transport in tokamaks

Energy Technology Data Exchange (ETDEWEB)

Tangri, V; Das, A; Kaw, P; Singh, R [Institute for Plasma Research, Gandhinagar (India)

2003-09-01

Based on the now well known and experimentally observed critical gradient length (R/L{sub Te} = RT/{nabla}T) in tokamaks, we present a continuum one dimensional model for explaining self organized heat transport in tokamaks. Key parameters of this model include a novel hysteresis parameter which ensures that the switch of heat transport coefficient {chi} upwards and downwards takes place at two different values of R/L{sub Te}. Extensive numerical simulations of this model reproduce many features of present day tokamaks such as submarginal temperature profiles, intermittent transport events, 1/f scaling of the frequency spectra, propagating fronts, etc. This model utilises a minimal set of phenomenological parameters, which may be determined from experiments and/or simulations. Analytical and physical understanding of the observed features has also been attempted. (author)

Self-construal differences in neural responses to negative social cues.

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Liddell, Belinda J; Felmingham, Kim L; Das, Pritha; Whitford, Thomas J; Malhi, Gin S; Battaglini, Eva; Bryant, Richard A

2017-10-01

Cultures differ substantially in representations of the self. Whereas individualistic cultural groups emphasize an independent self, reflected in processing biases towards centralized salient objects, collectivistic cultures are oriented towards an interdependent self, attending to contextual associations between visual cues. It is unknown how these perceptual biases may affect brain activity in response to negative social cues. Moreover, while some studies have shown that individual differences in self-construal moderate cultural group comparisons, few have examined self-construal differences separate to culture. To investigate these issues, a final sample of a group of healthy participants high in trait levels of collectivistic self-construal (n=16) and individualistic self-construal (n=19), regardless of cultural background, completed a negative social cue evaluation task designed to engage face/object vs context-specific neural processes whilst undergoing fMRI scanning. Between-group analyses revealed that the collectivistic group exclusively engaged the parahippocampal gyrus (parahippocampal place area) - a region critical to contextual integration - during negative face processing - suggesting compensatory activations when contextual information was missing. The collectivist group also displayed enhanced negative context dependent brain activity involving the left superior occipital gyrus/cuneus and right anterior insula. By contrast, the individualistic group did not engage object or localized face processing regions as predicted, but rather demonstrated heightened appraisal and self-referential activations in medial prefrontal and temporoparietal regions to negative contexts - again suggesting compensatory processes when focal cues were absent. While individualists also appeared more sensitive to negative faces in the scenes, activating the right middle cingulate gyrus, dorsal prefrontal and parietal activations, this activity was observed relative to the

Scaling laws and indications of self-organized criticality in urban systems

International Nuclear Information System (INIS)

Chen Yanguang; Zhou Yixing

2008-01-01

Evolution of urban systems has been considered to exhibit some form of self-organized criticality (SOC) in the literature. This paper provides further mathematical foundations and empirical evidences to support the supposition. The hierarchical structure of systems of cities can be formulated as three exponential functions: the number law, the population size law, and the area law. These laws are identical in form to the Horton-Strahler laws of rivers and Gutenberg-Richter laws of earthquakes. From the exponential functions, three indications of SOC are also derived: the frequency-spectrum relation indicting the 1/f noise, the power laws indicating the fractal structure, and the Zipf's law indicating the rank-size distribution. These mathematical models form a set of scaling laws for urban systems, as demonstrated in the empirical study of the system of cities in China. The fact that the scaling laws of urban systems bear an analogy to those on rivers and earthquakes lends further support to the notion of possible SOC in urban systems

Habituation in non-neural organisms: evidence from slime moulds

OpenAIRE

Boisseau, Romain P.; Vogel, David; Dussutour, Audrey

2016-01-01

Learning, defined as a change in behaviour evoked by experience, has hitherto been investigated almost exclusively in multicellular neural organisms. Evidence for learning in non-neural multicellular organisms is scant, and only a few unequivocal reports of learning have been described in single-celled organisms. Here we demonstrate habituation, an unmistakable form of learning, in the non-neural organism Physarum polycephalum. In our experiment, using chemotaxis as the behavioural output and...

Non-Taylor magnetohydrodynamic self-organization

International Nuclear Information System (INIS)

Zhu, Shao-ping; Horiuchi, Ritoku; Sato, Tetsuya.

1994-10-01

A self-organization process in a plasma with a finite pressure is investigated by means of a three-dimensional magnetohydrodynamic simulation. It is demonstrated that a non-Taylor finite β self-organized state is realized in which a perpendicular component of the electric current is generated and the force-free(parallel) current decreases until they reach to almost the same level. The self-organized state is described by an MHD force-balance relation, namely, j perpendicular = B x ∇p/B·B and j parallel = μB where μ is not a constant, and the pressure structure resembles the structure of the toroidal magnetic field intensity. Unless an anomalous perpendicular thermal conduction arises, the plasma cannot relax to a Taylor state but to a non-Taylor (non-force-free) self-organized state. This state becomes more prominent for a weaker resistivity condition. The non-Taylor state has a rather universal property, for example, independence of the initial β value. Another remarkable finding is that the Taylor's conjecture of helicity conservation is, in a strict sense, not valid. The helicity dissipation occurs and its rate slows down critically in accordance with the stepwise relaxation of the magnetic energy. It is confirmed that the driven magnetic reconnection caused by the nonlinearly excited plasma kink flows plays the leading role in all of these key features of the non-Taylor self-organization. (author)

Different neural processes accompany self-recognition in photographs across the lifespan: an ERP study using dizygotic twins.

Science.gov (United States)

Butler, David L; Mattingley, Jason B; Cunnington, Ross; Suddendorf, Thomas

2013-01-01

Our appearance changes over time, yet we can recognize ourselves in photographs from across the lifespan. Researchers have extensively studied self-recognition in photographs and have proposed that specific neural correlates are involved, but few studies have examined self-recognition using images from different periods of life. Here we compared ERP responses to photographs of participants when they were 5-15, 16-25, and 26-45 years old. We found marked differences between the responses to photographs from these time periods in terms of the neural markers generally assumed to reflect (i) the configural processing of faces (i.e., the N170), (ii) the matching of the currently perceived face to a representation already stored in memory (i.e., the P250), and (iii) the retrieval of information about the person being recognized (i.e., the N400). There was no uniform neural signature of visual self-recognition. To test whether there was anything specific to self-recognition in these brain responses, we also asked participants to identify photographs of their dizygotic twins taken from the same time periods. Critically, this allowed us to minimize the confounding effects of exposure, for it is likely that participants have been similarly exposed to each other's faces over the lifespan. The same pattern of neural response emerged with only one exception: the neural marker reflecting the retrieval of mnemonic information (N400) differed across the lifespan for self but not for twin. These results, as well as our novel approach using twins and photographs from across the lifespan, have wide-ranging consequences for the study of self-recognition and the nature of our personal identity through time.

Different neural processes accompany self-recognition in photographs across the lifespan: an ERP study using dizygotic twins.

Directory of Open Access Journals (Sweden)

David L Butler

Full Text Available Our appearance changes over time, yet we can recognize ourselves in photographs from across the lifespan. Researchers have extensively studied self-recognition in photographs and have proposed that specific neural correlates are involved, but few studies have examined self-recognition using images from different periods of life. Here we compared ERP responses to photographs of participants when they were 5-15, 16-25, and 26-45 years old. We found marked differences between the responses to photographs from these time periods in terms of the neural markers generally assumed to reflect (i the configural processing of faces (i.e., the N170, (ii the matching of the currently perceived face to a representation already stored in memory (i.e., the P250, and (iii the retrieval of information about the person being recognized (i.e., the N400. There was no uniform neural signature of visual self-recognition. To test whether there was anything specific to self-recognition in these brain responses, we also asked participants to identify photographs of their dizygotic twins taken from the same time periods. Critically, this allowed us to minimize the confounding effects of exposure, for it is likely that participants have been similarly exposed to each other's faces over the lifespan. The same pattern of neural response emerged with only one exception: the neural marker reflecting the retrieval of mnemonic information (N400 differed across the lifespan for self but not for twin. These results, as well as our novel approach using twins and photographs from across the lifespan, have wide-ranging consequences for the study of self-recognition and the nature of our personal identity through time.

Influence of Selective Edge Removal and Refractory Period in a Self-Organized Critical Neuron Model

International Nuclear Information System (INIS)

Lin Min; Gang, Zhao; Chen Tianlun

2009-01-01

A simple model for a set of integrate-and-fire neurons based on the weighted network is introduced. By considering the neurobiological phenomenon in brain development and the difference of the synaptic strength, we construct weighted networks develop with link additions and followed by selective edge removal. The network exhibits the small-world and scale-free properties with high network efficiency. The model displays an avalanche activity on a power-law distribution. We investigate the effect of selective edge removal and the neuron refractory period on the self-organized criticality of the system. (condensed matter: structural, mechanical, and thermal properties)

Self-organized criticality: An interplay between stable and turbulent regimes of multiple anodic double layers in glow discharge plasma

Science.gov (United States)

Alex, Prince; Carreras, Benjamin Andres; Arumugam, Saravanan; Sinha, Suraj Kumar

2018-05-01

The role of self-organized criticality (SOC) in the transformation of multiple anodic double layers (MADLs) from the stable to turbulent regime has been investigated experimentally as the system approaches towards critical behavior. The experiment was performed in a modified glow discharge plasma setup, and the initial stable state of MADL comprising three concentric perceptible layers was produced when the drift velocity of electrons towards the anode exceeds the electron thermal velocity (νd ≥ 1.3νte). The macroscopic arrangement of both positive and negative charges in opposite layers of MADL is attributed to the self-organization scenario. Beyond νd ≥ 3νte, MADL begins to collapse and approaches critical and supercritical states through layer reduction which continue till the last remaining layer of the double layer is transformed into a highly unstable radiant anode glow. The avalanche resulting from the collapse of MADL leads to the rise of turbulence in the system. Long-range correlations, a key signature of SOC, have been explored in the turbulent floating potential fluctuations using the rescaled-range analysis technique. The result shows that the existence of the self-similarity regime with self-similarity parameter H varies between 0.55 and 0.91 for time lags longer than the decorrelation time. The power law tail in the rank function, slowly decaying tail of the autocorrelation function, and 1/f behavior of the power spectra of the fluctuations are consistent with the fact that SOC plays a conclusive role in the transformation of MADL from the stable to turbulent regime. Since the existence of SOC gives a measure of complexity in the system, the result provides the condition under which complexity arises in cold plasma.

Inducing self-organized criticality in a network toy model by neighborhood assortativity.

Science.gov (United States)

Allen-Perkins, Alfonso; Galeano, Javier; Pastor, Juan Manuel

2016-11-01

Complex networks are a recent type of framework used to study complex systems with many interacting elements, such as self-organized criticality (SOC). The network nodes' tendency to link to other nodes of similar type is characterized by assortative mixing. Real networks exhibit assortative mixing by vertex degree, however, typical random network models, such as the Erdős-Rényi or the Barabási-Albert model, show no assortative arrangements. In this paper we introduce the notion of neighborhood assortativity as the tendency of a node to belong to a community (its neighborhood) showing an average property similar to its own. Imposing neighborhood assortative mixing by degree in a network toy model, SOC dynamics can be found. These dynamics are driven only by the network topology. The long-range correlations resulting from criticality have been characterized by means of fluctuation analysis and show an anticorrelation in the node's activity. The model contains only one parameter and its statistics plots for different values of the parameter can be collapsed into a single curve. The simplicity of the model allows us to perform numerical simulations and also to study analytically the statistics for a specific value of the parameter, making use of the Markov chains.

Neural activity in the reward-related brain regions predicts implicit self-esteem: A novel validity test of psychological measures using neuroimaging.

Science.gov (United States)

Izuma, Keise; Kennedy, Kate; Fitzjohn, Alexander; Sedikides, Constantine; Shibata, Kazuhisa

2018-03-01

Self-esteem, arguably the most important attitudes an individual possesses, has been a premier research topic in psychology for more than a century. Following a surge of interest in implicit attitude measures in the 90s, researchers have tried to assess self-esteem implicitly to circumvent the influence of biases inherent in explicit measures. However, the validity of implicit self-esteem measures remains elusive. Critical tests are often inconclusive, as the validity of such measures is examined in the backdrop of imperfect behavioral measures. To overcome this serious limitation, we tested the neural validity of the most widely used implicit self-esteem measure, the implicit association test (IAT). Given the conceptualization of self-esteem as attitude toward the self, and neuroscience findings that the reward-related brain regions represent an individual's attitude or preference for an object when viewing its image, individual differences in implicit self-esteem should be associated with neural signals in the reward-related regions during passive-viewing of self-face (the most obvious representation of the self). Using multi-voxel pattern analysis (MVPA) on functional MRI (fMRI) data, we demonstrate that the neural signals in the reward-related regions were robustly associated with implicit (but not explicit) self-esteem, thus providing unique evidence for the neural validity of the self-esteem IAT. In addition, both implicit and explicit self-esteem were related, although differently, to neural signals in regions involved in self-processing. Our finding highlights the utility of neuroscience methods in addressing fundamental psychological questions and providing unique insights into important psychological constructs. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network

Science.gov (United States)

Hoomod, Haider K.; Kareem Jebur, Tuka

2018-05-01

Mobile ad hoc networks (MANETs) play a critical role in today’s wireless ad hoc network research and consist of active nodes that can be in motion freely. Because it consider very important problem in this network, we suggested proposed method based on modified radial basis function networks RBFN and Self-Organizing Map SOM. These networks can be improved by the use of clusters because of huge congestion in the whole network. In such a system, the performance of MANET is improved by splitting the whole network into various clusters using SOM. The performance of clustering is improved by the cluster head selection and number of clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network. The SOM clustering average time (1-10 msec for stall nodes) and (8-75 msec for mobile nodes). While the routing time range (92-510 msec).The proposed system is faster than the Dijkstra by 150-300%, and faster from the RBFNN (without modify) by 145-180%.

Control of beam halo-chaos using neural network self-adaptation method

International Nuclear Information System (INIS)

Fang Jinqing; Huang Guoxian; Luo Xiaoshu

2004-11-01

Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

Analysis of mass incident diffusion in Weibo based on self-organization theory

Science.gov (United States)

Pan, Jun; Shen, Huizhang

2018-02-01

This study introduces some theories and methods of self-organization system to the research of the diffusion mechanism of mass incidents in Weibo (Chinese Twitter). Based on the analysis on massive Weibo data from Songjiang battery factory incident happened in 2013 and Jiiangsu Qidong OJI PAPER incident happened in 2012, we find out that diffusion system of mass incident in Weibo satisfies Power Law, Zipf's Law, 1/f noise and Self-similarity. It means this system is the self-organization criticality system and dissemination bursts can be understood as one kind of Self-organization behavior. As the consequence, self-organized criticality (SOC) theory can be used to explain the evolution of mass incident diffusion and people may come up with the right strategy to control such kind of diffusion if they can handle the key ingredients of Self-organization well. Such a study is of practical importance which can offer opportunities for policy makers to have good management on these events.

Self-reassurance, not self-esteem, serves as a buffer between self-criticism and depressive symptoms.

Science.gov (United States)

Petrocchi, Nicola; Dentale, Francesco; Gilbert, Paul

2018-06-15

Several studies suggest that self-criticism and self-reassurance operate through different mechanisms and might interact with each other. This study examined the hypothesis that self-reassurance serves as a buffer between self-criticism and depressive symptoms in a way that self-esteem, which is rooted in a different motivational system, may not. We hypothesized that self-criticism would be correlated with high levels of depressive symptoms, but that this association would be weaker at higher levels of self-reassurance abilities. We also hypothesized that self-esteem, a self-relating process based on feeling able and competent to achieve life goals, would not buffer the relationship between self-criticism and depression. Self-criticism, self-reassurance, depressive symptoms, and self-esteem were assessed in a sample of 419 participants (66% females; M age  = 33.40, SD = 11.13). At higher levels of self-reassurance, the relationship between self-criticism and depressive symptoms became non-significant, supporting the buffering hypothesis of self-reassurance. Despite the high correlation between self-esteem and self-reassurance, self-esteem did not moderate the relationship between self-criticism and depressive symptoms. Results support the growing evidence that not all positive self-relating processes exert the same protective function against psychopathological consequences of self-criticism. Implications for psychotherapy and the validity of using compassion-focused interventions with clients with self-critical issues are discussed. Self-reassurance and self-criticism are distinct processes and they should not be considered positive and negative variations of a single dimension Different types of positive self-relating do not show the same correlation with depressive symptoms. The ability to be self-reassuring protects against the psychopathological correlates of self-criticism while having high self-esteem does not. Compassion-focused interventions are

Self-organization phenomena in plasma physics

International Nuclear Information System (INIS)

Sanduloviciu, M.; Popescu, S.

2001-01-01

The self-assembling in nature and laboratory of structures in systems away from thermodynamic equilibrium is one of the problems that mostly fascinates the scientists working in all branches of science. In this context a substantial progress has been obtained by investigating the appearance of spatial and spatiotemporal patterns in plasma. These experiments revealed the presence of a scenario of self-organization able to suggest an answer to the central problem of the 'Science of Complexity', why matter transits spontaneously from a disordered into an ordered state? Based on this scenario of self-organization we present arguments proving the possibility to explain the challenging problems of nonequilibrium physics in general. These problems refer to: (i) genuine origin of phase transitions observed in gaseous conductors and semiconductors; (ii) the elucidation of the role played by self-organization in the simulation of oscillations; (iii) the physical basis of anomalous transport of matter and energy with special reference to the possibilities of improving the economical performance of fusion devices; (iv) the possibility to use self-confined gaseous space charged configurations as an alternative to the magnetically confined plasma used at present in fusion devices. In other branches of sciences, as for instance in Biology, the self-organization scenario reveals a new insight into a mechanism able to explain the appearance of the simplest possible space charge configuration able to evolve, under suitable conditions, into prebiotic structures. Referring to phenomena observed in nature, the same self-organization scenario suggests plausible answers to the appearance of ball lightening but also to the origin of the flickering phenomena observed in the light emission of the Sun and stars. For theory the described self-organization scenario offers a new physical basis for many problems of nonlinear science not solved yet and also a new model for the so-called 'self

Self-organizing neural networks for automatic detection and classification of contrast-enhancing lesions in dynamic MR-mammography; Selbstorganisierende neuronale Netze zur automatischen Detektion und Klassifikation von Kontrast(mittel)-verstaerkten Laesionen in der dynamischen MR-Mammographie

Energy Technology Data Exchange (ETDEWEB)

Vomweg, T.W.; Teifke, A.; Kauczor, H.U.; Achenbach, T.; Rieker, O.; Schreiber, W.G.; Heitmann, K.R.; Beier, T.; Thelen, M. [Klinik und Poliklinik fuer Radiologie, Klinikum der Univ. Mainz (Germany)

2005-05-01

Purpose: Investigation and statistical evaluation of 'Self-Organizing Maps', a special type of neural networks in the field of artificial intelligence, classifying contrast enhancing lesions in dynamic MR-mammography. Material and Methods: 176 investigations with proven histology after core biopsy or operation were randomly divided into two groups. Several Self-Organizing Maps were trained by investigations of the first group to detect and classify contrast enhancing lesions in dynamic MR-mammography. Each single pixel's signal/time curve of all patients within the second group was analyzed by the Self-Organizing Maps. The likelihood of malignancy was visualized by color overlays on the MR-images. At last assessment of contrast-enhancing lesions by each different network was rated visually and evaluated statistically. Results: A well balanced neural network achieved a sensitivity of 90.5% and a specificity of 72.2% in predicting malignancy of 88 enhancing lesions. Detailed analysis of false-positive results revealed that every second fibroadenoma showed a 'typical malignant' signal/time curve without any chance to differentiate between fibroadenomas and malignant tissue regarding contrast enhancement alone; but this special group of lesions was represented by a well-defined area of the Self-Organizing Map. Discussion: Self-Organizing Maps are capable of classifying a dynamic signal/time curve as 'typical benign' or 'typical malignant'. Therefore, they can be used as second opinion. In view of the now known localization of fibroadenomas enhancing like malignant tumors at the Self-Organizing Map, these lesions could be passed to further analysis by additional post-processing elements (e.g., based on T2-weighted series or morphology analysis) in the future. (orig.)

Neural correlates of processing "self-conscious" vs. "basic" emotions.

Science.gov (United States)

Gilead, Michael; Katzir, Maayan; Eyal, Tal; Liberman, Nira

2016-01-29

Self-conscious emotions are prevalent in our daily lives and play an important role in both normal and pathological behavior. Despite their immense significance, the neural substrates that are involved in the processing of such emotions are surprisingly under-studied. In light of this, we conducted an fMRI study in which participants thought of various personal events which elicited feelings of negative and positive self-conscious (i.e., guilt, pride) or basic (i.e., anger, joy) emotions. We performed a conjunction analysis to investigate the neural correlates associated with processing events that are related to self-conscious vs. basic emotions, irrespective of valence. The results show that processing self-conscious emotions resulted in activation within frontal areas associated with self-processing and self-control, namely, the mPFC extending to the dACC, and within the lateral-dorsal prefrontal cortex. Processing basic emotions resulted in activation throughout relatively phylogenetically-ancient regions of the cortex, namely in visual and tactile processing areas and in the insular cortex. Furthermore, self-conscious emotions differentially activated the mPFC such that the negative self-conscious emotion (guilt) was associated with a more dorsal activation, and the positive self-conscious emotion (pride) was associated with a more ventral activation. We discuss how these results shed light on the nature of mental representations and neural systems involved in self-reflective and affective processing. Copyright © 2015 Elsevier Ltd. All rights reserved.

Renormalization group and instantons in stochastic nonlinear dynamics, from self-organized criticality to thermonuclear reactors

International Nuclear Information System (INIS)

Volchenkov, D.

2009-01-01

Stochastic counterparts of nonlinear dynamics are studied by means of nonperturbative functional methods developed in the framework of quantum field theory (QFT). In particular, we discuss fully developed turbulence, including leading corrections on possible compressibility of fluids, transport through porous media, theory of waterspouts and tsunami waves, stochastic magnetohydrodynamics, turbulent transport in crossed fields, self-organized criticality, and dynamics of accelerated wrinkled flame fronts advancing in a wide canal. This report would be of interest to the broad auditorium of physicists and applied mathematicians, with a background in nonperturbative QFT methods or nonlinear dynamical systems, having an interest in both methodological developments and interdisciplinary applications. (author)

Renormalization group and instantons in stochastic nonlinear dynamics, from self-organized criticality to thermonuclear reactors

Energy Technology Data Exchange (ETDEWEB)

Volchenkov, D. [Bielefeld Univ., Center of Excellence Cognitive Interaction Technology (CITEC) (Germany)

2009-03-15

Stochastic counterparts of nonlinear dynamics are studied by means of nonperturbative functional methods developed in the framework of quantum field theory (QFT). In particular, we discuss fully developed turbulence, including leading corrections on possible compressibility of fluids, transport through porous media, theory of waterspouts and tsunami waves, stochastic magnetohydrodynamics, turbulent transport in crossed fields, self-organized criticality, and dynamics of accelerated wrinkled flame fronts advancing in a wide canal. This report would be of interest to the broad auditorium of physicists and applied mathematicians, with a background in nonperturbative QFT methods or nonlinear dynamical systems, having an interest in both methodological developments and interdisciplinary applications. (author)

On the self-organized critical state of Vesuvio volcano

Science.gov (United States)

Luongo, G.; Mazzarella, A.; Palumbo, A.

1996-01-01

The catalogue of volcanic earthquakes recorded at Vesuvio (1972-1993) is shown to be complete for events with magnitude enclosed between 1.8 and 3.0. Such a result is converted in significant fractal laws (power laws) relating the distribution of earthquakes to the distribution of energy release, seismic moment, size of fractured zone and linear dimension of faults. The application of the Cantor dust model to time sequence of Vesuvio seismic and eruptive events allows the determination of significant time-clustering fractal structures. In particular, the Vesuvio eruptive activity shows a double-regime process with a stronger clustering on short-time scales than on long-time scales. The complexity of the Vesuvio system does not depend on the number of geological, geophysical and geochemical factors that govern it, but mainly on the number of their interconnections, on the intensity of such linkages and on the feed-back processes. So, all the identified fractal features are taken as evidence that the Vesuvio system is in a self-organized critical state i.e., in a marginally stable state in which a small perturbation can start a chain reaction that can lead to catastrophe. After the catatrophe, the system regulates itself and begins a new cycle, not necessarily periodic, that will end with a successive catastrophe. The variations of the fractal dimension and of the specific scale ranges, in which the fractal behaviour is found to hold, serve as possible volcanic predictors reflecting changes of the same volcanic process.

11th Workshop on Self-Organizing Maps

CERN Document Server

Mendenhall, Michael; O'Driscoll, Patrick

2016-01-01

This book contains the articles from the international conference 11th Workshop on Self-Organizing Maps 2016 (WSOM 2016), held at Rice University in Houston, Texas, 6-8 January 2016. WSOM is a biennial international conference series starting with WSOM'97 in Helsinki, Finland, under the guidance and direction of Professor Tuevo Kohonen (Emeritus Professor, Academy of Finland). WSOM brings together the state-of-the-art theory and applications in Competitive Learning Neural Networks: SOMs, LVQs and related paradigms of unsupervised and supervised vector quantization. The current proceedings present the expert body of knowledge of 93 authors from 15 countries in 31 peer reviewed contributions. It includes papers and abstracts from the WSOM 2016 invited speakers representing leading researchers in the theory and real-world applications of Self-Organizing Maps and Learning Vector Quantization: Professor Marie Cottrell (Universite Paris 1 Pantheon Sorbonne, France), Professor Pablo Estevez (University of Chile and ...

Trait self-esteem and neural activities related to self-evaluation and social feedback

Science.gov (United States)

Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui

2016-01-01

Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one’s own personality traits and of others’ opinion about one’s own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one’s own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback. PMID:26842975

Trait self-esteem and neural activities related to self-evaluation and social feedback.

Science.gov (United States)

Yang, Juan; Xu, Xiaofan; Chen, Yu; Shi, Zhenhao; Han, Shihui

2016-02-04

Self-esteem has been associated with neural responses to self-reflection and attitude toward social feedback but in different brain regions. The distinct associations might arise from different tasks or task-related attitudes in the previous studies. The current study aimed to clarify these by investigating the association between self-esteem and neural responses to evaluation of one's own personality traits and of others' opinion about one's own personality traits. We scanned 25 college students using functional MRI during evaluation of oneself or evaluation of social feedback. Trait self-esteem was measured using the Rosenberg self-esteem scale after scanning. Whole-brain regression analyses revealed that trait self-esteem was associated with the bilateral orbitofrontal activity during evaluation of one's own positive traits but with activities in the medial prefrontal cortex, posterior cingulate, and occipital cortices during evaluation of positive social feedback. Our findings suggest that trait self-esteem modulates the degree of both affective processes in the orbitofrontal cortex during self-reflection and cognitive processes in the medial prefrontal cortex during evaluation of social feedback.

Critical self-evaluation for excellence

International Nuclear Information System (INIS)

Mullee, G.R.

1988-01-01

This paper discusses the use of critical self-evaluation of performance as a stimulus for excellence. Self-evaluation inherently provides acceptance and ownership of a problem, thereby motivating change. Some of the key elements of self-evaluation are discussed such as unbiased observations and appropriate performance standards. As circumstances change, an ongoing self-appraisal approach provides continuing feedback to maintain and further excellence. Two examples of critical self-evaluation in use at the Tennessee Valley Authority (TVA) are discussed

Self Organization in Compensated Semiconductors

Science.gov (United States)

Berezin, Alexander A.

2004-03-01

In partially compensated semiconductor (PCS) Fermi level is pinned to donor sub-band. Due to positional randomness and almost isoenergetic hoppings, donor-spanned electronic subsystem in PCS forms fluid-like highly mobile collective state. This makes PCS playground for pattern formation, self-organization, complexity emergence, electronic neural networks, and perhaps even for origins of life, bioevolution and consciousness. Through effects of impact and/or Auger ionization of donor sites, whole PCS may collapse (spinodal decomposition) into microblocks potentially capable of replication and protobiological activity (DNA analogue). Electronic screening effects may act in RNA fashion by introducing additional length scale(s) to system. Spontaneous quantum computing on charged/neutral sites becomes potential generator of informationally loaded microstructures akin to "Carl Sagan Effect" (hidden messages in Pi in his "Contact") or informational self-organization of "Library of Babel" of J.L. Borges. Even general relativity effects at Planck scale (R.Penrose) may affect the dynamics through (e.g.) isotopic variations of atomic mass and local density (A.A.Berezin, 1992). Thus, PCS can serve as toy model (experimental and computational) at interface of physics and life sciences.

I think therefore I am: Rest-related prefrontal cortex neural activity is involved in generating the sense of self.

Science.gov (United States)

Gruberger, M; Levkovitz, Y; Hendler, T; Harel, E V; Harari, H; Ben Simon, E; Sharon, H; Zangen, A

2015-05-01

The sense of self has always been a major focus in the psychophysical debate. It has been argued that this complex ongoing internal sense cannot be explained by any physical measure and therefore substantiates a mind-body differentiation. Recently, however, neuro-imaging studies have associated self-referential spontaneous thought, a core-element of the ongoing sense of self, with synchronous neural activations during rest in the medial prefrontal cortex (PFC), as well as the medial and lateral parietal cortices. By applying deep transcranial magnetic stimulation (TMS) over human PFC before rest, we disrupted activity in this neural circuitry thereby inducing reports of lowered self-awareness and strong feelings of dissociation. This effect was not found with standard or sham TMS, or when stimulation was followed by a task instead of rest. These findings demonstrate for the first time a critical, causal role of intact rest-related PFC activity patterns in enabling integrated, enduring, self-referential mental processing. Copyright © 2015 Elsevier Inc. All rights reserved.

Ordination of self-organizing feature map neural networks and its application to the study of plant communities

Institute of Scientific and Technical Information of China (English)

Jintun ZHANG; Dongping MENG; Yuexiang XI

2009-01-01

A self-organizing feature map (SOFM) neural network is a powerful tool in analyzing and solving complex, non-linear problems. According to its features, a SOFM is entirely compatible with ordination studies of plant communities. In our present work, mathematical principles, and ordination techniques and procedures are introduced. A SOFM ordination was applied to the study of plant communities in the middle of the Taihang mountains. The ordination was carried out by using the NNTool box in MATLAB. The results of 68 quadrats of plant communities were distributed in SOFM space. The ordination axes showed the ecological gradients clearly and provided the relationships between communities with ecological meaning. The results are consistent with the reality of vegetation in the study area. This suggests that SOFM ordination is an effective technique in plant ecology. During ordination procedures, it is easy to carry out clustering of communities and so it is beneficial for combining classification and ordination in vegetation studies.

Self-Organizing Robots

CERN Document Server

Murata, Satoshi

2012-01-01

It is man’s ongoing hope that a machine could somehow adapt to its environment by reorganizing itself. This is what the notion of self-organizing robots is based on. The theme of this book is to examine the feasibility of creating such robots within the limitations of current mechanical engineering. The topics comprise the following aspects of such a pursuit: the philosophy of design of self-organizing mechanical systems; self-organization in biological systems; the history of self-organizing mechanical systems; a case study of a self-assembling/self-repairing system as an autonomous distributed system; a self-organizing robot that can create its own shape and robotic motion; implementation and instrumentation of self-organizing robots; and the future of self-organizing robots. All topics are illustrated with many up-to-date examples, including those from the authors’ own work. The book does not require advanced knowledge of mathematics to be understood, and will be of great benefit to students in the rob...

Hierarchical organization versus self-organization

OpenAIRE

Busseniers, Evo

2014-01-01

In this paper we try to define the difference between hierarchical organization and self-organization. Organization is defined as a structure with a function. So we can define the difference between hierarchical organization and self-organization both on the structure as on the function. In the next two chapters these two definitions are given. For the structure we will use some existing definitions in graph theory, for the function we will use existing theory on (self-)organization. In the t...

Self-reported empathy and neural activity during action imitation and observation in schizophrenia

Directory of Open Access Journals (Sweden)

William P. Horan

2014-01-01

Conclusions: Although patients with schizophrenia demonstrated largely normal patterns of neural activation across the finger movement and facial expression tasks, they reported decreased self perceived empathy and failed to show the typical relationship between neural activity and self-reported empathy seen in controls. These findings suggest that patients show a disjunction between automatic neural responses to low level social cues and higher level, integrative social cognitive processes involved in self-perceived empathy.

A neural model of figure-ground organization.

Science.gov (United States)

Craft, Edward; Schütze, Hartmut; Niebur, Ernst; von der Heydt, Rüdiger

2007-06-01

Psychophysical studies suggest that figure-ground organization is a largely autonomous process that guides--and thus precedes--allocation of attention and object recognition. The discovery of border-ownership representation in single neurons of early visual cortex has confirmed this view. Recent theoretical studies have demonstrated that border-ownership assignment can be modeled as a process of self-organization by lateral interactions within V2 cortex. However, the mechanism proposed relies on propagation of signals through horizontal fibers, which would result in increasing delays of the border-ownership signal with increasing size of the visual stimulus, in contradiction with experimental findings. It also remains unclear how the resulting border-ownership representation would interact with attention mechanisms to guide further processing. Here we present a model of border-ownership coding based on dedicated neural circuits for contour grouping that produce border-ownership assignment and also provide handles for mechanisms of selective attention. The results are consistent with neurophysiological and psychophysical findings. The model makes predictions about the hypothetical grouping circuits and the role of feedback between cortical areas.

Self-organization of high intensity laser pulses propagating in gases

International Nuclear Information System (INIS)

Koga, James

2001-01-01

In recent years the development of high intensity short pulse lasers has opened up wide fields of science which had previously been difficult to study. Recent experiments of short pulse lasers propagating in air have shown that these laser pulses can propagate over very long distances (up to 12 km) with little or no distortion of the pulse. Here we present a model of this propagation using a modified version of the self-organized criticality model developed for sandpiles by Bak, Tang, and Weisenfeld. The additions to the sandpile model include the formation of plasma which acts as a threshold diffusion term and self-focusing by the nonlinear index of refraction which acts as a continuous inverse diffusion. Results of this simple model indicate that a strongly self-focusing laser pulse shows self-organized critical behavior. (author)

Self-Organized Complexity and Coherent Infomax from the Viewpoint of Jaynes’s Probability Theory

Directory of Open Access Journals (Sweden)

William A. Phillips

2012-01-01

Full Text Available This paper discusses concepts of self-organized complexity and the theory of Coherent Infomax in the light of Jaynes’s probability theory. Coherent Infomax, shows, in principle, how adaptively self-organized complexity can be preserved and improved by using probabilistic inference that is context-sensitive. It argues that neural systems do this by combining local reliability with flexible, holistic, context-sensitivity. Jaynes argued that the logic of probabilistic inference shows it to be based upon Bayesian and Maximum Entropy methods or special cases of them. He presented his probability theory as the logic of science; here it is considered as the logic of life. It is concluded that the theory of Coherent Infomax specifies a general objective for probabilistic inference, and that contextual interactions in neural systems perform functions required of the scientist within Jaynes’s theory.

Analogies between urban hierarchies and river networks: Fractals, symmetry, and self-organized criticality

International Nuclear Information System (INIS)

Chen Yanguang

2009-01-01

A pair of nonlinear programming models is built to explain the fractal structure of systems of cities and those of rivers. The hierarchies of cities can be characterized by a set of exponential functions, which is identical in form to the Horton-Strahler's laws of the river networks. Four power laws can be derived from these exponential functions. The evolution of both systems of cities and rivers are then represented as nonlinear dual programming models: to maximize information entropy subject to a certain energy use or to minimize energy dissipation subject to certain information capacity. The optimal solutions of the programming problems are just the exponential equations associated with scaling relations. By doing so, fractals and the self-organized criticality marked by the power laws are interpreted using the idea from the entropy-maximization principle, which gives further weight to the suggestion that optimality of the system as a whole defines the dynamical origin of fractal forms in both nature and society.

CRIM1 complexes with ß-catenin and cadherins, stabilizes cell-cell junctions and is critical for neural morphogenesis.

Directory of Open Access Journals (Sweden)

Virgilio G Ponferrada

Full Text Available In multicellular organisms, morphogenesis is a highly coordinated process that requires dynamically regulated adhesion between cells. An excellent example of cellular morphogenesis is the formation of the neural tube from the flattened epithelium of the neural plate. Cysteine-rich motor neuron protein 1 (CRIM1 is a single-pass (type 1 transmembrane protein that is expressed in neural structures beginning at the neural plate stage. In the frog Xenopus laevis, loss of function studies using CRIM1 antisense morpholino oligonucleotides resulted in a failure of neural development. The CRIM1 knockdown phenotype was, in some cases, mild and resulted in perturbed neural fold morphogenesis. In severely affected embryos there was a dramatic failure of cell adhesion in the neural plate and complete absence of neural structures subsequently. Investigation of the mechanism of CRIM1 function revealed that it can form complexes with ß-catenin and cadherins, albeit indirectly, via the cytosolic domain. Consistent with this, CRIM1 knockdown resulted in diminished levels of cadherins and ß-catenin in junctional complexes in the neural plate. We conclude that CRIM1 is critical for cell-cell adhesion during neural development because it is required for the function of cadherin-dependent junctions.

MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

Directory of Open Access Journals (Sweden)

Jerzy Balicki

2017-03-01

Full Text Available In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because some software applications can be automatically constructed by genetic programming. To obtain a competitive advantage, machine learning can be used for the management of self-organizing cloud computing systems performing calculations for business. Also the use of selected economic self-organizing distributed systems has been described, including some testing methods of predicting borrower reliability. Finally, some conclusions and directions for further research have been proposed.

Neural basis of self and other representation in autism: an FMRI study of self-face recognition.

Directory of Open Access Journals (Sweden)

Lucina Q Uddin

Full Text Available Autism is a developmental disorder characterized by decreased interest and engagement in social interactions and by enhanced self-focus. While previous theoretical approaches to understanding autism have emphasized social impairments and altered interpersonal interactions, there is a recent shift towards understanding the nature of the representation of the self in individuals with autism spectrum disorders (ASD. Still, the neural mechanisms subserving self-representations in ASD are relatively unexplored.We used event-related fMRI to investigate brain responsiveness to images of the subjects' own face and to faces of others. Children with ASD and typically developing (TD children viewed randomly presented digital morphs between their own face and a gender-matched other face, and made "self/other" judgments. Both groups of children activated a right premotor/prefrontal system when identifying images containing a greater percentage of the self face. However, while TD children showed activation of this system during both self- and other-processing, children with ASD only recruited this system while viewing images containing mostly their own face.This functional dissociation between the representation of self versus others points to a potential neural substrate for the characteristic self-focus and decreased social understanding exhibited by these individuals, and suggests that individuals with ASD lack the shared neural representations for self and others that TD children and adults possess and may use to understand others.

Habituation in non-neural organisms: evidence from slime moulds.

Science.gov (United States)

Boisseau, Romain P; Vogel, David; Dussutour, Audrey

2016-04-27

Learning, defined as a change in behaviour evoked by experience, has hitherto been investigated almost exclusively in multicellular neural organisms. Evidence for learning in non-neural multicellular organisms is scant, and only a few unequivocal reports of learning have been described in single-celled organisms. Here we demonstrate habituation, an unmistakable form of learning, in the non-neural organism Physarum polycephalum In our experiment, using chemotaxis as the behavioural output and quinine or caffeine as the stimulus, we showed that P. polycephalum learnt to ignore quinine or caffeine when the stimuli were repeated, but responded again when the stimulus was withheld for a certain time. Our results meet the principle criteria that have been used to demonstrate habituation: responsiveness decline and spontaneous recovery. To distinguish habituation from sensory adaptation or motor fatigue, we also show stimulus specificity. Our results point to the diversity of organisms lacking neurons, which likely display a hitherto unrecognized capacity for learning, and suggest that slime moulds may be an ideal model system in which to investigate fundamental mechanisms underlying learning processes. Besides, documenting learning in non-neural organisms such as slime moulds is centrally important to a comprehensive, phylogenetic understanding of when and where in the tree of life the earliest manifestations of learning evolved. © 2016 The Author(s).

Self-Organizing Neural Integration of Pose-Motion Features for Human Action Recognition

Directory of Open Access Journals (Sweden)

German Ignacio Parisi

2015-06-01

Full Text Available The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented towards human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR networks that obtain progressively generalized representations of sensory inputs and learn inherent spatiotemporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best 21 results for a public benchmark of domestic daily actions.

25 Years of Self-organized Criticality: Concepts and Controversies

Science.gov (United States)

Watkins, Nicholas W.; Pruessner, Gunnar; Chapman, Sandra C.; Crosby, Norma B.; Jensen, Henrik J.

2016-01-01

Introduced by the late Per Bak and his colleagues, self-organized criticality (SOC) has been one of the most stimulating concepts to come out of statistical mechanics and condensed matter theory in the last few decades, and has played a significant role in the development of complexity science. SOC, and more generally fractals and power laws, have attracted much comment, ranging from the very positive to the polemical. The other papers (Aschwanden et al. in Space Sci. Rev., 2014, this issue; McAteer et al. in Space Sci. Rev., 2015, this issue; Sharma et al. in Space Sci. Rev. 2015, in preparation) in this special issue showcase the considerable body of observations in solar, magnetospheric and fusion plasma inspired by the SOC idea, and expose the fertile role the new paradigm has played in approaches to modeling and understanding multiscale plasma instabilities. This very broad impact, and the necessary process of adapting a scientific hypothesis to the conditions of a given physical system, has meant that SOC as studied in these fields has sometimes differed significantly from the definition originally given by its creators. In Bak's own field of theoretical physics there are significant observational and theoretical open questions, even 25 years on (Pruessner 2012). One aim of the present review is to address the dichotomy between the great reception SOC has received in some areas, and its shortcomings, as they became manifest in the controversies it triggered. Our article tries to clear up what we think are misunderstandings of SOC in fields more remote from its origins in statistical mechanics, condensed matter and dynamical systems by revisiting Bak, Tang and Wiesenfeld's original papers.

Self-reported empathy and neural activity during action imitation and observation in schizophrenia.

Science.gov (United States)

Horan, William P; Iacoboni, Marco; Cross, Katy A; Korb, Alex; Lee, Junghee; Nori, Poorang; Quintana, Javier; Wynn, Jonathan K; Green, Michael F

2014-01-01

Although social cognitive impairments are key determinants of functional outcome in schizophrenia their neural bases are poorly understood. This study investigated neural activity during imitation and observation of finger movements and facial expressions in schizophrenia, and their correlates with self-reported empathy. 23 schizophrenia outpatients and 23 healthy controls were studied with functional magnetic resonance imaging (fMRI) while they imitated, executed, or simply observed finger movements and facial emotional expressions. Between-group activation differences, as well as relationships between activation and self-reported empathy, were evaluated. Both patients and controls similarly activated neural systems previously associated with these tasks. We found no significant between-group differences in task-related activations. There were, however, between-group differences in the correlation between self-reported empathy and right inferior frontal (pars opercularis) activity during observation of facial emotional expressions. As in previous studies, controls demonstrated a positive association between brain activity and empathy scores. In contrast, the pattern in the patient group reflected a negative association between brain activity and empathy. Although patients with schizophrenia demonstrated largely normal patterns of neural activation across the finger movement and facial expression tasks, they reported decreased self perceived empathy and failed to show the typical relationship between neural activity and self-reported empathy seen in controls. These findings suggest that patients show a disjunction between automatic neural responses to low level social cues and higher level, integrative social cognitive processes involved in self-perceived empathy.

Long-time tails do not necessarily imply self-organized criticality or the breakdown of the standard transport paradigm

International Nuclear Information System (INIS)

Krommes, J.A.; Ottaviani, M.

2000-01-01

Numerical measurements and analytical studies are performed on a stochastic model with features relevant to plasma confinement. Although the model lacks crucial features of self-organized criticality (SOC) and its transport can be computed by standard techniques, it nevertheless exhibits intermittency and algebraic time correlations. This suggests that SOC need not be the explanation for observed long-time tails in experimental fluctuation data. Arguments based on the renormalized spectral balance equation, and simulation of a standard nonlinear paradigm, predict a range of Hurst exponents in reasonable agreement with the observations without invoking submarginal dynamics

Self-enhancement learning: target-creating learning and its application to self-organizing maps.

Science.gov (United States)

Kamimura, Ryotaro

2011-05-01

In this article, we propose a new learning method called "self-enhancement learning." In this method, targets for learning are not given from the outside, but they can be spontaneously created within a neural network. To realize the method, we consider a neural network with two different states, namely, an enhanced and a relaxed state. The enhanced state is one in which the network responds very selectively to input patterns, while in the relaxed state, the network responds almost equally to input patterns. The gap between the two states can be reduced by minimizing the Kullback-Leibler divergence between the two states with free energy. To demonstrate the effectiveness of this method, we applied self-enhancement learning to the self-organizing maps, or SOM, in which lateral interactions were added to an enhanced state. We applied the method to the well-known Iris, wine, housing and cancer machine learning database problems. In addition, we applied the method to real-life data, a student survey. Experimental results showed that the U-matrices obtained were similar to those produced by the conventional SOM. Class boundaries were made clearer in the housing and cancer data. For all the data, except for the cancer data, better performance could be obtained in terms of quantitative and topological errors. In addition, we could see that the trustworthiness and continuity, referring to the quality of neighborhood preservation, could be improved by the self-enhancement learning. Finally, we used modern dimensionality reduction methods and compared their results with those obtained by the self-enhancement learning. The results obtained by the self-enhancement were not superior to but comparable with those obtained by the modern dimensionality reduction methods.

Automatic lithofacies segmentation from well-logs data. A comparative study between the Self-Organizing Map (SOM) and Walsh transform

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Aliouane, Leila; Ouadfeul, Sid-Ali; Rabhi, Abdessalem; Rouina, Fouzi; Benaissa, Zahia; Boudella, Amar

2013-04-01

The main goal of this work is to realize a comparison between two lithofacies segmentation techniques of reservoir interval. The first one is based on the Kohonen's Self-Organizing Map neural network machine. The second technique is based on the Walsh transform decomposition. Application to real well-logs data of two boreholes located in the Algerian Sahara shows that the Self-organizing map is able to provide more lithological details that the obtained lithofacies model given by the Walsh decomposition. Keywords: Comparison, Lithofacies, SOM, Walsh References: 1)Aliouane, L., Ouadfeul, S., Boudella, A., 2011, Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network, Arabian Journal of geosciences, doi: 10.1007/s12517-011-0459-4 2) Aliouane, L., Ouadfeul, S., Djarfour, N., Boudella, A., 2012, Petrophysical Parameters Estimation from Well-Logs Data Using Multilayer Perceptron and Radial Basis Function Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 730-736, doi : 10.1007/978-3-642-34500-5_86 3)Ouadfeul, S. and Aliouane., L., 2011, Multifractal analysis revisited by the continuous wavelet transform applied in lithofacies segmentation from well-logs data, International journal of applied physics and mathematics, Vol01 N01. 4) Ouadfeul, S., Aliouane, L., 2012, Lithofacies Classification Using the Multilayer Perceptron and the Self-organizing Neural Networks, Lecture Notes in Computer Science Volume 7667, 2012, pp 737-744, doi : 10.1007/978-3-642-34500-5_87 5) Weisstein, Eric W. "Fast Walsh Transform." From MathWorld--A Wolfram Web Resource. http://mathworld.wolfram.com/FastWalshTransform.html

Self-organization at the frictional interface for green tribology.

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Nosonovsky, Michael

2010-10-28

Despite the fact that self-organization during friction has received relatively little attention from tribologists so far, it has the potential for the creation of self-healing and self-lubricating materials, which are important for green or environment-friendly tribology. The principles of the thermodynamics of irreversible processes and of the nonlinear theory of dynamical systems are used to investigate the formation of spatial and temporal structures during friction. The transition to the self-organized state with low friction and wear occurs through destabilization of steady-state (stationary) sliding. The criterion for destabilization is formulated and several examples are discussed: the formation of a protective film, microtopography evolution and slip waves. The pattern formation may involve self-organized criticality and reaction-diffusion systems. A special self-healing mechanism may be embedded into the material by coupling the corresponding required forces. The analysis provides the structure-property relationship, which can be applied for the design optimization of composite self-lubricating and self-healing materials for various ecologically friendly applications and green tribology.

Dynamic cultural influences on neural representations of the self.

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Chiao, Joan Y; Harada, Tokiko; Komeda, Hidetsugu; Li, Zhang; Mano, Yoko; Saito, Daisuke; Parrish, Todd B; Sadato, Norihiro; Iidaka, Tetsuya

2010-01-01

People living in multicultural environments often encounter situations which require them to acquire different cultural schemas and to switch between these cultural schemas depending on their immediate sociocultural context. Prior behavioral studies show that priming cultural schemas reliably impacts mental processes and behavior underlying self-concept. However, less well understood is whether or not cultural priming affects neurobiological mechanisms underlying the self. Here we examined whether priming cultural values of individualism and collectivism in bicultural individuals affects neural activity in cortical midline structures underlying self-relevant processes using functional magnetic resonance imaging. Biculturals primed with individualistic values showed increased activation within medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) during general relative to contextual self-judgments, whereas biculturals primed with collectivistic values showed increased response within MPFC and PCC during contextual relative to general self-judgments. Moreover, degree of cultural priming was positively correlated with degree of MPFC and PCC activity during culturally congruent self-judgments. These findings illustrate the dynamic influence of culture on neural representations underlying the self and, more broadly, suggest a neurobiological basis by which people acculturate to novel environments.

Application of the Theory of Self-Organized Criticality to the Investigation of Historical Processes

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Dmitry S. Zhukov

2016-12-01

Full Text Available The article demonstrates heuristic possibilities of the theory of self-organized criticality (SOC in the investigation of historical processes. Key SOC concepts and ideas are explained. Specifically, tools that can be used for identifying pink noise, an attribute of a critical state, are described. The results of spectral analyses of historical demographic data (i.e., birth and death rates in Russian settlements in the 19th and 20th centuries and historical market data (i.e., grain prices in regions of Russia in the 18th, 19th, and early 20th centuries are presented. It was found that noise color in the data series differed substantially across different periods. Based on these observations, the assumption that a change in noise color can serve as an indicator of changes in historical processes was made. In some cases, this indicator can enable one to establish the time, speed, and direction of state changes in historical processes. Pink noise was discovered in the examined birth and death rate dynamics, as well as in the dynamics of prices across periods. The described methods have the potential to be used beyond the limits of the presently considered historical subjects, including in investigations of different types of social transformation.

Neural Network Based Intrusion Detection System for Critical Infrastructures

Energy Technology Data Exchange (ETDEWEB)

Todd Vollmer; Ondrej Linda; Milos Manic

2009-07-01

Resiliency and security in control systems such as SCADA and Nuclear plant’s in today’s world of hackers and malware are a relevant concern. Computer systems used within critical infrastructures to control physical functions are not immune to the threat of cyber attacks and may be potentially vulnerable. Tailoring an intrusion detection system to the specifics of critical infrastructures can significantly improve the security of such systems. The IDS-NNM – Intrusion Detection System using Neural Network based Modeling, is presented in this paper. The main contributions of this work are: 1) the use and analyses of real network data (data recorded from an existing critical infrastructure); 2) the development of a specific window based feature extraction technique; 3) the construction of training dataset using randomly generated intrusion vectors; 4) the use of a combination of two neural network learning algorithms – the Error-Back Propagation and Levenberg-Marquardt, for normal behavior modeling. The presented algorithm was evaluated on previously unseen network data. The IDS-NNM algorithm proved to be capable of capturing all intrusion attempts presented in the network communication while not generating any false alerts.

Principles of neural information processing

CERN Document Server

Seelen, Werner v

2016-01-01

In this fundamental book the authors devise a framework that describes the working of the brain as a whole. It presents a comprehensive introduction to the principles of Neural Information Processing as well as recent and authoritative research. The books´ guiding principles are the main purpose of neural activity, namely, to organize behavior to ensure survival, as well as the understanding of the evolutionary genesis of the brain. Among the developed principles and strategies belong self-organization of neural systems, flexibility, the active interpretation of the world by means of construction and prediction as well as their embedding into the world, all of which form the framework of the presented description. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, the authors have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework, but also syst...

Control of self-organizing nonlinear systems

CERN Document Server

Klapp, Sabine; Hövel, Philipp

2016-01-01

The book summarizes the state-of-the-art of research on control of self-organizing nonlinear systems with contributions from leading international experts in the field. The first focus concerns recent methodological developments including control of networks and of noisy and time-delayed systems. As a second focus, the book features emerging concepts of application including control of quantum systems, soft condensed matter, and biological systems. Special topics reflecting the active research in the field are the analysis and control of chimera states in classical networks and in quantum systems, the mathematical treatment of multiscale systems, the control of colloidal and quantum transport, the control of epidemics and of neural network dynamics.

Hierarchical Self Organizing Map for Novelty Detection using Mobile Robot with Robust Sensor

International Nuclear Information System (INIS)

Sha'abani, M N A H; Miskon, M F; Sakidin, H

2013-01-01

This paper presents a novelty detection method based on Self Organizing Map neural network using a mobile robot. Based on hierarchical neural network, the network is divided into three networks; position, orientation and sensor measurement network. A simulation was done to demonstrate and validate the proposed method using MobileSim. Three cases of abnormal events; new, missing and shifted objects are employed for performance evaluation. The result of detection was then filtered for false positive detection. The result shows that the inspection produced less than 2% false positive detection at high sensitivity settings

Neural processing of race during imitation: self-similarity versus social status

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Reynolds Losin, Elizabeth A.; Cross, Katy A.; Iacoboni, Marco; Dapretto, Mirella

2017-01-01

People preferentially imitate others who are similar to them or have high social status. Such imitative biases are thought to have evolved because they increase the efficiency of cultural acquisition. Here we focused on distinguishing between self-similarity and social status as two candidate mechanisms underlying neural responses to a person’s race during imitation. We used fMRI to measure neural responses when 20 African American (AA) and 20 European American (EA) young adults imitated AA, EA and Chinese American (CA) models and also passively observed their gestures and faces. We found that both AA and EA participants exhibited more activity in lateral fronto-parietal and visual regions when imitating AAs compared to EAs or CAs. These results suggest that racial self-similarity is not likely to modulate neural responses to race during imitation, in contrast with findings from previous neuroimaging studies of face perception and action observation. Furthermore, AA and EA participants associated AAs with lower social status than EAs or CAs, suggesting that the social status associated with different racial groups may instead modulate neural activity during imitation of individuals from those groups. Taken together, these findings suggest that neural responses to race during imitation are driven by socially-learned associations rather than self-similarity. This may reflect the adaptive role of imitation in social learning, where learning from higher-status models can be more beneficial. This study provides neural evidence consistent with evolutionary theories of cultural acquisition. PMID:23813738

What Can Psychiatric Disorders Tell Us about Neural Processing of the Self?

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Zhao, Weihua; Luo, Lizhu; Li, Qin; Kendrick, Keith M

2013-01-01

Many psychiatric disorders are associated with abnormal self-processing. While these disorders also have a wide-range of complex, and often heterogeneous sets of symptoms involving different cognitive, emotional, and motor domains, an impaired sense of self can contribute to many of these. Research investigating self-processing in healthy subjects has facilitated identification of changes in specific neural circuits which may cause altered self-processing in psychiatric disorders. While there is evidence for altered self-processing in many psychiatric disorders, here we will focus on four of the most studied ones, schizophrenia, autism spectrum disorder (ASD), major depression, and borderline personality disorder (BPD). We review evidence for dysfunction in two different neural systems implicated in self-processing, namely the cortical midline system (CMS) and the mirror neuron system (MNS), as well as contributions from altered inter-hemispheric connectivity (IHC). We conclude that while abnormalities in frontal-parietal activity and/or connectivity in the CMS are common to all four disorders there is more disruption of integration between frontal and parietal regions resulting in a shift toward parietal control in schizophrenia and ASD which may contribute to the greater severity and delusional aspects of their symptoms. Abnormalities in the MNS and in IHC are also particularly evident in schizophrenia and ASD and may lead to disturbances in sense of agency and the physical self in these two disorders. A better future understanding of how changes in the neural systems sub-serving self-processing contribute to different aspects of symptom abnormality in psychiatric disorders will require that more studies carry out detailed individual assessments of altered self-processing in conjunction with measurements of neural functioning.

Automated implementation of rule-based expert systems with neural networks for time-critical applications

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Ramamoorthy, P. A.; Huang, Song; Govind, Girish

1991-01-01

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Self-organizing plasmas

International Nuclear Information System (INIS)

Hayashi, T.; Sato, T.

1999-01-01

The primary purpose of this paper is to extract a grand view of self-organization through an extensive computer simulation of plasmas. The assertion is made that self-organization is governed by three key processes, i.e. the existence of an open complex system, the existence of information (energy) sources and the existence of entropy generation and expulsion processes. We find that self-organization takes place in an intermittent fashion when energy is supplied continuously from outside. In contrast, when the system state is suddenly changed into a non-equilibrium state externally, the system evolves stepwise and reaches a minimum energy state. We also find that the entropy production rate is maximized whenever a new ordered structure is created and that if the entropy generated during the self-organizing process is expelled from the system, then the self-organized structure becomes more prominent and clear. (author)

Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview

Science.gov (United States)

Rao, B. K. N.; Srinivasa Pai, P.; Nagabhushana, T. N.

2012-05-01

Rolling - Element Bearings are extensively used in almost all global industries. Any critical failures in these vitally important components would not only affect the overall systems performance but also its reliability, safety, availability and cost-effectiveness. Proactive strategies do exist to minimise impending failures in real time and at a minimum cost. Continuous innovative developments are taking place in the field of Artificial Neural Networks (ANNs) technology. Significant research and development are taking place in many universities, private and public organizations and a wealth of published literature is available highlighting the potential benefits of employing ANNs in intelligently monitoring, diagnosing, prognosing and managing rolling-element bearing failures. This paper attempts to critically review the recent trends in this topical area of interest.

Failure Diagnosis and Prognosis of Rolling - Element Bearings using Artificial Neural Networks: A Critical Overview

International Nuclear Information System (INIS)

Rao, B K N; Pai, P Srinivasa; Nagabhushana, T N

2012-01-01

Rolling - Element Bearings are extensively used in almost all global industries. Any critical failures in these vitally important components would not only affect the overall systems performance but also its reliability, safety, availability and cost-effectiveness. Proactive strategies do exist to minimise impending failures in real time and at a minimum cost. Continuous innovative developments are taking place in the field of Artificial Neural Networks (ANNs) technology. Significant research and development are taking place in many universities, private and public organizations and a wealth of published literature is available highlighting the potential benefits of employing ANNs in intelligently monitoring, diagnosing, prognosing and managing rolling-element bearing failures. This paper attempts to critically review the recent trends in this topical area of interest.

Critical Care Organizations: Business of Critical Care and Value/Performance Building.

Science.gov (United States)

Leung, Sharon; Gregg, Sara R; Coopersmith, Craig M; Layon, A Joseph; Oropello, John; Brown, Daniel R; Pastores, Stephen M; Kvetan, Vladimir

2018-01-01

New, value-based regulations and reimbursement structures are creating historic care management challenges, thinning the margins and threatening the viability of hospitals and health systems. The Society of Critical Care Medicine convened a taskforce of Academic Leaders in Critical Care Medicine on February 22, 2016, during the 45th Critical Care Congress to develop a toolkit drawing on the experience of successful leaders of critical care organizations in North America for advancing critical care organizations (Appendix 1). The goal of this article was to provide a roadmap and call attention to key factors that adult critical care medicine leadership in both academic and nonacademic setting should consider when planning for value-based care. Relevant medical literature was accessed through a literature search. Material published by federal health agencies and other specialty organizations was also reviewed. Collaboratively and iteratively, taskforce members corresponded by electronic mail and held monthly conference calls to finalize this report. The business and value/performance critical care organization building section comprised of leaders of critical care organizations with expertise in critical care administration, healthcare management, and clinical practice. Two phases of critical care organizations care integration are described: "horizontal," within the system and regionalization of care as an initial phase, and "vertical," with a post-ICU and postacute care continuum as a succeeding phase. The tools required for the clinical and financial transformation are provided, including the essential prerequisites of forming a critical care organization; the manner in which a critical care organization can help manage transformational domains is considered. Lastly, how to achieve organizational health system support for critical care organization implementation is discussed. A critical care organization that incorporates functional clinical horizontal and

Viewing brain processes as Critical State Transitions across levels of organization: Neural events in Cognition and Consciousness, and general principles.

Science.gov (United States)

Werner, Gerhard

2009-04-01

In this theoretical and speculative essay, I propose that insights into certain aspects of neural system functions can be gained from viewing brain function in terms of the branch of Statistical Mechanics currently referred to as "Modern Critical Theory" [Stanley, H.E., 1987. Introduction to Phase Transitions and Critical Phenomena. Oxford University Press; Marro, J., Dickman, R., 1999. Nonequilibrium Phase Transitions in Lattice Models. Cambridge University Press, Cambridge, UK]. The application of this framework is here explored in two stages: in the first place, its principles are applied to state transitions in global brain dynamics, with benchmarks of Cognitive Neuroscience providing the relevant empirical reference points. The second stage generalizes to suggest in more detail how the same principles could also apply to the relation between other levels of the structural-functional hierarchy of the nervous system and between neural assemblies. In this view, state transitions resulting from the processing at one level are the input to the next, in the image of a 'bucket brigade', with the content of each bucket being passed on along the chain, after having undergone a state transition. The unique features of a process of this kind will be discussed and illustrated.

Identification of lithofacies using Kohonen self-organizing maps

Science.gov (United States)

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.

2002-01-01

Lithofacies identification is a primary task in reservoir characterization. Traditional techniques of lithofacies identification from core data are costly, and it is difficult to extrapolate to non-cored wells. We present a low-cost automated technique using Kohonen self-organizing maps (SOMs) to identify systematically and objectively lithofacies from well log data. SOMs are unsupervised artificial neural networks that map the input space into clusters in a topological form whose organization is related to trends in the input data. A case study used five wells located in Appleton Field, Escambia County, Alabama (Smackover Formation, limestone and dolomite, Oxfordian, Jurassic). A five-input, one-dimensional output approach is employed, assuming the lithofacies are in ascending/descending order with respect to paleoenvironmental energy levels. To consider the possible appearance of new logfacies not seen in training mode, which may potentially appear in test wells, the maximum number of outputs is set to 20 instead of four, the designated number of lithosfacies in the study area. This study found eleven major clusters. The clusters were compared to depositional lithofacies identified by manual core examination. The clusters were ordered by the SOM in a pattern consistent with environmental gradients inferred from core examination: bind/boundstone, grainstone, packstone, and wackestone. This new approach predicted lithofacies identity from well log data with 78.8% accuracy which is more accurate than using a backpropagation neural network (57.3%). The clusters produced by the SOM are ordered with respect to paleoenvironmental energy levels. This energy-related clustering provides geologists and petroleum engineers with valuable geologic information about the logfacies and their interrelationships. This advantage is not obtained in backpropagation neural networks and adaptive resonance theory neural networks. ?? 2002 Elsevier Science Ltd. All rights reserved.

Information content of neural networks with self-control and variable activity

International Nuclear Information System (INIS)

Bolle, D.; Amari, S.I.; Dominguez Carreta, D.R.C.; Massolo, G.

2001-01-01

A self-control mechanism for the dynamics of neural networks with variable activity is discussed using a recursive scheme for the time evolution of the local field. It is based upon the introduction of a self-adapting time-dependent threshold as a function of both the neural and pattern activity in the network. This mechanism leads to an improvement of the information content of the network as well as an increase of the storage capacity and the basins of attraction. Different architectures are considered and the results are compared with numerical simulations

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

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Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

The neural correlates of visual self-recognition.

Science.gov (United States)

Devue, Christel; Brédart, Serge

2011-03-01

This paper presents a review of studies that were aimed at determining which brain regions are recruited during visual self-recognition, with a particular focus on self-face recognition. A complex bilateral network, involving frontal, parietal and occipital areas, appears to be associated with self-face recognition, with a particularly high implication of the right hemisphere. Results indicate that it remains difficult to determine which specific cognitive operation is reflected by each recruited brain area, in part due to the variability of used control stimuli and experimental tasks. A synthesis of the interpretations provided by previous studies is presented. The relevance of using self-recognition as an indicator of self-awareness is discussed. We argue that a major aim of future research in the field should be to identify more clearly the cognitive operations induced by the perception of the self-face, and search for dissociations between neural correlates and cognitive components. Copyright © 2010 Elsevier Inc. All rights reserved.

Self-Organization of Genome Expression from Embryo to Terminal Cell Fate: Single-Cell Statistical Mechanics of Biological Regulation

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Alessandro Giuliani

2017-12-01

Full Text Available A statistical mechanical mean-field approach to the temporal development of biological regulation provides a phenomenological, but basic description of the dynamical behavior of genome expression in terms of autonomous self-organization with a critical transition (Self-Organized Criticality: SOC. This approach reveals the basis of self-regulation/organization of genome expression, where the extreme complexity of living matter precludes any strict mechanistic approach. The self-organization in SOC involves two critical behaviors: scaling-divergent behavior (genome avalanche and sandpile-type critical behavior. Genome avalanche patterns—competition between order (scaling and disorder (divergence reflect the opposite sequence of events characterizing the self-organization process in embryo development and helper T17 terminal cell differentiation, respectively. On the other hand, the temporal development of sandpile-type criticality (the degree of SOC control in mouse embryo suggests the existence of an SOC control landscape with a critical transition state (i.e., the erasure of zygote-state criticality. This indicates that a phase transition of the mouse genome before and after reprogramming (immediately after the late 2-cell state occurs through a dynamical change in a control parameter. This result provides a quantitative open-thermodynamic appreciation of the still largely qualitative notion of the epigenetic landscape. Our results suggest: (i the existence of coherent waves of condensation/de-condensation in chromatin, which are transmitted across regions of different gene-expression levels along the genome; and (ii essentially the same critical dynamics we observed for cell-differentiation processes exist in overall RNA expression during embryo development, which is particularly relevant because it gives further proof of SOC control of overall expression as a universal feature.

Rethinking the Modernist Curriculum with Habermas's Concept of Self-Critical Appropriation

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Sarid, Ariel

2017-01-01

The primary objective of this paper is to discuss the implications of applying Habermas's concept of self-critical appropriation for rethinking the structure of the modernist curriculum, specifically the organization of school subjects and instruction time devoted to each of them. To this end, the paper examines Habermas's differentiation between…

Critical Neural Substrates for Correcting Unexpected Trajectory Errors and Learning from Them

Science.gov (United States)

Mutha, Pratik K.; Sainburg, Robert L.; Haaland, Kathleen Y.

2011-01-01

Our proficiency at any skill is critically dependent on the ability to monitor our performance, correct errors and adapt subsequent movements so that errors are avoided in the future. In this study, we aimed to dissociate the neural substrates critical for correcting unexpected trajectory errors and learning to adapt future movements based on…

Comparison of brass alloys composition by laser-induced breakdown spectroscopy and self-organizing maps

Energy Technology Data Exchange (ETDEWEB)

Pagnotta, Stefano; Grifoni, Emanuela; Legnaioli, Stefano [Applied and Laser Spectroscopy Laboratory, ICCOM-CNR, Research Area of Pisa, Via G. Moruzzi 1, 56124 Pisa (Italy); Lezzerini, Marco [Department of Earth Sciences, University of Pisa, Via S. Maria 53, 56126 Pisa (Italy); Lorenzetti, Giulia [Applied and Laser Spectroscopy Laboratory, ICCOM-CNR, Research Area of Pisa, Via G. Moruzzi 1, 56124 Pisa (Italy); Palleschi, Vincenzo, E-mail: vincenzo.palleschi@cnr.it [Applied and Laser Spectroscopy Laboratory, ICCOM-CNR, Research Area of Pisa, Via G. Moruzzi 1, 56124 Pisa (Italy); Department of Civilizations and Forms of Knowledge, University of Pisa, Via L. Galvani 1, 56126 Pisa (Italy)

2015-01-01

In this paper we face the problem of assessing similarities in the composition of different metallic alloys, using the laser-induced breakdown spectroscopy technique. The possibility of determining the degree of similarity through the use of artificial neural networks and self-organizing maps is discussed. As an example, we present a case study involving the comparison of two historical brass samples, very similar in their composition. The results of the paper can be extended to many other situations, not necessarily associated with cultural heritage and archeological studies, where objects with similar composition have to be compared. - Highlights: • A method for assessing the similarity of materials analyzed by LIBS is proposed. • Two very similar fragments of historical brass were analyzed. • Using a simple artificial neural network the composition of the two alloys was determined. • The composition of the two brass alloys was the same within the experimental error. • Using self-organizing maps, the probability of the alloys to have the same composition was assessed.

Comparison of brass alloys composition by laser-induced breakdown spectroscopy and self-organizing maps

International Nuclear Information System (INIS)

Pagnotta, Stefano; Grifoni, Emanuela; Legnaioli, Stefano; Lezzerini, Marco; Lorenzetti, Giulia; Palleschi, Vincenzo

2015-01-01

In this paper we face the problem of assessing similarities in the composition of different metallic alloys, using the laser-induced breakdown spectroscopy technique. The possibility of determining the degree of similarity through the use of artificial neural networks and self-organizing maps is discussed. As an example, we present a case study involving the comparison of two historical brass samples, very similar in their composition. The results of the paper can be extended to many other situations, not necessarily associated with cultural heritage and archeological studies, where objects with similar composition have to be compared. - Highlights: • A method for assessing the similarity of materials analyzed by LIBS is proposed. • Two very similar fragments of historical brass were analyzed. • Using a simple artificial neural network the composition of the two alloys was determined. • The composition of the two brass alloys was the same within the experimental error. • Using self-organizing maps, the probability of the alloys to have the same composition was assessed

GDNF/GFRα1 Complex Abrogates Self-Renewing Activity of Cortical Neural Precursors Inducing Their Differentiation

Directory of Open Access Journals (Sweden)

Antonela Bonafina

2018-03-01

Full Text Available Summary: The balance between factors leading to proliferation and differentiation of cortical neural precursors (CNPs determines the correct cortical development. In this work, we show that GDNF and its receptor GFRα1 are expressed in the neocortex during the period of cortical neurogenesis. We show that the GDNF/GFRα1 complex inhibits the self-renewal capacity of mouse CNP cells induced by fibroblast growth factor 2 (FGF2, promoting neuronal differentiation. While GDNF leads to decreased proliferation of cultured cortical precursor cells, ablation of GFRα1 in glutamatergic cortical precursors enhances its proliferation. We show that GDNF treatment of CNPs promoted morphological differentiation even in the presence of the self-renewal-promoting factor, FGF2. Analysis of GFRα1-deficient mice shows an increase in the number of cycling cells during cortical development and a reduction in dendrite development of cortical GFRα1-expressing neurons. Together, these results indicate that GDNF/GFRα1 signaling plays an essential role in regulating the proliferative condition and the differentiation of cortical progenitors. : In this article, Ledda and colleagues show that GDNF acting through its receptor GFRα1 plays a critical role in the maturation of cortical progenitors by counteracting FGF2 self-renewal activity on neural stem cells and promoting neuronal differentiation. Keywords: GDNF, GFRα1, cortical precursors, proliferation, postmitotic neurons, neuronal differentiation

The dynamics of marginality and self-organized criticality as a paradigm for turbulent transport

International Nuclear Information System (INIS)

Newman, D.E.; Carreras, B.A.; Diamond, P.H.; Hahm, T.S.

1995-01-01

A general paradigm, based on the concept of self-organized criticality (SOC), for turbulent transport in magnetically confined plasmas has been recently suggested as an explanation for some of the apparent discrepancies between most theoretical models of turbulent transport and experimental observations of the transport in magnetically confined plasmas. This model describes the dynamics of the transport without relying on the underlying local fluctuation mechanisms. Computations based on a cellular automata realization of such a model have found that noise driven SOC systems can maintain average profiles that are linearly stable (submarginal) and yet are able to sustain active transport dynamics. It is also found that the dominant scales in the transport dynamics in the absence of sheared flow are system scales rather than the underlying local fluctuation scales. The addition of sheared flow into the dynamics leads to a large reduction of the system-scale transport events and a commensurate increase in the fluctuation-scale transport events needed to maintain the constant flux. The dynamics of these models and the potential ramifications for transport studies are discussed

Study of neural cells on organic semiconductor ultra thin films

Energy Technology Data Exchange (ETDEWEB)

Bystrenova, Eva; Tonazzini, Ilaria; Stoliar, Pablo; Greco, Pierpaolo; Lazar, Adina; Dutta, Soumya; Dionigi, Chiara; Cacace, Marcello; Biscarini, Fabio [ISMN-CNR, Bologna (Italy); Jelitai, Marta; Madarasz, Emilia [IEM- HAS, Budapest (Hungary); Huth, Martin; Nickel, Bert [LMU, Munich (Germany); Martini, Claudia [Dept. PNPB, Univ. of Pisa (Italy)

2008-07-01

Many technological advances are currently being developed for nano-fabrication, offering the ability to create and control patterns of soft materials. We report the deposition of cells on organic semiconductor ultra-thin films. This is a first step towards the development of active bio/non bio systems for electrical transduction. Thin films of pentacene, whose thickness was systematically varied, were grown by high vacuum sublimation. We report adhesion, growth, and differentiation of human astroglial cells and mouse neural stem cells on an organic semiconductor. Viability of astroglial cells in time was measured as a function of the roughness and the characteristic morphology of ultra thin organic film, as well as the features of the patterned molecules. Optical fluorescence microscope coupled to atomic force microscope was used to monitor the presence, density and shape of deposited cells. Neural stem cells remain viable, differentiate by retinoic acid and form dense neuronal networks. We have shown the possibility to integrate living neural cells on organic semiconductor thin films.

Probabilistic models for neural populations that naturally capture global coupling and criticality.

Science.gov (United States)

Humplik, Jan; Tkačik, Gašper

2017-09-01

Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system's state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality.

Neural activity associated with self-reflection.

Science.gov (United States)

Herwig, Uwe; Kaffenberger, Tina; Schell, Caroline; Jäncke, Lutz; Brühl, Annette B

2012-05-24

Self-referential cognitions are important for self-monitoring and self-regulation. Previous studies have addressed the neural correlates of self-referential processes in response to or related to external stimuli. We here investigated brain activity associated with a short, exclusively mental process of self-reflection in the absence of external stimuli or behavioural requirements. Healthy subjects reflected either on themselves, a personally known or an unknown person during functional magnetic resonance imaging (fMRI). The reflection period was initialized by a cue and followed by photographs of the respective persons (perception of pictures of oneself or the other person). Self-reflection, compared with reflecting on the other persons and to a major part also compared with perceiving photographs of one-self, was associated with more prominent dorsomedial and lateral prefrontal, insular, anterior and posterior cingulate activations. Whereas some of these areas showed activity in the "other"-conditions as well, self-selective characteristics were revealed in right dorsolateral prefrontal and posterior cingulate cortex for self-reflection; in anterior cingulate cortex for self-perception and in the left inferior parietal lobe for self-reflection and -perception. Altogether, cingulate, medial and lateral prefrontal, insular and inferior parietal regions show relevance for self-related cognitions, with in part self-specificity in terms of comparison with the known-, unknown- and perception-conditions. Notably, the results are obtained here without behavioural response supporting the reliability of this methodological approach of applying a solely mental intervention. We suggest considering the reported structures when investigating psychopathologically affected self-related processing.

Critical features of coupling parameter in synchronization of small world neural networks

International Nuclear Information System (INIS)

Li Yanlong; Ma Jun; Xu Wenke; Li Hongbo; Wu Min

2008-01-01

The critical features of a coupling parameter in the synchronization of small world neural networks are investigated. A power law decay form is observed in this spatially extended system, the larger linked degree becomes, the larger critical coupling intensity. There exists maximal and minimal critical coupling intensity for synchronization in the extended system. An approximate synchronization diagram has been constructed. In the case of partial coupling, a primary result is presented about the critical coupling fraction for various linked degree of networks

75 FR 63878 - Self-Regulatory Organizations; Self-Regulatory Organizations; Notice of Filing and Immediate...

Science.gov (United States)

2010-10-18

...-Regulatory Organizations; Self-Regulatory Organizations; Notice of Filing and Immediate Effectiveness of...(b)(1). \\2\\ 17 CFR 240.19b-4. I. Self-Regulatory Organization's Statement of the Terms of Substance... Public Reference Room. II. Self-Regulatory Organization's Statement of the Purpose of, and Statutory...

MACHINE LEARNING FOR THE SELF-ORGANIZATION OF DISTRIBUTED SYSTEMS IN ECONOMIC APPLICATIONS

OpenAIRE

Jerzy Balicki; Waldemar Korłub

2017-01-01

In this paper, an application of machine learning to the problem of self-organization of distributed systems has been discussed with regard to economic applications, with particular emphasis on supervised neural network learning to predict stock investments and some ratings of companies. In addition, genetic programming can play an important role in the preparation and testing of several financial information systems. For this reason, machine learning applications have been discussed because ...

Neural correlates of the processing of self-referent emotional information in bulimia nervosa.

Science.gov (United States)

Pringle, A; Ashworth, F; Harmer, C J; Norbury, R; Cooper, M J

2011-10-01

There is increasing interest in understanding the roles of distorted beliefs about the self, ostensibly unrelated to eating, weight and shape, in eating disorders (EDs), but little is known about their neural correlates. We therefore used functional magnetic resonance imaging to investigate the neural correlates of self-referent emotional processing in EDs. During the scan, unmedicated patients with bulimia nervosa (n=11) and healthy controls (n=16) responded to personality words previously found to be related to negative self beliefs in EDs and depression. Rating of the negative personality descriptors resulted in reduced activation in patients compared to controls in parietal, occipital and limbic areas including the amygdala. There was no evidence that reduced activity in patients was secondary to increased cognitive control. Different patterns of neural activation between patients and controls may be the result of either habituation to personally relevant negative self beliefs or of emotional blunting in patients. Copyright © 2011 Elsevier Ltd. All rights reserved.

Delayed development of neural language organization in very preterm born children.

Science.gov (United States)

Mürner-Lavanchy, Ines; Steinlin, Maja; Kiefer, Claus; Weisstanner, Christian; Ritter, Barbara Catherine; Perrig, Walter; Everts, Regula

2014-01-01

This study investigates neural language organization in very preterm born children compared to control children and examines the relationship between language organization, age, and language performance. Fifty-six preterms and 38 controls (7-12 y) completed a functional magnetic resonance imaging language task. Lateralization and signal change were computed for language-relevant brain regions. Younger preterms showed a bilateral language network whereas older preterms revealed left-sided language organization. No age-related differences in language organization were observed in controls. Results indicate that preterms maintain atypical bilateral language organization longer than term born controls. This might reflect a delay of neural language organization due to very premature birth.

New Angle on the Parton Distribution Functions: Self-Organizing Maps

International Nuclear Information System (INIS)

Honkanen, H.; Liuti, S.

2009-01-01

Neural network (NN) algorithms have been recently applied to construct Parton Distribution Function (PDF) parametrizations, providing an alternative to standard global fitting procedures. Here we explore a novel technique using Self-Organizing Maps (SOMs). SOMs are a class of clustering algorithms based on competitive learning among spatially-ordered neurons. We train our SOMs with stochastically generated PDF samples. On every optimization iteration the PDFs are clustered on the SOM according to a user-defined feature and the most promising candidates are used as a seed for the subsequent iteration using the topology of the map to guide the PDF generating process. Our goal is a fitting procedure that, at variance with the standard neural network approaches, will allow for an increased control of the systematic bias by enabling user interaction in the various stages of the process.

Discriminative training of self-structuring hidden control neural models

DEFF Research Database (Denmark)

Sørensen, Helge Bjarup Dissing; Hartmann, Uwe; Hunnerup, Preben

1995-01-01

This paper presents a new training algorithm for self-structuring hidden control neural (SHC) models. The SHC models were trained non-discriminatively for speech recognition applications. Better recognition performance can generally be achieved, if discriminative training is applied instead. Thus...... we developed a discriminative training algorithm for SHC models, where each SHC model for a specific speech pattern is trained with utterances of the pattern to be recognized and with other utterances. The discriminative training of SHC neural models has been tested on the TIDIGITS database...

Macromolecular target prediction by self-organizing feature maps.

Science.gov (United States)

Schneider, Gisbert; Schneider, Petra

2017-03-01

Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

Principle of Minimum Energy in Magnetic Reconnection in a Self-organized Critical Model for Solar Flares

Science.gov (United States)

Farhang, Nastaran; Safari, Hossein; Wheatland, Michael S.

2018-05-01

Solar flares are an abrupt release of magnetic energy in the Sun’s atmosphere due to reconnection of the coronal magnetic field. This occurs in response to turbulent flows at the photosphere that twist the coronal field. Similar to earthquakes, solar flares represent the behavior of a complex system, and expectedly their energy distribution follows a power law. We present a statistical model based on the principle of minimum energy in a coronal loop undergoing magnetic reconnection, which is described as an avalanche process. We show that the distribution of peaks for the flaring events in this self-organized critical system is scale-free. The obtained power-law index of 1.84 ± 0.02 for the peaks is in good agreement with satellite observations of soft X-ray flares. The principle of minimum energy can be applied for general avalanche models to describe many other phenomena.

Development of a Real-Time Thermal Performance Diagnostic Monitoring system Using Self-Organizing Neural Network for Kori-2 Nuclear Power Unit

International Nuclear Information System (INIS)

Kang, Hyun Gook; Seong, Poong Hyun

1996-01-01

In this work, a PC-based thermal performance monitoring system is developed for the nuclear power plants. the system performs real-time thermal performance monitoring and diagnosis during plant operation. Specifically, a prototype for the Kori-2 nuclear power unit is developed and examined is very difficult because the system structure is highly complex and the components are very much inter-related. In this study, some major diagnostic performance parameters are selected in order to represent the thermal cycle effectively and to reduce the computing time. The Fuzzy ARTMAP, a self-organizing neural network, is used to recognize the characteristic pattern change of the performance parameters in abnormal situation. By examination, the algorithm is shown to be ale to detect abnormality and to identify the fault component or the change of system operation condition successfully. For the convenience of operators, a graphical user interface is also constructed in this work. 5 figs., 3 tabs., 11 refs. (Author)

Applications of self-organizing neural networks in virtual screening and diversity selection.

Science.gov (United States)

Selzer, Paul; Ertl, Peter

2006-01-01

Artificial neural networks provide a powerful technique for the analysis and modeling of nonlinear relationships between molecular structures and pharmacological activity. Many network types, including Kohonen and counterpropagation, also provide an intuitive method for the visual assessment of correspondence between the input and output data. This work shows how a combination of neural networks and radial distribution function molecular descriptors can be applied in various areas of industrial pharmaceutical research. These applications include the prediction of biological activity, the selection of screening candidates (cherry picking), and the extraction of representative subsets from large compound collections such as combinatorial libraries. The methods described have also been implemented as an easy-to-use Web tool, allowing chemists to perform interactive neural network experiments on the Novartis intranet.

The Neural Correlates Underlying Belief Reasoning for Self and for Others: Evidence from ERPs.

Science.gov (United States)

Jiang, Qin; Wang, Qi; Li, Peng; Li, Hong

2016-01-01

Belief reasoning is typical mental state reasoning in theory of mind (ToM). Although previous studies have explored the neural bases of belief reasoning, the neural correlates of belief reasoning for self and for others are rarely addressed. The decoupling mechanism of distinguishing the mental state of others from one's own is essential for ToM processing. To address the electrophysiological bases underlying the decoupling mechanism, the present event-related potential study compared the time course of neural activities associated with belief reasoning for self and for others when the belief belonging to self was consistent or inconsistent with others. Results showed that during a 450-600 ms period, belief reasoning for self elicited a larger late positive component (LPC) than for others when beliefs were inconsistent with each other. The LPC divergence is assumed to reflect the categorization of agencies in ToM processes.

Recalled peer relationship experiences and current levels of self-criticism and self-reassurance.

Science.gov (United States)

Kopala-Sibley, Daniel C; Zuroff, David C; Leybman, Michelle J; Hope, Nora

2013-03-01

Numerous studies have shown that personality factors may increase or decrease individuals' vulnerability to depression, but little research has examined the role of peer relationships in the development of these factors. Accordingly, this study examined the role of recalled parenting and peer experiences in the development of self-criticism and self-reassurance. It was hypothesized that, controlling for recalled parenting behaviours, specific recalled experiences of peer relationships would be related to current levels of specific forms of self-criticism and self-reassurance. Hypotheses were tested using a retrospective design in which participants were asked to recall experiences of parenting and peer relationships during early adolescence. This age was chosen as early adolescence has been shown to be a critical time for the development of vulnerability to depression. A total of 103 female and 97 male young adults completed measures of recalled parenting, overt and relational victimization and prosocial behaviour by peers, and current levels of self-criticism and self-reassurance. Hierarchical regression analyses showed that parents and peers independently contributed to the development of self-criticism and self-reassurance. Specifically, controlling for parental care and control, overt victimization predicted self-hating self-criticism, relational victimization predicted inadequacy self-criticism, and prosocial behaviour predicted self-reassurance. As well, prosocial behaviour buffered the effect of overt victimization on self-reassurance. Findings highlight the importance of peers in the development of personality risk and resiliency factors for depression, and suggest avenues for interventions to prevent the development of depressive vulnerabilities in youth. The nature of a patient's personality vulnerability to depression may be better understood through a consideration of the patient's relationships with their peers as well as with parents during

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

Science.gov (United States)

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-04-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

Directory of Open Access Journals (Sweden)

Kai Olav Ellefsen

2015-04-01

Full Text Available A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand. To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1 that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2 that one benefit of the modularity ubiquitous in the brains of natural animals

Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

Science.gov (United States)

Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

2015-01-01

A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

Neural Networks for Self-tuning Control Systems

Directory of Open Access Journals (Sweden)

A. Noriega Ponce

2004-01-01

Full Text Available In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications. 

Self-organized neural network for the quality control of 12-lead ECG signals

International Nuclear Information System (INIS)

Chen, Yun; Yang, Hui

2012-01-01

Telemedicine is very important for the timely delivery of health care to cardiovascular patients, especially those who live in the rural areas of developing countries. However, there are a number of uncertainty factors inherent to the mobile-phone-based recording of electrocardiogram (ECG) signals such as personnel with minimal training and other extraneous noises. PhysioNet organized a challenge in 2011 to develop efficient algorithms that can assess the ECG signal quality in telemedicine settings. This paper presents our efforts in this challenge to integrate multiscale recurrence analysis with a self-organizing map for controlling the ECG signal quality. As opposed to directly evaluating the 12-lead ECG, we utilize an information-preserving transform, i.e. Dower transform, to derive the 3-lead vectorcardiogram (VCG) from the 12-lead ECG in the first place. Secondly, we delineate the nonlinear and nonstationary characteristics underlying the 3-lead VCG signals into multiple time-frequency scales. Furthermore, a self-organizing map is trained, in both supervised and unsupervised ways, to identify the correlations between signal quality and multiscale recurrence features. The efficacy and robustness of this approach are validated using real-world ECG recordings available from PhysioNet. The average performance was demonstrated to be 95.25% for the training dataset and 90.0% for the independent test dataset with unknown labels. (paper)

USING STROKE-BASED OR CHARACTER-BASED SELF-ORGANIZING MAPS IN THE RECOGNITION OF ONLINE, CONNECTED CURSIVE SCRIPT

NARCIS (Netherlands)

SCHOMAKER, L

Comparisons are made between a number of stroke-based and character-based recognizers of connected cursive script. In both approaches a Kohonen self-organizing neural network is used as a feature-vector quantizer. It is found that a ''best match only'' character-based recognizer performs better than

Ferritin nanoparticles for improved self-renewal and differentiation of human neural stem cells.

Science.gov (United States)

Lee, Jung Seung; Yang, Kisuk; Cho, Ann-Na; Cho, Seung-Woo

2018-01-01

Biomaterials that promote the self-renewal ability and differentiation capacity of neural stem cells (NSCs) are desirable for improving stem cell therapy to treat neurodegenerative diseases. Incorporation of micro- and nanoparticles into stem cell culture has gained great attention for the control of stem cell behaviors, including proliferation and differentiation. In this study, ferritin, an iron-containing natural protein nanoparticle, was applied as a biomaterial to improve the self-renewal and differentiation of NSCs and neural progenitor cells (NPCs). Ferritin nanoparticles were added to NSC or NPC culture during cell growth, allowing for incorporation of ferritin nanoparticles during neurosphere formation. Compared to neurospheres without ferritin treatment, neurospheres with ferritin nanoparticles showed significantly promoted self-renewal and cell-cell interactions. When spontaneous differentiation of neurospheres was induced during culture without mitogenic factors, neuronal differentiation was enhanced in the ferritin-treated neurospheres. In conclusion, we found that natural nanoparticles can be used to improve the self-renewal ability and differentiation potential of NSCs and NPCs, which can be applied in neural tissue engineering and cell therapy for neurodegenerative diseases.

The Relationship between Critical Thinking Disposition and Self-Esteem

Directory of Open Access Journals (Sweden)

Shirin Iranfar

2013-12-01

Full Text Available Introduction: Critical Thinking Disposition indicates individual’s inclination to Critical Thinking, which is one of the domains of personality. Individual characteristics are important and influential factors in the growth and development of students’ Critical Thinking. One of these influential characteristics might be self-esteem, thus this study was to determine the correlation between Critical Thinking Disposition and self-esteem in medical students. Methods: In an analytical cross-sectional study, 289 medical students were selected through stratified random sampling method in Kermanshah University of Medical Sciences in 2011. The instrument for data collection was a questionnaire containing 3 parts: demographic data, California Critical Thinking Disposition Inventory, and Cooper-Smith Self-Esteem Inventory. The results were analyzed by SPSS-16 using descriptive statistics, Pearson and Spearman Correlation Coefficient, ANOVA, Chi-Square and Fisher exact test. Results: Results showed that 98.6% (285 of students had deficiency, 1.4% (4 ambivalence and nobody had positive critical thinking disposition. There was a significantly negative correlation between Critical Thinking Disposition and self-esteem (r=-0.462, P<0.001. Also, there was no a significant relationship between two groups of low self-esteem , high self-esteem , negative and ambivalent Critical Thinking Disposition. Conclusion: It seems that Critical Thinking Disposition, like other psychological variables, is influenced by social factors and social environment plays a role in promoting or undermining it. So, similar studies are recommended to investigate the factors affecting Critical Thinking in medical students.

Critical Care Organizations: Building and Integrating Academic Programs.

Science.gov (United States)

Moore, Jason E; Oropello, John M; Stoltzfus, Daniel; Masur, Henry; Coopersmith, Craig M; Nates, Joseph; Doig, Christopher; Christman, John; Hite, R Duncan; Angus, Derek C; Pastores, Stephen M; Kvetan, Vladimir

2018-04-01

Academic medical centers in North America are expanding their missions from the traditional triad of patient care, research, and education to include the broader issue of healthcare delivery improvement. In recent years, integrated Critical Care Organizations have developed within academic centers to better meet the challenges of this broadening mission. The goal of this article was to provide interested administrators and intensivists with the proper resources, lines of communication, and organizational approach to accomplish integration and Critical Care Organization formation effectively. The Academic Critical Care Organization Building section workgroup of the taskforce established regular monthly conference calls to reach consensus on the development of a toolkit utilizing methods proven to advance the development of their own academic Critical Care Organizations. Relevant medical literature was reviewed by literature search. Materials from federal agencies and other national organizations were accessed through the Internet. The Society of Critical Care Medicine convened a taskforce entitled "Academic Leaders in Critical Care Medicine" on February 22, 2016 at the 45th Critical Care Congress using the expertise of successful leaders of advanced governance Critical Care Organizations in North America to develop a toolkit for advancing Critical Care Organizations. Key elements of an academic Critical Care Organization are outlined. The vital missions of multidisciplinary patient care, safety, and quality are linked to the research, education, and professional development missions that enhance the value of such organizations. Core features, benefits, barriers, and recommendations for integration of academic programs within Critical Care Organizations are described. Selected readings and resources to successfully implement the recommendations are provided. Communication with medical school and hospital leadership is discussed. We present the rationale for critical

Respiratory therapists and critical-thinking behaviors: a self-assessment.

Science.gov (United States)

Goodfellow, L T

2001-01-01

The purpose of this study was to assess critical-thinking behaviors of respiratory therapists through self-report. Using a quantitative survey research method, respiratory therapists rated themselves on seven critical thinking skills. The effects of personal variables on the self-assessments were also investigated. The respiratory therapists self-assessed their critical-thinking behaviors highest in the categories of prioritizing, troubleshooting, and communicating. Anticipating was self-assessed as the lowest-ranked critical-thinking behavior. Age and educational level were found to have no effect on the self-assessed behaviors, while years of experience in respiratory care and gender were found to affect self-assessed troubleshooting, decision making, and anticipating. The results of this study suggest that educators and clinicians should consider learning strategies that incorporate the use of experience when targeting novice practitioners.

Germ layers, the neural crest and emergent organization in development and evolution.

Science.gov (United States)

Hall, Brian K

2018-04-10

Discovered in chick embryos by Wilhelm His in 1868 and named the neural crest by Arthur Milnes Marshall in 1879, the neural crest cells that arise from the neural folds have since been shown to differentiate into almost two dozen vertebrate cell types and to have played major roles in the evolution of such vertebrate features as bone, jaws, teeth, visceral (pharyngeal) arches, and sense organs. I discuss the discovery that ectodermal neural crest gave rise to mesenchyme and the controversy generated by that finding; the germ layer theory maintained that only mesoderm could give rise to mesenchyme. A second topic of discussion is germ layers (including the neural crest) as emergent levels of organization in animal development and evolution that facilitated major developmental and evolutionary change. The third topic is gene networks, gene co-option, and the evolution of gene-signaling pathways as key to developmental and evolutionary transitions associated with the origin and evolution of the neural crest and neural crest cells. © 2018 Wiley Periodicals, Inc.

Neural network recognition of mammographic lesions

International Nuclear Information System (INIS)

Oldham, W.J.B.; Downes, P.T.; Hunter, V.

1987-01-01

A method for recognition of mammographic lesions through the use of neural networks is presented. Neural networks have exhibited the ability to learn the shape andinternal structure of patterns. Digitized mammograms containing circumscribed and stelate lesions were used to train a feedfoward synchronous neural network that self-organizes to stable attractor states. Encoding of data for submission to the network was accomplished by performing a fractal analysis of the digitized image. This results in scale invariant representation of the lesions. Results are discussed

Development of Self-Assembled Nanoribbon Bound Peptide-Polyaniline Composite Scaffolds and Their Interactions with Neural Cortical Cells

Directory of Open Access Journals (Sweden)

Andrew M. Smith

2018-01-01

Full Text Available Degenerative neurological disorders and traumatic brain injuries cause significant damage to quality of life and often impact survival. As a result, novel treatments are necessary that can allow for the regeneration of neural tissue. In this work, a new biomimetic scaffold was designed with potential for applications in neural tissue regeneration. To develop the scaffold, we first prepared a new bolaamphiphile that was capable of undergoing self-assembly into nanoribbons at pH 7. Those nanoribbons were then utilized as templates for conjugation with specific proteins known to play a critical role in neural tissue growth. The template (Ile-TMG-Ile was prepared by conjugating tetramethyleneglutaric acid with isoleucine and the ability of the bolaamphiphile to self-assemble was probed at a pH range of 4 through 9. The nanoribbons formed under neutral conditions were then functionalized step-wise with the basement membrane protein laminin, the neurotropic factor artemin and Type IV collagen. The conductive polymer polyaniline (PANI was then incorporated through electrostatic and π–π stacking interactions to the scaffold to impart electrical properties. Distinct morphology changes were observed upon conjugation with each layer, which was also accompanied by an increase in Young’s Modulus as well as surface roughness. The Young’s Modulus of the dried PANI-bound biocomposite scaffolds was found to be 5.5 GPa, indicating the mechanical strength of the scaffold. Thermal phase changes studied indicated broad endothermic peaks upon incorporation of the proteins which were diminished upon binding with PANI. The scaffolds also exhibited in vitro biodegradable behavior over a period of three weeks. Furthermore, we observed cell proliferation and short neurite outgrowths in the presence of rat neural cortical cells, confirming that the scaffolds may be applicable in neural tissue regeneration. The electrochemical properties of the scaffolds were also

Development of Self-Assembled Nanoribbon Bound Peptide-Polyaniline Composite Scaffolds and Their Interactions with Neural Cortical Cells

Science.gov (United States)

Smith, Andrew M.; Pajovich, Harrison T.; Banerjee, Ipsita A.

2018-01-01

Degenerative neurological disorders and traumatic brain injuries cause significant damage to quality of life and often impact survival. As a result, novel treatments are necessary that can allow for the regeneration of neural tissue. In this work, a new biomimetic scaffold was designed with potential for applications in neural tissue regeneration. To develop the scaffold, we first prepared a new bolaamphiphile that was capable of undergoing self-assembly into nanoribbons at pH 7. Those nanoribbons were then utilized as templates for conjugation with specific proteins known to play a critical role in neural tissue growth. The template (Ile-TMG-Ile) was prepared by conjugating tetramethyleneglutaric acid with isoleucine and the ability of the bolaamphiphile to self-assemble was probed at a pH range of 4 through 9. The nanoribbons formed under neutral conditions were then functionalized step-wise with the basement membrane protein laminin, the neurotropic factor artemin and Type IV collagen. The conductive polymer polyaniline (PANI) was then incorporated through electrostatic and π–π stacking interactions to the scaffold to impart electrical properties. Distinct morphology changes were observed upon conjugation with each layer, which was also accompanied by an increase in Young’s Modulus as well as surface roughness. The Young’s Modulus of the dried PANI-bound biocomposite scaffolds was found to be 5.5 GPa, indicating the mechanical strength of the scaffold. Thermal phase changes studied indicated broad endothermic peaks upon incorporation of the proteins which were diminished upon binding with PANI. The scaffolds also exhibited in vitro biodegradable behavior over a period of three weeks. Furthermore, we observed cell proliferation and short neurite outgrowths in the presence of rat neural cortical cells, confirming that the scaffolds may be applicable in neural tissue regeneration. The electrochemical properties of the scaffolds were also studied by

A review of organic and inorganic biomaterials for neural interfaces.

Science.gov (United States)

Fattahi, Pouria; Yang, Guang; Kim, Gloria; Abidian, Mohammad Reza

2014-03-26

Recent advances in nanotechnology have generated wide interest in applying nanomaterials for neural prostheses. An ideal neural interface should create seamless integration into the nervous system and performs reliably for long periods of time. As a result, many nanoscale materials not originally developed for neural interfaces become attractive candidates to detect neural signals and stimulate neurons. In this comprehensive review, an overview of state-of-the-art microelectrode technologies provided fi rst, with focus on the material properties of these microdevices. The advancements in electro active nanomaterials are then reviewed, including conducting polymers, carbon nanotubes, graphene, silicon nanowires, and hybrid organic-inorganic nanomaterials, for neural recording, stimulation, and growth. Finally, technical and scientific challenges are discussed regarding biocompatibility, mechanical mismatch, and electrical properties faced by these nanomaterials for the development of long-lasting functional neural interfaces.

Orphan nuclear receptor TLX activates Wnt/β-catenin signalling to stimulate neural stem cell proliferation and self-renewal

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Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T.; Gage, Fred H.; Evans, Ronald M.; Shi, Yanhong

2010-01-01

The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/β-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/β-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active β-catenin promote neural stem cell self-renewal, whereas the deletion of Wnt7a or the lentiviral transduction of axin, a β-catenin inhibitor, led to decreased cell proliferation in adult neurogenic areas. Lentiviral transduction of active β-catenin led to increased numbers of type B neural stem cells in the subventricular zone of adult brains, whereas deletion of Wnt7a or TLX resulted in decreased numbers of neural stem cells retaining bromodeoxyuridine label in the adult brain. Both Wnt7a and active β-catenin significantly rescued a TLX (also known as Nr2e1) short interfering RNA-induced deficiency in neural stem cell proliferation. Lentiviral transduction of an active β-catenin increased cell proliferation in neurogenic areas of TLX-null adult brains markedly. These results strongly support the hypothesis that TLX acts through the Wnt/β-catenin pathway to regulate neural stem cell proliferation and self-renewal. Moreover, this study suggests that neural stem cells can promote their own self-renewal by secreting signalling molecules that act in an autocrine/paracrine mode. PMID:20010817

Orphan nuclear receptor TLX activates Wnt/beta-catenin signalling to stimulate neural stem cell proliferation and self-renewal.

Science.gov (United States)

Qu, Qiuhao; Sun, Guoqiang; Li, Wenwu; Yang, Su; Ye, Peng; Zhao, Chunnian; Yu, Ruth T; Gage, Fred H; Evans, Ronald M; Shi, Yanhong

2010-01-01

The nuclear receptor TLX (also known as NR2E1) is essential for adult neural stem cell self-renewal; however, the molecular mechanisms involved remain elusive. Here we show that TLX activates the canonical Wnt/beta-catenin pathway in adult mouse neural stem cells. Furthermore, we demonstrate that Wnt/beta-catenin signalling is important in the proliferation and self-renewal of adult neural stem cells in the presence of epidermal growth factor and fibroblast growth factor. Wnt7a and active beta-catenin promote neural stem cell self-renewal, whereas the deletion of Wnt7a or the lentiviral transduction of axin, a beta-catenin inhibitor, led to decreased cell proliferation in adult neurogenic areas. Lentiviral transduction of active beta-catenin led to increased numbers of type B neural stem cells in the subventricular zone of adult brains, whereas deletion of Wnt7a or TLX resulted in decreased numbers of neural stem cells retaining bromodeoxyuridine label in the adult brain. Both Wnt7a and active beta-catenin significantly rescued a TLX (also known as Nr2e1) short interfering RNA-induced deficiency in neural stem cell proliferation. Lentiviral transduction of an active beta-catenin increased cell proliferation in neurogenic areas of TLX-null adult brains markedly. These results strongly support the hypothesis that TLX acts through the Wnt/beta-catenin pathway to regulate neural stem cell proliferation and self-renewal. Moreover, this study suggests that neural stem cells can promote their own self-renewal by secreting signalling molecules that act in an autocrine/paracrine mode.

Chaotic state to self-organized critical state transition of serrated flow dynamics during brittle-to-ductile transition in metallic glass

Energy Technology Data Exchange (ETDEWEB)

Wang, C.; Wang, W. H.; Bai, H. Y., E-mail: hybai@aphy.iphy.ac.cn [Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Sun, B. A. [Centre for Advanced Structural Materials, Department of Mechanical and Biomedical Engineering, City University of Hong Kong, Kowloon Tong, Kowloon (Hong Kong)

2016-02-07

We study serrated flow dynamics during brittle-to-ductile transition induced by tuning the sample aspect ratio in a Zr-based metallic glass. The statistical analysis reveals that the serrated flow dynamics transforms from a chaotic state characterized by Gaussian-distribution serrations corresponding to stick-slip motion of randomly generated and uncorrelated single shear band and brittle behavior, into a self-organized critical state featured by intermittent scale-free distribution of shear avalanches corresponding to a collective motion of multiple shear bands and ductile behavior. The correlation found between serrated flow dynamics and plastic deformation might shed light on the plastic deformation dynamic and mechanism in metallic glasses.

Brain Basis of Self: Self-Organization and Lessons from Dreaming

Directory of Open Access Journals (Sweden)

David eKahn

2013-07-01

Full Text Available Through dreaming a different facet of the self is created as a result of a self-organizing process in the brain. Self-organization in biological systems often happens as an answer to an environmental change for which the existing system cannot cope; self-organization creates a system that can cope in the newly changed environment. In dreaming, self-organization serves the function of organizing disparate memories into a dream since the dreamer herself is not able to control how individual memories become weaved into a dream. The self-organized dream provides, thereby, a wide repertoire of experiences; this expanded repertoire of experience results in an expansion of the self beyond that obtainable when awake. Since expression of the self is associated with activity in specific areas of the brain, the article also discusses the brain basis of the self by reviewing studies of brain injured patients, discussing brain imaging studies in normal brain functioning when focused, when daydreaming and when asleep and dreaming.

Critical Point in Self-Organized Tissue Growth

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Aguilar-Hidalgo, Daniel; Werner, Steffen; Wartlick, Ortrud; González-Gaitán, Marcos; Friedrich, Benjamin M.; Jülicher, Frank

2018-05-01

We present a theory of pattern formation in growing domains inspired by biological examples of tissue development. Gradients of signaling molecules regulate growth, while growth changes these graded chemical patterns by dilution and advection. We identify a critical point of this feedback dynamics, which is characterized by spatially homogeneous growth and proportional scaling of patterns with tissue length. We apply this theory to the biological model system of the developing wing of the fruit fly Drosophila melanogaster and quantitatively identify signatures of the critical point.

Observation of self-organized criticality (SOC) behavior during edge biasing experiment on TEXTOR

Energy Technology Data Exchange (ETDEWEB)

Xu, Y.H.; Jachmich, S.; Weynants, R.R. [Ecole Royale Militaire/Koninklijke Militaire School, Laboratory for Plasma Physics, Euratom-Belgian State Association, Brussels, Belgium, Partner in the Trilateral Euregio Cluster (Belgium)

2004-07-01

The self-organized criticality (SOC) behavior of the edge plasma transport has been investigated using the fluctuation data measured in the plasma edge and the scrape-off layer of TEXTOR tokamak before and during the edge electrode biasing experiments. In the 'non-shear' discharge phase before biasing, both the potential and density fluctuations clearly exhibit some of the characteristics associated with SOC: (1) existence of f{sup -1} power-law dependence in the frequency spectrum, (2) slowly decaying long tails in the autocorrelation function, (3) values of Hurst parameters larger than 0.5 at all the detected radial locations, (4) non-Gaussian probability density function of fluctuations and (5) radial propagation of avalanche-like events in the edge plasma area. During the biasing phase, with the generation of an edge radial electric field E{sub r} and hence a sheared E{sub r} x B flow, the local turbulence is found to be well de-correlated by the E{sub r} x B velocity shear, consistent with theoretical predictions. Nevertheless, it is concomitantly found that the Hurst parameters are substantially enhanced in the negative flow shear region and in the scrape-off layer as well, which is contrary to theoretical expectation. Implication of these observations to our understanding of plasma transport mechanisms is discussed. (authors)

Sociocultural patterning of neural activity during self-reflection.

Science.gov (United States)

Ma, Yina; Bang, Dan; Wang, Chenbo; Allen, Micah; Frith, Chris; Roepstorff, Andreas; Han, Shihui

2014-01-01

Western cultures encourage self-construals independent of social contexts, whereas East Asian cultures foster interdependent self-construals that rely on how others perceive the self. How are culturally specific self-construals mediated by the human brain? Using functional magnetic resonance imaging, we monitored neural responses from adults in East Asian (Chinese) and Western (Danish) cultural contexts during judgments of social, mental and physical attributes of themselves and public figures to assess cultural influences on self-referential processing of personal attributes in different dimensions. We found that judgments of self vs a public figure elicited greater activation in the medial prefrontal cortex (mPFC) in Danish than in Chinese participants regardless of attribute dimensions for judgments. However, self-judgments of social attributes induced greater activity in the temporoparietal junction (TPJ) in Chinese than in Danish participants. Moreover, the group difference in TPJ activity was mediated by a measure of a cultural value (i.e. interdependence of self-construal). Our findings suggest that individuals in different sociocultural contexts may learn and/or adopt distinct strategies for self-reflection by changing the weight of the mPFC and TPJ in the social brain network.

Self-Tuning Vibration Control of a Rotational Flexible Timoshenko Arm Using Neural Networks

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Minoru Sasaki

2012-01-01

Full Text Available A self-tuning vibration control of a rotational flexible arm using neural networks is presented. To the self-tuning control system, the control scheme consists of gain tuning neural networks and a variable-gain feedback controller. The neural networks are trained so as to make the root moment zero. In the process, the neural networks learn the optimal gain of the feedback controller. The feedback controller is designed based on Lyapunov's direct method. The feedback control of the vibration of the flexible system is derived by considering the time rate of change of the total energy of the system. This approach has the advantage over the conventional methods in the respect that it allows one to deal directly with the system's partial differential equations without resorting to approximations. Numerical and experimental results for the vibration control of a rotational flexible arm are discussed. It verifies that the proposed control system is effective at controlling flexible dynamical systems.

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

International Nuclear Information System (INIS)

Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

2015-01-01

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing

Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

Energy Technology Data Exchange (ETDEWEB)

Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin, E-mail: xmli@cqu.edu.cn [Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044 (China); College of Automation, Chongqing University, Chongqing 400044 (China)

2015-11-15

In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy.

Science.gov (United States)

Jia, Zi-Jun; Song, Yong-Duan

2017-06-01

This paper presents a new approach to construct neural adaptive control for uncertain nonaffine systems. By integrating locally weighted learning with barrier Lyapunov function (BLF), a novel control design method is presented to systematically address the two critical issues in neural network (NN) control field: one is how to fulfill the compact set precondition for NN approximation, and the other is how to use varying rather than a fixed NN structure to improve the functionality of NN control. A BLF is exploited to ensure the NN inputs to remain bounded during the entire system operation. To account for system nonlinearities, a neuron self-growing strategy is proposed to guide the process for adding new neurons to the system, resulting in a self-adjustable NN structure for better learning capabilities. It is shown that the number of neurons needed to accomplish the control task is finite, and better performance can be obtained with less number of neurons as compared with traditional methods. The salient feature of the proposed method also lies in the continuity of the control action everywhere. Furthermore, the resulting control action is smooth almost everywhere except for a few time instants at which new neurons are added. Numerical example illustrates the effectiveness of the proposed approach.

The impact of cultural differences in self-representation on the neural substrates of posttraumatic stress disorder

Directory of Open Access Journals (Sweden)

Belinda J. Liddell

2016-06-01

Full Text Available A significant body of literature documents the neural mechanisms involved in the development and maintenance of posttraumatic stress disorder (PTSD. However, there is very little empirical work considering the influence of culture on these underlying mechanisms. Accumulating cultural neuroscience research clearly indicates that cultural differences in self-representation modulate many of the same neural processes proposed to be aberrant in PTSD. The objective of this review paper is to consider how culture may impact on the neural mechanisms underlying PTSD. We first outline five key affective and cognitive functions and their underlying neural correlates that have been identified as being disrupted in PTSD: (1 fear dysregulation; (2 attentional biases to threat; (3 emotion and autobiographical memory; (4 self-referential processing; and (5 attachment and interpersonal processing. Second, we consider prominent cultural theories and review the empirical research that has demonstrated the influence of cultural variations in self-representation on the neural substrates of these same five affective and cognitive functions. Finally, we propose a conceptual model that suggests that these five processes have major relevance to considering how culture may influence the neural processes underpinning PTSD. Highlights of the article:

The impact of cultural differences in self-representation on the neural substrates of posttraumatic stress disorder.

Science.gov (United States)

Liddell, Belinda J; Jobson, Laura

2016-01-01

A significant body of literature documents the neural mechanisms involved in the development and maintenance of posttraumatic stress disorder (PTSD). However, there is very little empirical work considering the influence of culture on these underlying mechanisms. Accumulating cultural neuroscience research clearly indicates that cultural differences in self-representation modulate many of the same neural processes proposed to be aberrant in PTSD. The objective of this review paper is to consider how culture may impact on the neural mechanisms underlying PTSD. We first outline five key affective and cognitive functions and their underlying neural correlates that have been identified as being disrupted in PTSD: (1) fear dysregulation; (2) attentional biases to threat; (3) emotion and autobiographical memory; (4) self-referential processing; and (5) attachment and interpersonal processing. Second, we consider prominent cultural theories and review the empirical research that has demonstrated the influence of cultural variations in self-representation on the neural substrates of these same five affective and cognitive functions. Finally, we propose a conceptual model that suggests that these five processes have major relevance to considering how culture may influence the neural processes underpinning PTSD.

Effects of Random Environment on a Self-Organized Critical System: Renormalization Group Analysis of a Continuous Model

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Antonov N.V.

2016-01-01

Full Text Available We study effects of the random fluid motion on a system in a self-organized critical state. The latter is described by the continuous stochastic model proposed by Hwa and Kardar [Phys. Rev. Lett. 62: 1813 (1989]. The advecting velocity field is Gaussian, not correlated in time, with the pair correlation function of the form ∝ δ(t − t′/k⊥d-1+ξ , where k⊥ = |k⊥| and k⊥ is the component of the wave vector, perpendicular to a certain preferred direction – the d-dimensional generalization of the ensemble introduced by Avellaneda and Majda [Commun. Math. Phys. 131: 381 (1990]. Using the field theoretic renormalization group we show that, depending on the relation between the exponent ξ and the spatial dimension d, the system reveals different types of large-scale, long-time scaling behaviour, associated with the three possible fixed points of the renormalization group equations. They correspond to ordinary diffusion, to passively advected scalar field (the nonlinearity of the Hwa–Kardar model is irrelevant and to the “pure” Hwa–Kardar model (the advection is irrelevant. For the special case ξ = 2(4 − d/3 both the nonlinearity and the advection are important. The corresponding critical exponents are found exactly for all these cases.

Self-generation of controller of an underwater robot with neural network

International Nuclear Information System (INIS)

Suto, T.; Ura, T.

1994-01-01

A self-organizing controller system is constructed based on artificial neural networks and applied to constant altitude swimming of the autonomous underwater robot PTEROA 150. The system consists of a controller and a forward model which calculates the values for evaluation as a result of control. Some methods are introduced for quick and appropriate adjustment of the controller network. Modification of the controller network is executed based on error-back-propagation method utilizing the forward model network. The forward model is divided into three sub-networks which represent dynamics of the vehicle, estimation of relative position to the seabed and calculation of the altitude. The proposed adaptive system is demonstrated in computer simulations where objective of a vehicle is keeping a constant altitude from seabed which is constituted of triangular ridges

Neural differences in self-perception during illness and after weight-recovery in anorexia nervosa.

Science.gov (United States)

McAdams, Carrie J; Jeon-Slaughter, Haekyung; Evans, Siobahn; Lohrenz, Terry; Montague, P Read; Krawczyk, Daniel C

2016-11-01

Anorexia nervosa (AN) is a severe mental illness characterized by problems with self-perception. Whole-brain neural activations in healthy women, women with AN and women in long-term weight recovery following AN were compared using two functional magnetic resonance imaging tasks probing different aspects of self-perception. The Social Identity-V2 task involved consideration about oneself and others using socially descriptive adjectives. Both the ill and weight-recovered women with AN engaged medial prefrontal cortex less than healthy women for self-relevant cognitions, a potential biological trait difference. Weight-recovered women also activated the inferior frontal gyri and dorsal anterior cingulate more for direct self-evaluations than for reflected self-evaluations, unlike both other groups, suggesting that recovery may include compensatory neural changes related to social perspectives. The Faces task compared viewing oneself to a stranger. Participants with AN showed elevated activity in the bilateral fusiform gyri for self-images, unlike the weight-recovered and healthy women, suggesting cognitive distortions about physical appearance are a state rather than trait problem in this disease. Because both ill and recovered women showed neural differences related to social self-perception, but only recovered women differed when considering social perspectives, these neurocognitive targets may be particularly important for treatment. © The Author (2016). Published by Oxford University Press.

Hierarchical Recursive Organization and the Free Energy Principle: From Biological Self-Organization to the Psychoanalytic Mind

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Patrick Connolly

2017-09-01

Full Text Available The present paper argues that a systems theory epistemology (and particularly the notion of hierarchical recursive organization provides the critical theoretical context within which the significance of Friston's (2010a Free Energy Principle (FEP for both evolution and psychoanalysis is best understood. Within this perspective, the FEP occupies a particular level of the hierarchical organization of the organism, which is the level of biological self-organization. This form of biological self-organization is in turn understood as foundational and pervasive to the higher levels of organization of the human organism that are of interest to both neuroscience as well as psychoanalysis. Consequently, central psychoanalytic claims should be restated, in order to be located in their proper place within a hierarchical recursive organization of the (situated organism. In light of the FEP the realization of the psychoanalytic mind by the brain should be seen in terms of the evolution of different levels of systematic organization where the concepts of psychoanalysis describe a level of hierarchical recursive organization superordinate to that of biological self-organization and the FEP. The implication of this formulation is that while “psychoanalytic” mental processes are fundamentally subject to the FEP, they nonetheless also add their own principles of process over and above that of the FEP. A model found in Grobbelaar (1989 offers a recursive bottom-up description of the self-organization of the psychoanalytic ego as dependent on the organization of language (and affect, which is itself founded upon the tendency toward autopoiesis (self-making within the organism, which is in turn described as formally similar to the FEP. Meaningful consilience between Grobbelaar's model and the hierarchical recursive description available in Friston's (2010a theory is described. The paper concludes that the valuable contribution of the FEP to psychoanalysis

The morphological classification of normal and abnormal red blood cell using Self Organizing Map

Science.gov (United States)

Rahmat, R. F.; Wulandari, F. S.; Faza, S.; Muchtar, M. A.; Siregar, I.

2018-02-01

Blood is an essential component of living creatures in the vascular space. For possible disease identification, it can be tested through a blood test, one of which can be seen from the form of red blood cells. The normal and abnormal morphology of the red blood cells of a patient is very helpful to doctors in detecting a disease. With the advancement of digital image processing technology can be used to identify normal and abnormal blood cells of a patient. This research used self-organizing map method to classify the normal and abnormal form of red blood cells in the digital image. The use of self-organizing map neural network method can be implemented to classify the normal and abnormal form of red blood cells in the input image with 93,78% accuracy testing.

A study of self organized criticality in ion temperature gradient mode driven gyrokinetic turbulence

Science.gov (United States)

Mavridis, M.; Isliker, H.; Vlahos, L.; Görler, T.; Jenko, F.; Told, D.

2014-10-01

An investigation on the characteristics of self organized criticality (Soc) in ITG mode driven turbulence is made, with the use of various statistical tools (histograms, power spectra, Hurst exponents estimated with the rescaled range analysis, and the structure function method). For this purpose, local non-linear gyrokinetic simulations of the cyclone base case scenario are performed with the GENE software package. Although most authors concentrate on global simulations, which seem to be a better choice for such an investigation, we use local simulations in an attempt to study the locally underlying mechanisms of Soc. We also study the structural properties of radially extended structures, with several tools (fractal dimension estimate, cluster analysis, and two dimensional autocorrelation function), in order to explore whether they can be characterized as avalanches. We find that, for large enough driving temperature gradients, the local simulations exhibit most of the features of Soc, with the exception of the probability distribution of observables, which show a tail, yet they are not of power-law form. The radial structures have the same radial extent at all temperature gradients examined; radial motion (transport) though appears only at large temperature gradients, in which case the radial structures can be interpreted as avalanches.

Particle acceleration in solar active regions being in the state of self-organized criticality.

Science.gov (United States)

Vlahos, Loukas

We review the recent observational results on flare initiation and particle acceleration in solar active regions. Elaborating a statistical approach to describe the spatiotemporally intermittent electric field structures formed inside a flaring solar active region, we investigate the efficiency of such structures in accelerating charged particles (electrons and protons). The large-scale magnetic configuration in the solar atmosphere responds to the strong turbulent flows that convey perturbations across the active region by initiating avalanche-type processes. The resulting unstable structures correspond to small-scale dissipation regions hosting strong electric fields. Previous research on particle acceleration in strongly turbulent plasmas provides a general framework for addressing such a problem. This framework combines various electromagnetic field configurations obtained by magnetohydrodynamical (MHD) or cellular automata (CA) simulations, or by employing a statistical description of the field’s strength and configuration with test particle simulations. We work on data-driven 3D magnetic field extrapolations, based on a self-organized criticality models (SOC). A relativistic test-particle simulation traces each particle’s guiding center within these configurations. Using the simulated particle-energy distributions we test our results against observations, in the framework of the collisional thick target model (CTTM) of solar hard X-ray (HXR) emission and compare our results with the current observations.

A study of self organized criticality in ion temperature gradient mode driven gyrokinetic turbulence

International Nuclear Information System (INIS)

Mavridis, M.; Isliker, H.; Vlahos, L.; Görler, T.; Jenko, F.; Told, D.

2014-01-01

An investigation on the characteristics of self organized criticality (Soc) in ITG mode driven turbulence is made, with the use of various statistical tools (histograms, power spectra, Hurst exponents estimated with the rescaled range analysis, and the structure function method). For this purpose, local non-linear gyrokinetic simulations of the cyclone base case scenario are performed with the GENE software package. Although most authors concentrate on global simulations, which seem to be a better choice for such an investigation, we use local simulations in an attempt to study the locally underlying mechanisms of Soc. We also study the structural properties of radially extended structures, with several tools (fractal dimension estimate, cluster analysis, and two dimensional autocorrelation function), in order to explore whether they can be characterized as avalanches. We find that, for large enough driving temperature gradients, the local simulations exhibit most of the features of Soc, with the exception of the probability distribution of observables, which show a tail, yet they are not of power-law form. The radial structures have the same radial extent at all temperature gradients examined; radial motion (transport) though appears only at large temperature gradients, in which case the radial structures can be interpreted as avalanches

A study of self organized criticality in ion temperature gradient mode driven gyrokinetic turbulence

Energy Technology Data Exchange (ETDEWEB)

Mavridis, M.; Isliker, H.; Vlahos, L. [Section of Astrophysics, Astronomy and Mechanics, Department of Physics, Aristotle University of Thessaloniki, GR-54124 Thessaloniki (Greece); Görler, T.; Jenko, F.; Told, D. [Max Planck Institute for Plasma Physics, Boltzmannstr. 2, 85748 Garching (Germany)

2014-10-15

An investigation on the characteristics of self organized criticality (Soc) in ITG mode driven turbulence is made, with the use of various statistical tools (histograms, power spectra, Hurst exponents estimated with the rescaled range analysis, and the structure function method). For this purpose, local non-linear gyrokinetic simulations of the cyclone base case scenario are performed with the GENE software package. Although most authors concentrate on global simulations, which seem to be a better choice for such an investigation, we use local simulations in an attempt to study the locally underlying mechanisms of Soc. We also study the structural properties of radially extended structures, with several tools (fractal dimension estimate, cluster analysis, and two dimensional autocorrelation function), in order to explore whether they can be characterized as avalanches. We find that, for large enough driving temperature gradients, the local simulations exhibit most of the features of Soc, with the exception of the probability distribution of observables, which show a tail, yet they are not of power-law form. The radial structures have the same radial extent at all temperature gradients examined; radial motion (transport) though appears only at large temperature gradients, in which case the radial structures can be interpreted as avalanches.

Psychopathic traits linked to alterations in neural activity during personality judgments of self and others.

Science.gov (United States)

Deming, Philip; Philippi, Carissa L; Wolf, Richard C; Dargis, Monika; Kiehl, Kent A; Koenigs, Michael

2018-01-01

Psychopathic individuals are notorious for their grandiose sense of self-worth and disregard for the welfare of others. One potential psychological mechanism underlying these traits is the relative consideration of "self" versus "others". Here we used task-based functional magnetic resonance imaging (fMRI) to identify neural responses during personality trait judgments about oneself and a familiar other in a sample of adult male incarcerated offenders ( n  = 57). Neural activity was regressed on two clusters of psychopathic traits: Factor 1 (e.g., egocentricity and lack of empathy) and Factor 2 (e.g., impulsivity and irresponsibility). Contrary to our hypotheses, Factor 1 scores were not significantly related to neural activity during self- or other-judgments. However, Factor 2 traits were associated with diminished activation to self-judgments, in relation to other-judgments, in bilateral posterior cingulate cortex and right temporoparietal junction. These findings highlight cortical regions associated with a dimension of social-affective cognition that may underlie psychopathic individuals' impulsive traits.

From self-organization to self-assembly: a new materialism?

Science.gov (United States)

Vincent, Bernadette Bensaude

2016-09-01

While self-organization has been an integral part of academic discussions about the distinctive features of living organisms, at least since Immanuel Kant's Critique of Judgement, the term 'self-assembly' has only been used for a few decades as it became a hot research topic with the emergence of nanotechnology. Could it be considered as an attempt at reducing vital organization to a sort of assembly line of molecules? Considering the context of research on self-assembly I argue that the shift of attention from self-organization to self-assembly does not really challenge the boundary between chemistry and biology. Self-assembly was first and foremost investigated in an engineering context as a strategy for manufacturing without human intervention and did not raise new perspectives on the emergence of vital organization itself. However self-assembly implies metaphysical assumptions that this paper tries to disentangle. It first describes the emergence of self-assembly as a research field in the context of materials science and nanotechnology. The second section outlines the metaphysical implications and will emphasize a sharp contrast between the ontology underlying two practices of self-assembly developed under the umbrella of synthetic biology. And unexpectedly, we shall see that chemists are less on the reductionist side than most synthetic biologists. Finally, the third section ventures some reflections on the kind of design involved in self-assembly practices.

Dependency and self-criticism in treatments for depression.

Science.gov (United States)

Chui, Harold; Zilcha-Mano, Sigal; Dinger, Ulrike; Barrett, Marna S; Barber, Jacques P

2016-07-01

Dependency and self-criticism are vulnerability factors for depression. How these personality factors change with treatment for depression and how they relate to symptom change across different types of treatment require further research. In addition, cultural differences that interact with the dependency/self-criticism-depression relation remain underinvestigated. We randomly assigned 149 adults with major depression to receive active medication (MED; n = 50), supportive-expressive therapy (SET; n = 49), or placebo pill (PBO; n = 50). Participants completed the Depressive Experiences Questionnaire (DEQ; Blatt, D'Afflitti, & Quinlan, 1976) before and after treatment and completed the Hamilton Rating Scale for Depression (Hamilton, 1967) throughout the course of treatment. Self-criticism as measured on the DEQ decreased with treatment similarly across conditions. DEQ Dependency decreased in MED but remained unchanged in SET and PBO. Higher initial dependency, but not higher initial self-criticism, predicted poor treatment response across conditions. Greater reduction in self-criticism was associated with greater reduction in depressive symptoms, but the effect was weaker for racial minorities (vs. White). Increase in connectedness, an adaptive form of dependency, was associated with symptom improvement in SET but not MED. Hence, different pathways of change seem to be implicated in the treatment of depression depending on culture and type of intervention. Implications for future research are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Dynamical system with plastic self-organized velocity field as an alternative conceptual model of a cognitive system.

Science.gov (United States)

Janson, Natalia B; Marsden, Christopher J

2017-12-05

It is well known that architecturally the brain is a neural network, i.e. a collection of many relatively simple units coupled flexibly. However, it has been unclear how the possession of this architecture enables higher-level cognitive functions, which are unique to the brain. Here, we consider the brain from the viewpoint of dynamical systems theory and hypothesize that the unique feature of the brain, the self-organized plasticity of its architecture, could represent the means of enabling the self-organized plasticity of its velocity vector field. We propose that, conceptually, the principle of cognition could amount to the existence of appropriate rules governing self-organization of the velocity field of a dynamical system with an appropriate account of stimuli. To support this hypothesis, we propose a simple non-neuromorphic mathematical model with a plastic self-organized velocity field, which has no prototype in physical world. This system is shown to be capable of basic cognition, which is illustrated numerically and with musical data. Our conceptual model could provide an additional insight into the working principles of the brain. Moreover, hardware implementations of plastic velocity fields self-organizing according to various rules could pave the way to creating artificial intelligence of a novel type.

Self-organization theories and environmental management: The case of South Moresby, Canada

Science.gov (United States)

Grzybowski, Alex G. S.; Slocombe, D. Scott

1988-07-01

This article presents a new approach to the analysis and management of large-scale societal problems with complex ecological, economic, and social dimensions. The approach is based on the theory of self-organizing systems—complex, open, far-from-equilibrium systems with nonlinear dynamics. A brief overview and comparison of different self-organization theories (synergetics, self-organization theory, hypercycles, and autopoiesis) is presented in order to isolate the key characteristics of such systems. The approach is used to develop an analysis of the landuse controversy in the South Moresby area of the Queen Charlotte Islands, British Columbia, Canada. Critical variables are identified for each subsystem and classified by spatial and temporal scale, and discussed in terms of information content and internal/external origin. Eradication of sea otters, introduction of black-tailed deer, impacts of large-scale clearcut logging, sustainability of the coastal forest industry, and changing relations between native peoples and governments are discussed in detail to illustrate the system dynamics of the South Moresby “sociobiophysical” system. Finally, implications of the self-organizing sociobiophysical system view for regional analysis and management are identified.

A self-organized criticality model for ion temperature gradient mode driven turbulence in confined plasma

Science.gov (United States)

Isliker, H.; Pisokas, Th.; Strintzi, D.; Vlahos, L.

2010-08-01

A new self-organized criticality (SOC) model is introduced in the form of a cellular automaton (CA) for ion temperature gradient (ITG) mode driven turbulence in fusion plasmas. Main characteristics of the model are that it is constructed in terms of the actual physical variable, the ion temperature, and that the temporal evolution of the CA, which necessarily is in the form of rules, mimics actual physical processes as they are considered to be active in the system, i.e., a heating process and a local diffusive process that sets on if a threshold in the normalized ITG R /LT is exceeded. The model reaches the SOC state and yields ion temperature profiles of exponential shape, which exhibit very high stiffness, in that they basically are independent of the loading pattern applied. This implies that there is anomalous heat transport present in the system, despite the fact that diffusion at the local level is imposed to be of a normal kind. The distributions of the heat fluxes in the system and of the heat out-fluxes are of power-law shape. The basic properties of the model are in good qualitative agreement with experimental results.

A self-organized criticality model for ion temperature gradient mode driven turbulence in confined plasma

International Nuclear Information System (INIS)

Isliker, H.; Pisokas, Th.; Vlahos, L.; Strintzi, D.

2010-01-01

A new self-organized criticality (SOC) model is introduced in the form of a cellular automaton (CA) for ion temperature gradient (ITG) mode driven turbulence in fusion plasmas. Main characteristics of the model are that it is constructed in terms of the actual physical variable, the ion temperature, and that the temporal evolution of the CA, which necessarily is in the form of rules, mimics actual physical processes as they are considered to be active in the system, i.e., a heating process and a local diffusive process that sets on if a threshold in the normalized ITG R/L T is exceeded. The model reaches the SOC state and yields ion temperature profiles of exponential shape, which exhibit very high stiffness, in that they basically are independent of the loading pattern applied. This implies that there is anomalous heat transport present in the system, despite the fact that diffusion at the local level is imposed to be of a normal kind. The distributions of the heat fluxes in the system and of the heat out-fluxes are of power-law shape. The basic properties of the model are in good qualitative agreement with experimental results.

Innovative Mechanism of Rural Organization Based on Self-Organization

OpenAIRE

Wang, Xing jin; Gao, Bing

2011-01-01

The paper analyzes the basic situation for the formation of innovative rural organizations with the form of self-organization; revels the features of self-organization, including the four aspects of openness of rural organization, innovation of rural organization is far away from equilibrium, the non-linear response mechanism of rural organization innovation and the random rise and fall of rural organization innovation. The evolution mechanism of rural organization innovation is reveled accor...

Evolution of insurance company service quality survey, using self-learning neural network

Directory of Open Access Journals (Sweden)

Vladimír Konečný

2011-01-01

Full Text Available The objective of the paper is to demonstrate the abilities and possible approaches to classification of set of objects using self-organizing maps. As the objects, clients of an insurance company that made an agreement regarding mandatory insurance of motor vehicles were selected. The opinions of the clients and their overall satisfaction reflected in responses to presented answers. The clients were classified into three groups. The first two contained satisfied clients (i.e. good clients for the company, the last group contained clients that could potentially switch to the competitors. Subsequent analysis enabled discovering the reasons of low customer satisfaction and critical factors of losing the least satisfied clients. For the analysis of the responses (one hundred fifty-one and the insurance company, experimental model of self-organizing map realized at the Department of informatics was used. Used experimental model has proved very effective software tool.

Matrix regulators in neural stem cell functions.

Science.gov (United States)

Wade, Anna; McKinney, Andrew; Phillips, Joanna J

2014-08-01

Neural stem/progenitor cells (NSPCs) reside within a complex and dynamic extracellular microenvironment, or niche. This niche regulates fundamental aspects of their behavior during normal neural development and repair. Precise yet dynamic regulation of NSPC self-renewal, migration, and differentiation is critical and must persist over the life of an organism. In this review, we summarize some of the major components of the NSPC niche and provide examples of how cues from the extracellular matrix regulate NSPC behaviors. We use proteoglycans to illustrate the many diverse roles of the niche in providing temporal and spatial regulation of cellular behavior. The NSPC niche is comprised of multiple components that include; soluble ligands, such as growth factors, morphogens, chemokines, and neurotransmitters, the extracellular matrix, and cellular components. As illustrated by proteoglycans, a major component of the extracellular matrix, the NSPC, niche provides temporal and spatial regulation of NSPC behaviors. The factors that control NSPC behavior are vital to understand as we attempt to modulate normal neural development and repair. Furthermore, an improved understanding of how these factors regulate cell proliferation, migration, and differentiation, crucial for malignancy, may reveal novel anti-tumor strategies. This article is part of a Special Issue entitled Matrix-mediated cell behaviour and properties. Copyright © 2014 Elsevier B.V. All rights reserved.

Profile of the biodiesel B100 commercialized in the region of Londrina: application of artificial neural networks of the type self organizing maps

Directory of Open Access Journals (Sweden)

Vilson Machado de Campos Filho

2015-10-01

Full Text Available The 97 samples were grouped according to the year of analysis. For each year, letters from A to D were attributed, between 2010 and 2013; A (33 B (25 C (24 and D (15. The parameters of compliance previously analyzed are those established by the National Agency of Petroleum, Natural Gas and Biofuels (ANP, through resolution ANP 07/2008. The parameters analyzed were density, flash point, peroxide and acid value. The observed values were presented to Artificial Neural Network (ANN Self Organizing MAP (SOM in order to classify, by physical-chemical properties, each sample from year of production. The ANN was trained on different days and randomly divided samples into two groups, training and test set. It was found that SOM network differentiated samples by the year and the compliance parameters, allowing to identify that the density and the flash point were the most significant compliance parameters, so good for the distinction and classification of these samples.

Innovative Mechanism of Rural Organization Based on Self-Organization

Institute of Scientific and Technical Information of China (English)

2011-01-01

The paper analyzes the basic situation of the formation of innovative rural organizations with the form of self-organization;reveals the features of self-organization,including the four aspects of openness of rural organization,innovation of rural organization far away from equilibrium,the non-linear response mechanism of rural organization innovation and the random rise and fall of rural organization innovation.The evolution mechanism of rural organization innovation is revealed according to the growth stage,the ideal stage,the decline and the fall stage.The paper probes into the basic restriction mechanism of the self-organization evaluation of rural organization from three aspects,including target recognition,path dependence and knowledge sharing.The basic measures on cultivating the innovative mechanism of rural organization are put forward.Firstly,constructing the dissipative structure of rural organization innovation;secondly,cultivating the dynamic study capability of rural organization innovation system;thirdly,selecting the step-by-step evolution strategy of rural organization innovation system.

Mobility Model for Self-Organizing and Cooperative MSN and MANET Systems

Directory of Open Access Journals (Sweden)

Andrzej Sikora

2012-03-01

Full Text Available Self-organization mechanisms are used for building scalable systems consisting of a huge number of subsystems. In computer networks, self-organizing is especially important in ad hoc networking. A self-organizing ad hoc network is a collection of wireless devices that collaborate with each other to form a network system that adapts to achieve a goal or goals. Such network is often built from mobile devices that may spontaneously create a network and dynamically adapted to changes in an unknown environment. Mobility pattern is a critical element that influences the performance characteristics of mobile sensor networks (MSN and mobile ad hoc networks (MANET. In this paper, we survey main directions to mobility modeling. We describe a novel algorithm for calculating mobility patterns for mobile devices that is based on a cluster formation and an artificial potential function. Finally, we present the simulation results of its application to a rescue mission planning.

Adaptive critic learning techniques for engine torque and air-fuel ratio control.

Science.gov (United States)

Liu, Derong; Javaherian, Hossein; Kovalenko, Olesia; Huang, Ting

2008-08-01

A new approach for engine calibration and control is proposed. In this paper, we present our research results on the implementation of adaptive critic designs for self-learning control of automotive engines. A class of adaptive critic designs that can be classified as (model-free) action-dependent heuristic dynamic programming is used in this research project. The goals of the present learning control design for automotive engines include improved performance, reduced emissions, and maintained optimum performance under various operating conditions. Using the data from a test vehicle with a V8 engine, we developed a neural network model of the engine and neural network controllers based on the idea of approximate dynamic programming to achieve optimal control. We have developed and simulated self-learning neural network controllers for both engine torque (TRQ) and exhaust air-fuel ratio (AFR) control. The goal of TRQ control and AFR control is to track the commanded values. For both control problems, excellent neural network controller transient performance has been achieved.

Complex-valued neural networks advances and applications

CERN Document Server

Hirose, Akira

2013-01-01

Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and

Self-organized complex space charge configurations at the origin of flicker noise

International Nuclear Information System (INIS)

Popescu, S.; Lozneanu, E.; Sanduloviciu, M.

2003-01-01

Based on experimental results obtained from a plasma diode we explain the fluctuations of the voltage supported by a non-linear gaseous conductor by the dynamical behavior of spatiotemporal patterns, in the form of moving double layers, formed after self-organization. Such phenomena appear when the system is subjected to an external constraint that creates and maintains a local gradient of electron kinetic energy. The described phenomenology suggests a plausible explanation for the appearance of flicker noise also in other physical systems, as for example semiconductors and, implicitly, offers a new model for the so-called self-organized criticality concept

Evolutionary Cell Computing: From Protocells to Self-Organized Computing

Science.gov (United States)

Colombano, Silvano; New, Michael H.; Pohorille, Andrew; Scargle, Jeffrey; Stassinopoulos, Dimitris; Pearson, Mark; Warren, James

2000-01-01

On the path from inanimate to animate matter, a key step was the self-organization of molecules into protocells - the earliest ancestors of contemporary cells. Studies of the properties of protocells and the mechanisms by which they maintained themselves and reproduced are an important part of astrobiology. These studies also have the potential to greatly impact research in nanotechnology and computer science. Previous studies of protocells have focussed on self-replication. In these systems, Darwinian evolution occurs through a series of small alterations to functional molecules whose identities are stored. Protocells, however, may have been incapable of such storage. We hypothesize that under such conditions, the replication of functions and their interrelationships, rather than the precise identities of the functional molecules, is sufficient for survival and evolution. This process is called non-genomic evolution. Recent breakthroughs in experimental protein chemistry have opened the gates for experimental tests of non-genomic evolution. On the basis of these achievements, we have developed a stochastic model for examining the evolutionary potential of non-genomic systems. In this model, the formation and destruction (hydrolysis) of bonds joining amino acids in proteins occur through catalyzed, albeit possibly inefficient, pathways. Each protein can act as a substrate for polymerization or hydrolysis, or as a catalyst of these chemical reactions. When a protein is hydrolyzed to form two new proteins, or two proteins are joined into a single protein, the catalytic abilities of the product proteins are related to the catalytic abilities of the reactants. We will demonstrate that the catalytic capabilities of such a system can increase. Its evolutionary potential is dependent upon the competition between the formation of bond-forming and bond-cutting catalysts. The degree to which hydrolysis preferentially affects bonds in less efficient, and therefore less well

Self-Organization and the Self-Assembling Process in Tissue Engineering

Science.gov (United States)

Eswaramoorthy, Rajalakshmanan; Hadidi, Pasha; Hu, Jerry C.

2015-01-01

In recent years, the tissue engineering paradigm has shifted to include a new and growing subfield of scaffoldless techniques which generate self-organizing and self-assembling tissues. This review aims to provide a cogent description of this relatively new research area, with special emphasis on applications toward clinical use and research models. Particular emphasis is placed on providing clear definitions of self-organization and the self-assembling process, as delineated from other scaffoldless techniques in tissue engineering and regenerative medicine. Significantly, during formation, self-organizing and self-assembling tissues display biological processes similar to those that occur in vivo. These help lead to the recapitulation of native tissue morphological structure and organization. Notably, functional properties of these tissues also approach native tissue values; some of these engineered tissues are already in clinical trials. This review aims to provide a cohesive summary of work in this field, and to highlight the potential of self-organization and the self-assembling process to provide cogent solutions to current intractable problems in tissue engineering. PMID:23701238

Self-organized Learning Environments

DEFF Research Database (Denmark)

Dalsgaard, Christian; Mathiasen, Helle

2007-01-01

system actively. The two groups used the system in their own way to support their specific activities and ways of working. The paper concludes that self-organized learning environments can strengthen the development of students’ academic as well as social qualifications. Further, the paper identifies......The purpose of the paper is to discuss the potentials of using a conference system in support of a project based university course. We use the concept of a self-organized learning environment to describe the shape of the course. In the paper we argue that educational technology, such as conference...... systems, has a potential to support students’ development of self-organized learning environments and facilitate self-governed activities in higher education. The paper is based on an empirical study of two project groups’ use of a conference system. The study showed that the students used the conference...

PREFACE: Self-organized nanostructures

Science.gov (United States)

Rousset, Sylvie; Ortega, Enrique

2006-04-01

In order to fabricate ordered arrays of nanostructures, two different strategies might be considered. The `top-down' approach consists of pushing the limit of lithography techniques down to the nanometre scale. However, beyond 10 nm lithography techniques will inevitably face major intrinsic limitations. An alternative method for elaborating ultimate-size nanostructures is based on the reverse `bottom-up' approach, i.e. building up nanostructures (and eventually assemble them to form functional circuits) from individual atoms or molecules. Scanning probe microscopies, including scanning tunnelling microscopy (STM) invented in 1982, have made it possible to create (and visualize) individual structures atom by atom. However, such individual atomic manipulation is not suitable for industrial applications. Self-assembly or self-organization of nanostructures on solid surfaces is a bottom-up approach that allows one to fabricate and assemble nanostructure arrays in a one-step process. For applications, such as high density magnetic storage, self-assembly appears to be the simplest alternative to lithography for massive, parallel fabrication of nanostructure arrays with regular sizes and spacings. These are also necessary for investigating the physical properties of individual nanostructures by means of averaging techniques, i.e. all those using light or particle beams. The state-of-the-art and the current developments in the field of self-organization and physical properties of assembled nanostructures are reviewed in this issue of Journal of Physics: Condensed Matter. The papers have been selected from among the invited and oral presentations of the recent summer workshop held in Cargese (Corsica, France, 17-23 July 2005). All authors are world-renowned in the field. The workshop has been funded by the Marie Curie Actions: Marie Curie Conferences and Training Courses series named `NanosciencesTech' supported by the VI Framework Programme of the European Community, by

Psychopathic traits linked to alterations in neural activity during personality judgments of self and others

Directory of Open Access Journals (Sweden)

Philip Deming

Full Text Available Psychopathic individuals are notorious for their grandiose sense of self-worth and disregard for the welfare of others. One potential psychological mechanism underlying these traits is the relative consideration of “self” versus “others”. Here we used task-based functional magnetic resonance imaging (fMRI to identify neural responses during personality trait judgments about oneself and a familiar other in a sample of adult male incarcerated offenders (n = 57. Neural activity was regressed on two clusters of psychopathic traits: Factor 1 (e.g., egocentricity and lack of empathy and Factor 2 (e.g., impulsivity and irresponsibility. Contrary to our hypotheses, Factor 1 scores were not significantly related to neural activity during self- or other-judgments. However, Factor 2 traits were associated with diminished activation to self-judgments, in relation to other-judgments, in bilateral posterior cingulate cortex and right temporoparietal junction. These findings highlight cortical regions associated with a dimension of social-affective cognition that may underlie psychopathic individuals' impulsive traits. Keywords: Psychopathy, fMRI, Social cognition, Self-referential processing, Emotion, Psychopathology

Self-organization and forcing templates in coastal barrier response to storms

Science.gov (United States)

Lazarus, E.

2015-12-01

When a storm event pushes water up and over a coastal barrier, cross-shore flow transports sediment from the barrier face to the back-barrier environment. This natural physical process is called "overwash", and "washover" is the sedimentary deposit it forms. Overwash and washover support critical coastal habitats, and enable barriers to maintain their height and width relative to rising sea level. On developed barrier coasts, overwash constitutes a natural hazard, which sea-level rise will exacerbate. Overwash is also a prerequisite for barrier breaching and coastal flooding. Predicting occurrence and characteristics of overwash and washover has significant societal value. Hazard models typically assume that pre-storm barrier morphology determines how the barrier changes during a storm. However, classic work has documented the absence of a relationship between pre/post-storm topography in some cases, and has also identified rhythmic patterns in washover alongshore. Previous explanations for these spatial patterns have looked to forcing templates, forms that get imprinted in the barrier shape. An alternative explanation is that washover patterns self-organize, emerging from feedbacks between water flow and sediment transport. Self-organization and forcing templates are often framed as mutually exclusive, but patterns likely form across a continuum of conditions. Here, I use data from a new physical experiment to suggest that spatial patterns in washover can self-organize within the limit of a forcing template of some critical "strength", beyond which pre/post-storm morphologies are highly correlated. Quantifying spatial patterns in washover deposits opens exciting questions regarding coastal morphodynamic response to storms. Measurement of relative template strength over extended spatial (and temporal) scales has the potential to improve hazard assessment and prediction, particularly where template strength is low and self-organization dominates barrier change.

Self-Organization in Embedded Real-Time Systems

CERN Document Server

Brinkschulte, Uwe; Rettberg, Achim

2013-01-01

This book describes the emerging field of self-organizing, multicore, distributed and real-time embedded systems.  Self-organization of both hardware and software can be a key technique to handle the growing complexity of modern computing systems. Distributed systems running hundreds of tasks on dozens of processors, each equipped with multiple cores, requires self-organization principles to ensure efficient and reliable operation. This book addresses various, so-called Self-X features such as self-configuration, self-optimization, self-adaptation, self-healing and self-protection. Presents open components for embedded real-time adaptive and self-organizing applications; Describes innovative techniques in: scheduling, memory management, quality of service, communications supporting organic real-time applications; Covers multi-/many-core embedded systems supporting real-time adaptive systems and power-aware, adaptive hardware and software systems; Includes case studies of open embedded real-time self-organizi...

Self-organization analysis for a nonlocal convective Fisher equation

Energy Technology Data Exchange (ETDEWEB)

Cunha, J.A.R. da [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil); Penna, A.L.A. [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil)], E-mail: penna.andre@gmail.com; Vainstein, M.H. [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil); Morgado, R. [International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil); Departamento de Matematica, Universidade de Brasilia, 70910-900 Brasilia DF (Brazil); Oliveira, F.A. [Instituto de Fisica, Universidade de Brasilia, 70919-970 Brasilia DF (Brazil); International Center for Condensed Matter Physics, CP 04513, 70919-970 Brasilia DF (Brazil)

2009-02-02

Using both an analytical method and a numerical approach we have investigated pattern formation for a nonlocal convective Fisher equation with constant and spatial velocity fields. We analyze the limits of the influence function due to nonlocal interaction and we obtain the phase diagram of critical velocities v{sub c} as function of the width {mu} of the influence function, which characterize the self-organization of a finite system.

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Self-organization of domain growth in the Ising model with impurities

DEFF Research Database (Denmark)

Andersen, Jørgen Vitting; Mouritsen, Ole G.

1992-01-01

We have studied avalanchelike rearrangements of domain patterns in the two-dimensional Ising model with static impurities, which is quenched to low temperatures. When breaking the up-down symmetry of the spins by a small applied field, the mere fluctuation of a single spin eventually results...... in a cascade of spin flips at the domain boundaries. We have analyzed the lifetime and size distribution functions for the avalanches and related the results to the general phenomena of self-organized criticality and to recent experiments on cellular magnetic domain patterns in magnetic garnet films. Our...... results suggest that the self-organized state in this system appears to be subcritical, in agreement with a recent theory....

Neural networks for perception human and machine perception

CERN Document Server

Wechsler, Harry

1991-01-01

Neural Networks for Perception, Volume 1: Human and Machine Perception focuses on models for understanding human perception in terms of distributed computation and examples of PDP models for machine perception. This book addresses both theoretical and practical issues related to the feasibility of both explaining human perception and implementing machine perception in terms of neural network models. The book is organized into two parts. The first part focuses on human perception. Topics on network model ofobject recognition in human vision, the self-organization of functional architecture in t

A Model of Self-Organizing Head-Centered Visual Responses in Primate Parietal Areas

Science.gov (United States)

Mender, Bedeho M. W.; Stringer, Simon M.

2013-01-01

We present a hypothesis for how head-centered visual representations in primate parietal areas could self-organize through visually-guided learning, and test this hypothesis using a neural network model. The model consists of a competitive output layer of neurons that receives afferent synaptic connections from a population of input neurons with eye position gain modulated retinal receptive fields. The synaptic connections in the model are trained with an associative trace learning rule which has the effect of encouraging output neurons to learn to respond to subsets of input patterns that tend to occur close together in time. This network architecture and synaptic learning rule is hypothesized to promote the development of head-centered output neurons during periods of time when the head remains fixed while the eyes move. This hypothesis is demonstrated to be feasible, and each of the core model components described is tested and found to be individually necessary for successful self-organization. PMID:24349064

Self-reported empathy and neural activity during action imitation and observation in schizophrenia

OpenAIRE

Horan, William P.; Iacoboni, Marco; Cross, Katy A.; Korb, Alex; Lee, Junghee; Nori, Poorang; Quintana, Javier; Wynn, Jonathan K.; Green, Michael F.

2014-01-01

Introduction: Although social cognitive impairments are key determinants of functional outcome in schizophrenia their neural bases are poorly understood. This study investigated neural activity during imitation and observation of finger movements and facial expressions in schizophrenia, and their correlates with self-reported empathy. Methods: 23 schizophrenia outpatients and 23 healthy controls were studied with functional magnetic resonance imaging (fMRI) while they imitated, executed, o...

Individual and collective processes in the construction of the self: self-enhancement in the United States and self-criticism in Japan.

Science.gov (United States)

Kitayama, S; Markus, H R; Matsumoto, H; Norasakkunkit, V

1997-06-01

A collective constructionist theory of the self proposes that many psychological processes, including enhancement of the self (pervasive in the United States) and criticism and subsequent improvement of the self (widespread in Japan), result from and support the very ways in which social acts and situations are collectively defined and subjectively experienced in the respective cultural contexts. In support of the theory, 2 studies showed, first, that American situations are relatively conducive to self-enhancement and American people are relatively likely to engage in self-enhancement and, second, that Japanese situations are relatively conducive to self-criticism and Japanese people are relatively likely to engage in self-criticism. Implications are discussed for the collective construction of psychological processes implicated in the self and, more generally, for the mutual constitution of culture and the self.

Challenging emotional prejudice by changing self-concept: priming independent self-construal reduces racial in-group bias in neural responses to other's pain.

Science.gov (United States)

Wang, Chenbo; Wu, Bing; Liu, Yi; Wu, Xinhuai; Han, Shihui

2015-09-01

Humans show stronger empathy for in-group compared with out-group members' suffering and help in-group members more than out-group members. Moreover, the in-group bias in empathy and parochial altruism tend to be more salient in collectivistic than individualistic cultures. This work tested the hypothesis that modifying self-construals, which differentiate between collectivistic and individualistic cultural orientations, affects in-group bias in empathy for perceived own-race vs other-race pain. By scanning adults using functional magnetic resonance imaging, we found stronger neural activities in the mid-cingulate, left insula and supplementary motor area (SMA) in response to racial in-group compared with out-group members' pain after participants had been primed with interdependent self-construals. However, the racial in-group bias in neural responses to others' pain in the left SMA, mid-cingulate cortex and insula was significantly reduced by priming independent self-construals. Our findings suggest that shifting an individual's self-construal leads to changes of his/her racial in-group bias in neural responses to others' suffering. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

[Neural basis of self-face recognition: social aspects].

Science.gov (United States)

Sugiura, Motoaki

2012-07-01

Considering the importance of the face in social survival and evidence from evolutionary psychology of visual self-recognition, it is reasonable that we expect neural mechanisms for higher social-cognitive processes to underlie self-face recognition. A decade of neuroimaging studies so far has, however, not provided an encouraging finding in this respect. Self-face specific activation has typically been reported in the areas for sensory-motor integration in the right lateral cortices. This observation appears to reflect the physical nature of the self-face which representation is developed via the detection of contingency between one's own action and sensory feedback. We have recently revealed that the medial prefrontal cortex, implicated in socially nuanced self-referential process, is activated during self-face recognition under a rich social context where multiple other faces are available for reference. The posterior cingulate cortex has also exhibited this activation modulation, and in the separate experiment showed a response to attractively manipulated self-face suggesting its relevance to positive self-value. Furthermore, the regions in the right lateral cortices typically showing self-face-specific activation have responded also to the face of one's close friend under the rich social context. This observation is potentially explained by the fact that the contingency detection for physical self-recognition also plays a role in physical social interaction, which characterizes the representation of personally familiar people. These findings demonstrate that neuroscientific exploration reveals multiple facets of the relationship between self-face recognition and social-cognitive process, and that technically the manipulation of social context is key to its success.

Neural processes underlying cultural differences in cognitive persistence.

Science.gov (United States)

Telzer, Eva H; Qu, Yang; Lin, Lynda C

2017-08-01

Self-improvement motivation, which occurs when individuals seek to improve upon their competence by gaining new knowledge and improving upon their skills, is critical for cognitive, social, and educational adjustment. While many studies have delineated the neural mechanisms supporting extrinsic motivation induced by monetary rewards, less work has examined the neural processes that support intrinsically motivated behaviors, such as self-improvement motivation. Because cultural groups traditionally vary in terms of their self-improvement motivation, we examined cultural differences in the behavioral and neural processes underlying motivated behaviors during cognitive persistence in the absence of extrinsic rewards. In Study 1, 71 American (47 females, M=19.68 years) and 68 Chinese (38 females, M=19.37 years) students completed a behavioral cognitive control task that required cognitive persistence across time. In Study 2, 14 American and 15 Chinese students completed the same cognitive persistence task during an fMRI scan. Across both studies, American students showed significant declines in cognitive performance across time, whereas Chinese participants demonstrated effective cognitive persistence. These behavioral effects were explained by cultural differences in self-improvement motivation and paralleled by increasing activation and functional coupling between the inferior frontal gyrus (IFG) and ventral striatum (VS) across the task among Chinese participants, neural activation and coupling that remained low in American participants. These findings suggest a potential neural mechanism by which the VS and IFG work in concert to promote cognitive persistence in the absence of extrinsic rewards. Thus, frontostriatal circuitry may be a neurobiological signal representing intrinsic motivation for self-improvement that serves an adaptive function, increasing Chinese students' motivation to engage in cognitive persistence. Copyright © 2017 Elsevier Inc. All rights

Classification and source determination of medium petroleum distillates by chemometric and artificial neural networks: a self organizing feature approach.

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Mat-Desa, Wan N S; Ismail, Dzulkiflee; NicDaeid, Niamh

2011-10-15

Three different medium petroleum distillate (MPD) products (white spirit, paint brush cleaner, and lamp oil) were purchased from commercial stores in Glasgow, Scotland. Samples of 10, 25, 50, 75, 90, and 95% evaporated product were prepared, resulting in 56 samples in total which were analyzed using gas chromatography-mass spectrometry. Data sets from the chromatographic patterns were examined and preprocessed for unsupervised multivariate analyses using principal component analysis (PCA), hierarchical cluster analysis (HCA), and a self organizing feature map (SOFM) artificial neural network. It was revealed that data sets comprised of higher boiling point hydrocarbon compounds provided a good means for the classification of the samples and successfully linked highly weathered samples back to their unevaporated counterpart in every case. The classification abilities of SOFM were further tested and validated for their predictive abilities where one set of weather data in each case was withdrawn from the sample set and used as a test set of the retrained network. This revealed SOFM to be an outstanding mechanism for sample discrimination and linkage over the more conventional PCA and HCA methods often suggested for such data analysis. SOFM also has the advantage of providing additional information through the evaluation of component planes facilitating the investigation of underlying variables that account for the classification. © 2011 American Chemical Society

Self-organizing map classifier for stressed speech recognition

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Partila, Pavol; Tovarek, Jaromir; Voznak, Miroslav

2016-05-01

This paper presents a method for detecting speech under stress using Self-Organizing Maps. Most people who are exposed to stressful situations can not adequately respond to stimuli. Army, police, and fire department occupy the largest part of the environment that are typical of an increased number of stressful situations. The role of men in action is controlled by the control center. Control commands should be adapted to the psychological state of a man in action. It is known that the psychological changes of the human body are also reflected physiologically, which consequently means the stress effected speech. Therefore, it is clear that the speech stress recognizing system is required in the security forces. One of the possible classifiers, which are popular for its flexibility, is a self-organizing map. It is one type of the artificial neural networks. Flexibility means independence classifier on the character of the input data. This feature is suitable for speech processing. Human Stress can be seen as a kind of emotional state. Mel-frequency cepstral coefficients, LPC coefficients, and prosody features were selected for input data. These coefficients were selected for their sensitivity to emotional changes. The calculation of the parameters was performed on speech recordings, which can be divided into two classes, namely the stress state recordings and normal state recordings. The benefit of the experiment is a method using SOM classifier for stress speech detection. Results showed the advantage of this method, which is input data flexibility.

Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules.

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Zhang, Zhen; Ma, Cheng; Zhu, Rong

2016-10-14

High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial-temporal complexity. This paper presents a multi-input multi-output (MIMO) self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional-integral-derivative (PID) neural network (FCPIDNN) and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

Self-Tuning Fully-Connected PID Neural Network System for Distributed Temperature Sensing and Control of Instrument with Multi-Modules

Directory of Open Access Journals (Sweden)

Zhen Zhang

2016-10-01

Full Text Available High integration of multi-functional instruments raises a critical issue in temperature control that is challenging due to its spatial–temporal complexity. This paper presents a multi-input multi-output (MIMO self-tuning temperature sensing and control system for efficiently modulating the temperature environment within a multi-module instrument. The smart system ensures that the internal temperature of the instrument converges to a target without the need of a system model, thus making the control robust. The system consists of a fully-connected proportional–integral–derivative (PID neural network (FCPIDNN and an on-line self-tuning module. The experimental results show that the presented system can effectively control the internal temperature under various mission scenarios, in particular, it is able to self-reconfigure upon actuator failure. The system provides a new scheme for a complex and time-variant MIMO control system which can be widely applied for the distributed measurement and control of the environment in instruments, integration electronics, and house constructions.

NEURAL NETWORKS CONTROL OF THE HYBRID POWER UNIT BASED ON THE METHOD OF ADAPTIVE CRITICS

Directory of Open Access Journals (Sweden)

S. Serikov

2012-01-01

Full Text Available The formal statement of the optimization problem of hybrid vehicle power unit control is given. Its solving by neural networks method application on the basis of adaptive critic is considered.

Embodying compassion: a virtual reality paradigm for overcoming excessive self-criticism.

Science.gov (United States)

Falconer, Caroline J; Slater, Mel; Rovira, Aitor; King, John A; Gilbert, Paul; Antley, Angus; Brewin, Chris R

2014-01-01

Virtual reality has been successfully used to study and treat psychological disorders such as phobias and posttraumatic stress disorder but has rarely been applied to clinically-relevant emotions other than fear and anxiety. Self-criticism is a ubiquitous feature of psychopathology and can be treated by increasing levels of self-compassion. We exploited the known effects of identification with a virtual body to arrange for healthy female volunteers high in self-criticism to experience self-compassion from an embodied first-person perspective within immersive virtual reality. Whereas observation and practice of compassionate responses reduced self-criticism, the additional experience of embodiment also increased self-compassion and feelings of being safe. The results suggest potential new uses for immersive virtual reality in a range of clinical conditions.

Organization of Anti-Phase Synchronization Pattern in Neural Networks: What are the Key Factors?

Science.gov (United States)

Li, Dong; Zhou, Changsong

2011-01-01

Anti-phase oscillation has been widely observed in cortical neural network. Elucidating the mechanism underlying the organization of anti-phase pattern is of significance for better understanding more complicated pattern formations in brain networks. In dynamical systems theory, the organization of anti-phase oscillation pattern has usually been considered to relate to time delay in coupling. This is consistent to conduction delays in real neural networks in the brain due to finite propagation velocity of action potentials. However, other structural factors in cortical neural network, such as modular organization (connection density) and the coupling types (excitatory or inhibitory), could also play an important role. In this work, we investigate the anti-phase oscillation pattern organized on a two-module network of either neuronal cell model or neural mass model, and analyze the impact of the conduction delay times, the connection densities, and coupling types. Our results show that delay times and coupling types can play key roles in this organization. The connection densities may have an influence on the stability if an anti-phase pattern exists due to the other factors. Furthermore, we show that anti-phase synchronization of slow oscillations can be achieved with small delay times if there is interaction between slow and fast oscillations. These results are significant for further understanding more realistic spatiotemporal dynamics of cortico-cortical communications. PMID:22232576

Anomaly Detection for Resilient Control Systems Using Fuzzy-Neural Data Fusion Engine

Energy Technology Data Exchange (ETDEWEB)

Ondrej Linda; Milos Manic; Timothy R. McJunkin

2011-08-01

Resilient control systems in critical infrastructures require increased cyber-security and state-awareness. One of the necessary conditions for achieving the desired high level of resiliency is timely reporting and understanding of the status and behavioral trends of the control system. This paper describes the design and development of a neural-network based data-fusion system for increased state-awareness of resilient control systems. The proposed system consists of a dedicated data-fusion engine for each component of the control system. Each data-fusion engine implements three-layered alarm system consisting of: (1) conventional threshold-based alarms, (2) anomalous behavior detector using self-organizing maps, and (3) prediction error based alarms using neural network based signal forecasting. The proposed system was integrated with a model of the Idaho National Laboratory Hytest facility, which is a testing facility for hybrid energy systems. Experimental results demonstrate that the implemented data fusion system provides timely plant performance monitoring and cyber-state reporting.

Requirement of mouse BCCIP for neural development and progenitor proliferation.

Directory of Open Access Journals (Sweden)

Yi-Yuan Huang

Full Text Available Multiple DNA repair pathways are involved in the orderly development of neural systems at distinct stages. The homologous recombination (HR pathway is required to resolve stalled replication forks and critical for the proliferation of progenitor cells during neural development. BCCIP is a BRCA2 and CDKN1A interacting protein implicated in HR and inhibition of DNA replication stress. In this study, we determined the role of BCCIP in neural development using a conditional BCCIP knock-down mouse model. BCCIP deficiency impaired embryonic and postnatal neural development, causing severe ataxia, cerebral and cerebellar defects, and microcephaly. These development defects are associated with spontaneous DNA damage and subsequent cell death in the proliferative cell populations of the neural system during embryogenesis. With in vitro neural spheroid cultures, BCCIP deficiency impaired neural progenitor's self-renewal capability, and spontaneously activated p53. These data suggest that BCCIP and its anti-replication stress functions are essential for normal neural development by maintaining an orderly proliferation of neural progenitors.

Neural and computational processes underlying dynamic changes in self-esteem

Science.gov (United States)

Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

2017-01-01

Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-esteem resulting from these errors co-varied with activity in ventromedial prefrontal cortex (vmPFC). We linked computational parameters to psychiatric symptoms using canonical correlation analysis to identify an ‘interpersonal vulnerability’ dimension. Vulnerability modulated the expression of prediction error responses in anterior insula and insula-vmPFC connectivity during self-esteem updates. Our findings indicate that updating of self-evaluative beliefs relies on learning mechanisms akin to those used in learning about others. Enhanced insula-vmPFC connectivity during updating of those beliefs may represent a marker for psychiatric vulnerability. PMID:29061228

Neural and computational processes underlying dynamic changes in self-esteem.

Science.gov (United States)

Will, Geert-Jan; Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

2017-10-24

Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-esteem resulting from these errors co-varied with activity in ventromedial prefrontal cortex (vmPFC). We linked computational parameters to psychiatric symptoms using canonical correlation analysis to identify an 'interpersonal vulnerability' dimension. Vulnerability modulated the expression of prediction error responses in anterior insula and insula-vmPFC connectivity during self-esteem updates. Our findings indicate that updating of self-evaluative beliefs relies on learning mechanisms akin to those used in learning about others. Enhanced insula-vmPFC connectivity during updating of those beliefs may represent a marker for psychiatric vulnerability.

High School Students' Critical Thinking Related to Their Metacognitive Self-Regulation and Physics Self-Efficacy Beliefs

Science.gov (United States)

Gurcay, Deniz; Ferah, Hatice Ozturk

2018-01-01

The purpose of this study is to examine the relationships between ninth grade students' metacognitive self-regulation skills and physics self-efficacy beliefs and their critical thinking. To this end, 162 students attending the ninth grade participated in the study. Critical thinking scale, metacognitive self-regulation scale and physics…

Body Image, Self-Esteem, and Weight-Related Criticism from Romantic Partners.

Science.gov (United States)

Befort, Christie; Kurpius, Sharon E. Robinson; Hull-Blanks, Elva; Nicpon, Megan Foley; Huser, Laura; Sollenberger, Sonja

2001-01-01

Examines weight-related criticism from romantic partners and the importance of the romantic relationship in relation to the body image and self esteem for college freshmen women. Results reveal that self esteem and body image were positively related. Partner importance also predicted self esteem, whereas criticism did not. (Contains 55 references…

The concept of self-organizing systems. Why bother?

Science.gov (United States)

Elverfeldt, Kirsten v.; Embleton-Hamann, Christine; Slaymaker, Olav

2016-04-01

Complexity theory and the concept of self-organizing systems provide a rather challenging conceptual framework for explaining earth systems change. Self-organization - understood as the aggregate processes internal to an environmental system that lead to a distinctive spatial or temporal organization - reduces the possibility of implicating a specific process as being causal, and it poses some restrictions on the idea that external drivers cause a system to change. The concept of self-organizing systems suggests that many phenomena result from an orchestration of different mechanisms, so that no causal role can be assigned to an individual factor or process. The idea that system change can be due to system-internal processes of self-organization thus proves a huge challenge to earth system research, especially in the context of global environmental change. In order to understand the concept's implications for the Earth Sciences, we need to know the characteristics of self-organizing systems and how to discern self-organizing systems. Within the talk, we aim firstly at characterizing self-organizing systems, and secondly at highlighting the advantages and difficulties of the concept within earth system sciences. The presentation concludes that: - The concept of self-organizing systems proves especially fruitful for small-scale earth surface systems. Beach cusps and patterned ground are only two of several other prime examples of self-organizing earth surface systems. They display characteristics of self-organization like (i) system-wide order from local interactions, (ii) symmetry breaking, (iii) distributed control, (iv) robustness and resilience, (v) nonlinearity and feedbacks, (vi) organizational closure, (vii) adaptation, and (viii) variation and selection. - It is comparatively easy to discern self-organization in small-scale systems, but to adapt the concept to larger scale systems relevant to global environmental change research is more difficult: Self-organizing

Critical thinking as a self-regulatory process component in teaching and learning.

Science.gov (United States)

Phan, Huy P

2010-05-01

This article presents a theoretically grounded model of critical thinking and self-regulation in the context of teaching and learning. Critical thinking, deriving from an educational psychology perspective is a complex process of reflection that helps individuals become more analytical in their thinking and professional development. My conceptualisation in this discussion paper argues that both theoretical orientations (critical thinking and self-regulation) operate in a dynamic interactive system of teaching and learning. My argument, based on existing research evidence, suggests two important points: (i) critical thinking acts as another cognitive strategy of self-regulation that learners use in their learning, and (ii) critical thinking may be a product of various antecedents such as different self-regulatory strategies.

Embodying compassion: a virtual reality paradigm for overcoming excessive self-criticism.

Directory of Open Access Journals (Sweden)

Caroline J Falconer

Full Text Available Virtual reality has been successfully used to study and treat psychological disorders such as phobias and posttraumatic stress disorder but has rarely been applied to clinically-relevant emotions other than fear and anxiety. Self-criticism is a ubiquitous feature of psychopathology and can be treated by increasing levels of self-compassion. We exploited the known effects of identification with a virtual body to arrange for healthy female volunteers high in self-criticism to experience self-compassion from an embodied first-person perspective within immersive virtual reality. Whereas observation and practice of compassionate responses reduced self-criticism, the additional experience of embodiment also increased self-compassion and feelings of being safe. The results suggest potential new uses for immersive virtual reality in a range of clinical conditions.

Layers and Multilayers of Self-Assembled Polymers: Tunable Engineered Extracellular Matrix Coatings for Neural Cell Growth.

Science.gov (United States)

Landry, Michael J; Rollet, Frédéric-Guillaume; Kennedy, Timothy E; Barrett, Christopher J

2018-03-12

Growing primary cells and tissue in long-term cultures, such as primary neural cell culture, presents many challenges. A critical component of any environment that supports neural cell growth in vivo is an appropriate 2-D surface or 3-D scaffold, typically in the form of a thin polymer layer that coats an underlying plastic or glass substrate and aims to mimic critical aspects of the extracellular matrix. A fundamental challenge to mimicking a hydrophilic, soft natural cell environment is that materials with these properties are typically fragile and are difficult to adhere to and stabilize on an underlying plastic or glass cell culture substrate. In this review, we highlight the current state of the art and overview recent developments of new artificial extracellular matrix (ECM) surfaces for in vitro neural cell culture. Notably, these materials aim to strike a balance between being hydrophilic and soft while also being thick, stable, robust, and bound well to the underlying surface to provide an effective surface to support long-term cell growth. We focus on improved surface and scaffold coating systems that can mimic the natural physicochemical properties that enhance neuronal survival and growth, applied as soft hydrophilic polymer coatings for both in vitro cell culture and for implantable neural probes and 3-D matrixes that aim to enhance stability and longevity to promote neural biocompatibility in vivo. With respect to future developments, we outline four emerging principles that serve to guide the development of polymer assemblies that function well as artificial ECMs: (a) design inspired by biological systems and (b) the employment of principles of aqueous soft bonding and self-assembly to achieve (c) a high-water-content gel-like coating that is stable over time in a biological environment and possesses (d) a low modulus to more closely mimic soft, compliant real biological tissue. We then highlight two emerging classes of thick material coatings that

Artificial neural network study on organ-targeting peptides

Science.gov (United States)

Jung, Eunkyoung; Kim, Junhyoung; Choi, Seung-Hoon; Kim, Minkyoung; Rhee, Hokyoung; Shin, Jae-Min; Choi, Kihang; Kang, Sang-Kee; Lee, Nam Kyung; Choi, Yun-Jaie; Jung, Dong Hyun

2010-01-01

We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.

Clustering Multiple Sclerosis Subgroups with Multifractal Methods and Self-Organizing Map Algorithm

Science.gov (United States)

Karaca, Yeliz; Cattani, Carlo

Magnetic resonance imaging (MRI) is the most sensitive method to detect chronic nervous system diseases such as multiple sclerosis (MS). In this paper, Brownian motion Hölder regularity functions (polynomial, periodic (sine), exponential) for 2D image, such as multifractal methods were applied to MR brain images, aiming to easily identify distressed regions, in MS patients. With these regions, we have proposed an MS classification based on the multifractal method by using the Self-Organizing Map (SOM) algorithm. Thus, we obtained a cluster analysis by identifying pixels from distressed regions in MR images through multifractal methods and by diagnosing subgroups of MS patients through artificial neural networks.

Chaos, self-organized criticality, and SETAR nonlinearity: An analysis of purchasing power parity between Canada and the United States

Energy Technology Data Exchange (ETDEWEB)

Serletis, Apostolos [Department of Economics, University of Calgary, Calgary, Alta., T2N 1N4 (Canada)]. E-mail: Serletis@ucalgary.ca; Shahmoradi, Asghar [Faculty of Economics, University of Tehran, Tehran (Iran, Islamic Republic of)

2007-08-15

This paper uses monthly observations for the real exchange rate between Canada and the United States over the recent flexible exchange rate period (from January 1, 1973 to August 1, 2004) to test purchasing power parity between Canada and the United States using unit root and stationarity tests. Moreover, given the apparent random walk behavior in the real exchange rate, various tests from dynamical systems theory, such as for example, the Nychka et al. [Nychka DW, Ellner S, Ronald GA, McCaffrey D. Finding chaos in noisy systems. J Roy Stat Soc B 1992;54:399-426] chaos test, the Li [Li W. Absence of 1/f spectra in Dow Jones average. Int J Bifurcat Chaos 1991;1:583-97] self-organized criticality test, and the Hansen [Hansen, B.E. Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 1996;64:413-30] threshold effects test are used to distinguish between stochastic and deterministic origin for the real exchange rate.

Information theory explanation of the fluctuation theorem, maximum entropy production and self-organized criticality in non-equilibrium stationary states

CERN Document Server

Dewar, R

2003-01-01

Jaynes' information theory formalism of statistical mechanics is applied to the stationary states of open, non-equilibrium systems. First, it is shown that the probability distribution p subGAMMA of the underlying microscopic phase space trajectories GAMMA over a time interval of length tau satisfies p subGAMMA propor to exp(tau sigma subGAMMA/2k sub B) where sigma subGAMMA is the time-averaged rate of entropy production of GAMMA. Three consequences of this result are then derived: (1) the fluctuation theorem, which describes the exponentially declining probability of deviations from the second law of thermodynamics as tau -> infinity; (2) the selection principle of maximum entropy production for non-equilibrium stationary states, empirical support for which has been found in studies of phenomena as diverse as the Earth's climate and crystal growth morphology; and (3) the emergence of self-organized criticality for flux-driven systems in the slowly-driven limit. The explanation of these results on general inf...

Chaos, self-organized criticality, and SETAR nonlinearity: An analysis of purchasing power parity between Canada and the United States

International Nuclear Information System (INIS)

Serletis, Apostolos; Shahmoradi, Asghar

2007-01-01

This paper uses monthly observations for the real exchange rate between Canada and the United States over the recent flexible exchange rate period (from January 1, 1973 to August 1, 2004) to test purchasing power parity between Canada and the United States using unit root and stationarity tests. Moreover, given the apparent random walk behavior in the real exchange rate, various tests from dynamical systems theory, such as for example, the Nychka et al. [Nychka DW, Ellner S, Ronald GA, McCaffrey D. Finding chaos in noisy systems. J Roy Stat Soc B 1992;54:399-426] chaos test, the Li [Li W. Absence of 1/f spectra in Dow Jones average. Int J Bifurcat Chaos 1991;1:583-97] self-organized criticality test, and the Hansen [Hansen, B.E. Inference when a nuisance parameter is not identified under the null hypothesis. Econometrica 1996;64:413-30] threshold effects test are used to distinguish between stochastic and deterministic origin for the real exchange rate

Self-Organization in Integrated Conservation and Development Initiatives

Directory of Open Access Journals (Sweden)

Cristiana Simão Seixas

2007-11-01

Full Text Available This paper uses a cooking metaphor to explore key elements (i.e., ingredients for a great meal that contribute to self-organization processes in the context of successful community-based conservation (CBC or integrated conservation and development projects (ICDP. We pose two major questions: (1 What are the key factors that drive peoples' and/or organizations' willingness to take responsibilities and to act? (2 What contributes to community self-organization? In other words, how conservation-development projects originate, evolve, survive or disappear? In order to address these questions we examine trigger events and catalytic elements in several cases among the Equator Prize finalists and short-listed nominees, from both the 2002 and 2004 awards. The Prize recognizes efforts in integrating biodiversity conservation and poverty reduction. We use secondary data in our analysis, including data from several technical reports and scientific papers written about the Equator Prize finalists and short-listed nominees. We observed common ingredients in most projects including: (1 involvement and commitment of key players (including communities, (2 funding, (3 strong leadership, (4 capacity building, (5 partnership with supportive organizations and government, and (6 economic incentives (including alternative livelihood options. We also observed that CBC and ICDP initiatives opportunistically evolve in a multi-level world, in which local communities establish linkages with people and organizations at different political levels, across different geographical scales and for different purposes. We conclude that there is no right 'recipe' to promote community self-organization but often a mix of some of these six ingredients need to come together for 'success' and that one or two ingredients are not sufficient to ensure success. Also the existence of these six ingredients does not guarantee a great meal - the 'chef's' creativity also is critical. That is

Classification and prediction of the critical heat flux using fuzzy theory and artificial neural networks

International Nuclear Information System (INIS)

Moon, Sang Ki; Chang, Soon Heung

1994-01-01

A new method to predict the critical heat flux (CHF) is proposed, based on the fuzzy clustering and artificial neural network. The fuzzy clustering classifies the experimental CHF data into a few data clusters (data groups) according to the data characteristics. After classification of the experimental data, the characteristics of the resulting clusters are discussed with emphasis on the distribution of the experimental conditions and physical mechanism. The CHF data in each group are trained in an artificial neural network to predict the CHF. The artificial neural network adjusts the weight so as to minimize the prediction error within the corresponding cluster. Application of the proposed method to the KAIST CHF data bank shows good prediction capability of the CHF, better than other existing methods. ((orig.))

Self-Organized Percolation and Critical Sales Fluctuations

Science.gov (United States)

Weisbuch, Gérard; Solomon, Sorin

There is a discrepancy between the standard view of equilibrium through price adjustment in economics and the observation of large fluctuations in stock markets. We study here a simple model where agents decisions not only depend upon their individual preferences but also upon information obtained from their neighbors in a social network. The model shows that information diffusion coupled to the adjustment process drives the system to criticality with large fluctuations rather than converging smoothly to equilibrium.

Investigation of self-organized criticality behavior of edge plasma transport in Torus experiment of technology oriented research

International Nuclear Information System (INIS)

Xu, Y.H.; Jachmich, S.; Weynants, R.R.; Huber, A.; Unterberg, B.; Samm, U.

2004-01-01

The self-organized criticality (SOC) behavior of the edge plasma transport has been studied using fluctuation data measured in the plasma edge and the scrape-off layer of Torus experiment of technology oriented research tokamak [H. Soltwisch et al., Plasma Phys. Controlled Fusion 26, 23 (1984)] before and during the edge biasing experiments. In the 'nonshear' discharge phase before biasing, the fluctuation data clearly show some of the characteristics associated with SOC, including similar frequency spectra to those obtained in 'sandpile' transport and other SOC systems, slowly decaying long tails in the autocorrelation function, values of Hurst parameters larger than 0.5 at all the detected radial locations, and a radial propagation of avalanchelike events in the edge plasma area. During the edge biasing phase, with the generation of an edge radial electric field E r and thus of E r xB flow shear, contrary to theoretical expectation, the Hurst parameters are substantially enhanced in the negative flow shear region and in the scrape-off layer as well. Concomitantly, it is found that the local turbulence is well decorrelated by the E r xB velocity shear, consistent with theoretical predictions

Prerequisites of ideal safety-critical organizations

International Nuclear Information System (INIS)

Takeuchi, Michiru; Hikono, Masaru; Matsui, Yuko; Goto, Manabu; Sakuda, Hiroshi

2013-01-01

This study explores the prerequisites of ideal safety-critical organizations, marshalling arguments of 4 areas of organizational research on safety, each of which has overlap: a safety culture, high reliability organizations (HROs), organizational resilience, and leadership especially in safety-critical organizations. The approach taken in this study was to retrieve questionnaire items or items on checklists of the 4 research areas and use them as materials of abduction (as referred to in the KJ method). The results showed that the prerequisites of ideal safety-oriented organizations consist of 9 factors as follows: (1) The organization provides resources and infrastructure to ensure safety. (2) The organization has a sharable vision. (3) Management attaches importance to safety. (4) Employees openly communicate issues and share wide-ranging information with each other. (5) Adjustments and improvements are made as the organization's situation changes. (6) Learning activities from mistakes and failures are performed. (7) Management creates a positive work environment and promotes good relations in the workplace. (8) Workers have good relations in the workplace. (9) Employees have all the necessary requirements to undertake their own functions, and act conservatively. (author)

Psychosis-proneness and neural correlates of self-inhibition in theory of mind.

Directory of Open Access Journals (Sweden)

Lisette van der Meer

Full Text Available Impaired Theory of Mind (ToM has been repeatedly reported as a feature of psychotic disorders. ToM is crucial in social interactions and for the development of social behavior. It has been suggested that reasoning about the belief of others, requires inhibition of the self-perspective. We investigated the neural correlates of self-inhibition in nineteen low psychosis prone (PP and eighteen high PP subjects presenting with subclinical features. High PP subjects have a more than tenfold increased risk of developing a schizophrenia-spectrum disorder. Brain activation was measured with functional Magnetic Resonance Imaging during a ToM task differentiating between self-perspective inhibition and belief reasoning. Furthermore, to test underlying inhibitory mechanisms, we included a stop-signal task. We predicted worse behavioral performance for high compared to low PP subjects on both tasks. Moreover, based on previous neuroimaging results, different activation patterns were expected in the inferior frontal gyrus (IFG in high versus low PP subjects in self-perspective inhibition and simple response inhibition. Results showed increased activation in left IFG during self-perspective inhibition, but not during simple response inhibition, for high PP subjects as compared to low PP subjects. High and low PP subjects showed equal behavioral performance. The results suggest that at a neural level, high PP subjects need more resources for inhibiting the self-perspective, but not for simple motor response inhibition, to equal the performance of low PP subjects. This may reflect a compensatory mechanism, which may no longer be available for patients with schizophrenia-spectrum disorders resulting in ToM impairments.

Psychosis-proneness and neural correlates of self-inhibition in theory of mind.

Science.gov (United States)

van der Meer, Lisette; Groenewold, Nynke A; Pijnenborg, Marieke; Aleman, André

2013-01-01

Impaired Theory of Mind (ToM) has been repeatedly reported as a feature of psychotic disorders. ToM is crucial in social interactions and for the development of social behavior. It has been suggested that reasoning about the belief of others, requires inhibition of the self-perspective. We investigated the neural correlates of self-inhibition in nineteen low psychosis prone (PP) and eighteen high PP subjects presenting with subclinical features. High PP subjects have a more than tenfold increased risk of developing a schizophrenia-spectrum disorder. Brain activation was measured with functional Magnetic Resonance Imaging during a ToM task differentiating between self-perspective inhibition and belief reasoning. Furthermore, to test underlying inhibitory mechanisms, we included a stop-signal task. We predicted worse behavioral performance for high compared to low PP subjects on both tasks. Moreover, based on previous neuroimaging results, different activation patterns were expected in the inferior frontal gyrus (IFG) in high versus low PP subjects in self-perspective inhibition and simple response inhibition. Results showed increased activation in left IFG during self-perspective inhibition, but not during simple response inhibition, for high PP subjects as compared to low PP subjects. High and low PP subjects showed equal behavioral performance. The results suggest that at a neural level, high PP subjects need more resources for inhibiting the self-perspective, but not for simple motor response inhibition, to equal the performance of low PP subjects. This may reflect a compensatory mechanism, which may no longer be available for patients with schizophrenia-spectrum disorders resulting in ToM impairments.

Mindful attention reduces neural and self-reported cue-induced craving in smokers

Science.gov (United States)

Creswell, John David; Tabibnia, Golnaz; Julson, Erica; Kober, Hedy; Tindle, Hilary A.

2013-01-01

An emerging body of research suggests that mindfulness-based interventions may be beneficial for smoking cessation and the treatment of other addictive disorders. One way that mindfulness may facilitate smoking cessation is through the reduction of craving to smoking cues. The present work considers whether mindful attention can reduce self-reported and neural markers of cue-induced craving in treatment seeking smokers. Forty-seven (n = 47) meditation-naïve treatment-seeking smokers (12-h abstinent from smoking) viewed and made ratings of smoking and neutral images while undergoing functional magnetic resonance imaging (fMRI). Participants were trained and instructed to view these images passively or with mindful attention. Results indicated that mindful attention reduced self-reported craving to smoking images, and reduced neural activity in a craving-related region of subgenual anterior cingulate cortex (sgACC). Moreover, a psychophysiological interaction analysis revealed that mindful attention reduced functional connectivity between sgACC and other craving-related regions compared to passively viewing smoking images, suggesting that mindfulness may decouple craving neurocircuitry when viewing smoking cues. These results provide an initial indication that mindful attention may describe a ‘bottom-up’ attention to one’s present moment experience in ways that can help reduce subjective and neural reactivity to smoking cues in smokers. PMID:22114078

Self-teaching neural network learns difficult reactor control problem

International Nuclear Information System (INIS)

Jouse, W.C.

1989-01-01

A self-teaching neural network used as an adaptive controller quickly learns to control an unstable reactor configuration. The network models the behavior of a human operator. It is trained by allowing it to operate the reactivity control impulsively. It is punished whenever either the power or fuel temperature stray outside technical limits. Using a simple paradigm, the network constructs an internal representation of the punishment and of the reactor system. The reactor is constrained to small power orbits

Built-in self-repair of VLSI memories employing neural nets

Science.gov (United States)

Mazumder, Pinaki

1998-10-01

The decades of the Eighties and the Nineties have witnessed the spectacular growth of VLSI technology, when the chip size has increased from a few hundred devices to a staggering multi-millon transistors. This trend is expected to continue as the CMOS feature size progresses towards the nanometric dimension of 100 nm and less. SIA roadmap projects that, where as the DRAM chips will integrate over 20 billion devices in the next millennium, the future microprocessors may incorporate over 100 million transistors on a single chip. As the VLSI chip size increase, the limited accessibility of circuit components poses great difficulty for external diagnosis and replacement in the presence of faulty components. For this reason, extensive work has been done in built-in self-test techniques, but little research is known concerning built-in self-repair. Moreover, the extra hardware introduced by conventional fault-tolerance techniques is also likely to become faulty, therefore causing the circuit to be useless. This research demonstrates the feasibility of implementing electronic neural networks as intelligent hardware for memory array repair. Most importantly, we show that the neural network control possesses a robust and degradable computing capability under various fault conditions. Overall, a yield analysis performed on 64K DRAM's shows that the yield can be improved from as low as 20 percent to near 99 percent due to the self-repair design, with overhead no more than 7 percent.

Neural Correlates of Biased Responses: The Negative Method Effect in the Rosenberg Self-Esteem Scale Is Associated with Right Amygdala Volume.

Science.gov (United States)

Wang, Yinan; Kong, Feng; Huang, Lijie; Liu, Jia

2016-10-01

Self-esteem is a widely studied construct in psychology that is typically measured by the Rosenberg Self-Esteem Scale (RSES). However, a series of cross-sectional and longitudinal studies have suggested that a simple and widely used unidimensional factor model does not provide an adequate explanation of RSES responses due to method effects. To identify the neural correlates of the method effect, we sought to determine whether and how method effects were associated with the RSES and investigate the neural basis of these effects. Two hundred and eighty Chinese college students (130 males; mean age = 22.64 years) completed the RSES and underwent magnetic resonance imaging (MRI). Behaviorally, method effects were linked to both positively and negatively worded items in the RSES. Neurally, the right amygdala volume negatively correlated with the negative method factor, while the hippocampal volume positively correlated with the general self-esteem factor in the RSES. The neural dissociation between the general self-esteem factor and negative method factor suggests that there are different neural mechanisms underlying them. The amygdala is involved in modulating negative affectivity; therefore, the current study sheds light on the nature of method effects that are related to self-report with a mix of positively and negatively worded items. © 2015 Wiley Periodicals, Inc.

The self-organizing map, a new approach to apprehend the Madden–Julian Oscillation influence on the intraseasonal variability of rainfall in the southern African region

CSIR Research Space (South Africa)

Oettli, P

2013-11-01

Full Text Available -linear classification method, the self-organizing map (SOM), a type of artificial neural network used to produce a low-dimensional representation of high-dimensional datasets, to capture more accurately the life cycle of the MJO and its global impacts...

A neural network based seafloor classification using acoustic backscatter

Digital Repository Service at National Institute of Oceanography (India)

Chakraborty, B.

This paper presents a study results of the Artificial Neural Network (ANN) architectures [Self-Organizing Map (SOM) and Multi-Layer Perceptron (MLP)] using single beam echosounding data. The single beam echosounder, operable at 12 kHz, has been used...

Democracy versus dictatorship in self-organized models of financial markets

Science.gov (United States)

D'Hulst, R.; Rodgers, G. J.

2000-06-01

Models to mimic the transmission of information in financial markets are introduced. As an attempt to generate the demand process, we distinguish between dictatorship associations, where groups of agents rely on one of them to make decision, and democratic associations, where each agent takes part in the group decision. In the dictatorship model, agents segregate into two distinct populations, while the democratic model is driven towards a critical state where groups of agents of all sizes exist. Hence, both models display a level of organization, but only the democratic model is self-organized. We show that the dictatorship model generates less-volatile markets than the democratic model.

Direct Neural Conversion from Human Fibroblasts Using Self-Regulating and Nonintegrating Viral Vectors

Directory of Open Access Journals (Sweden)

Shong Lau

2014-12-01

Full Text Available Summary: Recent findings show that human fibroblasts can be directly programmed into functional neurons without passing via a proliferative stem cell intermediate. These findings open up the possibility of generating subtype-specific neurons of human origin for therapeutic use from fetal cell, from patients themselves, or from matched donors. In this study, we present an improved system for direct neural conversion of human fibroblasts. The neural reprogramming genes are regulated by the neuron-specific microRNA, miR-124, such that each cell turns off expression of the reprogramming genes once the cell has reached a stable neuronal fate. The regulated system can be combined with integrase-deficient vectors, providing a nonintegrative and self-regulated conversion system that rids problems associated with the integration of viral transgenes into the host genome. These modifications make the system suitable for clinical use and therefore represent a major step forward in the development of induced neurons for cell therapy. : Lau et al. now use miRNA targeting to build a self-regulating neural conversion system. Combined with nonintegrating vectors, this system can efficiently drive conversion of human fibroblasts into functional induced neurons (iNs suitable for clinical applications.

Self-assessment, reflection on practice and critical thinking in nursing students.

Science.gov (United States)

Siles-González, José; Solano-Ruiz, Carmen

2016-10-01

In accordance with the principles of the European Higher Education Area, the aim of this study was to contribute to the implementation of self-assessment through the application of reflection on learning and critical thinking. The theoretical framework employed was Habermas's critical theory and emancipatory interest as a preliminary step to generate educational transformations. The methodological contribution is the design a student self-assessment document that promotes reflection on action and critical thinking. The development of assessment through peer evaluation and other intermediate solutions until achieving self-assessment entails a shift in the educational and scientific paradigm, but also involves the implementation in practice of democratic and ethical principles, values and premises in society. Self-assessment is a novel concept for students, and obliges them to reinterpret their role. Due to the diversity of students' principles, values, motivations, interests and aspirations, this reinterpretation of their role can have a positive outcome, stimulating an active and critical attitude towards group work and self-assessment; or, on the contrary, can generate a stance characterised by disinterest, passivity and lack of critical thinking. The forms of assessment adopted in a given educational system reflect ways of thinking related to ideologies, values, ethical principles and educational paradigms: in order to render implementation of effective self-assessment feasible, it is necessary to undertake structural and regulatory reforms. Students have little experience of reflection on practice or critical thinking. Massification and cultural and structural factors determine the form of assessment. In this context, it would seem advisable to move towards self-assessment gradually and cautiously. Copyright © 2016 Elsevier Ltd. All rights reserved.

CRIM1 Complexes with ß-catenin and Cadherins, Stabilizes Cell-Cell Junctions and Is Critical for Neural Morphogenesis

OpenAIRE

Ponferrada, Virgilio G.; Fan, Jieqing; Vallance, Jefferson E.; Hu, Shengyong; Mamedova, Aygun; Rankin, Scott A.; Kofron, Matthew; Zorn, Aaron M.; Hegde, Rashmi S.; Lang, Richard A.

2012-01-01

In multicellular organisms, morphogenesis is a highly coordinated process that requires dynamically regulated adhesion between cells. An excellent example of cellular morphogenesis is the formation of the neural tube from the flattened epithelium of the neural plate. Cysteine-rich motor neuron protein 1 (CRIM1) is a single-pass (type 1) transmembrane protein that is expressed in neural structures beginning at the neural plate stage. In the frog Xenopus laevis, loss of function studies using C...

Interpersonal problems, dependency, and self-criticism in major depressive disorder.

Science.gov (United States)

Dinger, Ulrike; Barrett, Marna S; Zimmermann, Johannes; Schauenburg, Henning; Wright, Aidan G C; Renner, Fritz; Zilcha-Mano, Sigal; Barber, Jacques P

2015-01-01

The goal of the present research was the examination of overlap between 2 research traditions on interpersonal personality traits in major depression. We hypothesized that Blatt's (2004) dimensions of depressive experiences around the dimensions of relatedness (i.e., dependency) and self-definition (i.e., self-criticism) are associated with specific interpersonal problems according to the interpersonal circumplex model (Leary, 1957). In addition, we examined correlations of interpersonal characteristics with depression severity. Analyses were conducted on 283 patients with major depressive disorder combined from 2 samples. Of the patients, 151 participated in a randomized controlled trial in the United States, and 132 patients were recruited in an inpatient unit in Germany. Patients completed measures of symptomatic distress, interpersonal problems, and depressive experiences. Dependency was associated with more interpersonal problems related to low dominance and high affiliation, while self-criticism was associated with more interpersonal problems related to low affiliation. These associations were independent of depression severity. Self-criticism showed high overlap with cognitive symptoms of depression. The findings support the interpersonal nature of Blatt's dimensions of depressive experiences. Self-criticism is associated with being too distant or cold toward others as well as greater depression severity, but is not related to the dimension of dominance. © 2014 Wiley Periodicals, Inc.

Critical thinking, self-esteem, and state anxiety of nursing students.

Science.gov (United States)

Suliman, Wafika A; Halabi, Jehad

2007-02-01

This study aimed at exploring the existing predominant critical thinking disposition(s) of baccalaureate nursing students and the relationship among their critical thinking (CT), self-esteem (SE), and state anxiety (SA). Cross-sectional correlational design was utilized to achieve the said aim. A voluntary convenient sample consisted of first year (n=105) and fourth year (n=60) nursing students. The California Critical Thinking Disposition Inventory, Rosenberg Self-Esteem Scale, and Spielberger State Anxiety Inventory were used for data collection after their translation to Arabic language and test for validity and reliability. Descriptive and inferential statistics were used to analyze data. Results showed that both groups overall CT was marginal indicating no serious deficiency, their SE was average, and their SA was relatively high; they reported analyticity, open-mindedness, systematicity, inquisitiveness, and truth seeking as predominant critical thinking dispositions with no significant difference between them. However, the two groups were weak with significant difference on CT self-confidence (t=-2.053, df=136.904, p=.042) with beginning students reporting poorer level of CT self-confidence. Significant correlation results showed that critical thinking is positively correlated with SE, negatively correlated with SA, and SE is negatively correlated with SA; however, all correlations were actually quite low.

Indirect adaptive fuzzy wavelet neural network with self- recurrent consequent part for AC servo system.

Science.gov (United States)

Hou, Runmin; Wang, Li; Gao, Qiang; Hou, Yuanglong; Wang, Chao

2017-09-01

This paper proposes a novel indirect adaptive fuzzy wavelet neural network (IAFWNN) to control the nonlinearity, wide variations in loads, time-variation and uncertain disturbance of the ac servo system. In the proposed approach, the self-recurrent wavelet neural network (SRWNN) is employed to construct an adaptive self-recurrent consequent part for each fuzzy rule of TSK fuzzy model. For the IAFWNN controller, the online learning algorithm is based on back propagation (BP) algorithm. Moreover, an improved particle swarm optimization (IPSO) is used to adapt the learning rate. The aid of an adaptive SRWNN identifier offers the real-time gradient information to the adaptive fuzzy wavelet neural controller to overcome the impact of parameter variations, load disturbances and other uncertainties effectively, and has a good dynamic. The asymptotical stability of the system is guaranteed by using the Lyapunov method. The result of the simulation and the prototype test prove that the proposed are effective and suitable. Copyright © 2017. Published by Elsevier Ltd.

Self-learning Monte Carlo with deep neural networks

Science.gov (United States)

Shen, Huitao; Liu, Junwei; Fu, Liang

2018-05-01

The self-learning Monte Carlo (SLMC) method is a general algorithm to speedup MC simulations. Its efficiency has been demonstrated in various systems by introducing an effective model to propose global moves in the configuration space. In this paper, we show that deep neural networks can be naturally incorporated into SLMC, and without any prior knowledge can learn the original model accurately and efficiently. Demonstrated in quantum impurity models, we reduce the complexity for a local update from O (β2) in Hirsch-Fye algorithm to O (β lnβ ) , which is a significant speedup especially for systems at low temperatures.

Development of test algorithm for semiconductor package with defects by using probabilistic neural network

International Nuclear Information System (INIS)

Kim, Jae Yeol; Sim, Jae Gi; Ko, Myoung Soo; Kim, Chang Hyun; Kim, Hun Cho

2001-01-01

In this study, researchers developing the estimative algorithm for artificial defects in semiconductor packages and performing it by pattern recognition technology. For this purpose, the estimative algorithm was included that researchers made software with MATLAB. The software consists of some procedures including ultrasonic image acquisition, equalization filtering, Self-Organizing Map and Probabilistic Neural Network. Self-Organizing Map and Probabilistic Neural Network are belong to methods of Neural Networks. And the pattern recognition technology has applied to classify three kinds of detective patterns in semiconductor packages. This study presumes probability density function from a sample of learning and present which is automatically determine method. PNN can distinguish flaws very difficult distinction as well as. This can do parallel process to stand in a row we confirm that is very efficiently classifier if we applied many data real the process.

Neural representations of the self and the mother for Chinese individuals.

Directory of Open Access Journals (Sweden)

Gaowa Wuyun

Full Text Available An important question in social neuroscience is the similarities and differences in the neural representations between the self and close others. Most studies examining this topic have identified the medial prefrontal cortex (MPFC region as the primary area involved in this process. However, several studies have reported conflicting data, making further investigation of this topic very important. In this functional magnetic resonance imaging (fMRI study, we investigated the brain activity in the anterior cingulate cortex (ACC when Chinese participants passively listened to their self-name (SN, their mother's name (MN, and unknown names (UN. The results showed that compared with UN recognition, SN perception was associated with a robust activation in a widely distributed bilateral network, including the cortical midline structure (the MPFC and ACC, the inferior frontal gyrus, and the middle temporal gyrus. The SN invoked the bilateral superior temporal gyrus in contrast to the MN; the MN recognition provoked a stronger activation in the central and posterior brain regions in contrast to the SN recognition. The SN and MN caused an activation of overlapping areas, namely, the ACC, MPFC, and superior frontal gyrus. These results suggest that Chinese individuals utilize certain common brain region in processing both the SN and the MN. The present findings provide evidence for the neural basis of the self and close others for Chinese individuals.

Experimental investigation of multiple self-organized structures in plasma

International Nuclear Information System (INIS)

Ivan, L. M.; Gaman, C.; Aflori, M.; Mihai-Plugaru, M.; Dimitriu, D.G.; Lozneanu, E.; Sanduloviciu, M.

2005-01-01

Complex space charge configuration emerges by self-organization in front of an electrode immersed in plasma when its potential is increased at a certain critical value. Consisting from a nucleus protected from the surrounding plasma by an electrical double layer, the complexity reveals an internal structure and behaviour which remind us primitive organisms. Thus the complexity is not static but stationary open system in which continuous decay is constantly compensated by substance and energy from the surrounding plasma. Endowed with a special kind of memory the complexity can work as an intelligent multifunctional system and consequently it is also able to perform innovations after selective interaction with an environment in evolution. Additionally, the complexity is able to replicate by division. (authors)

Enhancement of signal sensitivity in a heterogeneous neural network refined from synaptic plasticity

Energy Technology Data Exchange (ETDEWEB)

Li Xiumin; Small, Michael, E-mail: ensmall@polyu.edu.h, E-mail: 07901216r@eie.polyu.edu.h [Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon (Hong Kong)

2010-08-15

Long-term synaptic plasticity induced by neural activity is of great importance in informing the formation of neural connectivity and the development of the nervous system. It is reasonable to consider self-organized neural networks instead of prior imposition of a specific topology. In this paper, we propose a novel network evolved from two stages of the learning process, which are respectively guided by two experimentally observed synaptic plasticity rules, i.e. the spike-timing-dependent plasticity (STDP) mechanism and the burst-timing-dependent plasticity (BTDP) mechanism. Due to the existence of heterogeneity in neurons that exhibit different degrees of excitability, a two-level hierarchical structure is obtained after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that it has the small-world properties of small shortest path length and high clustering coefficients. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission in the network.

Enhancement of signal sensitivity in a heterogeneous neural network refined from synaptic plasticity

International Nuclear Information System (INIS)

Li Xiumin; Small, Michael

2010-01-01

Long-term synaptic plasticity induced by neural activity is of great importance in informing the formation of neural connectivity and the development of the nervous system. It is reasonable to consider self-organized neural networks instead of prior imposition of a specific topology. In this paper, we propose a novel network evolved from two stages of the learning process, which are respectively guided by two experimentally observed synaptic plasticity rules, i.e. the spike-timing-dependent plasticity (STDP) mechanism and the burst-timing-dependent plasticity (BTDP) mechanism. Due to the existence of heterogeneity in neurons that exhibit different degrees of excitability, a two-level hierarchical structure is obtained after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that it has the small-world properties of small shortest path length and high clustering coefficients. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission in the network.

Neural correlates of self-deception and impression-management.

Science.gov (United States)

Farrow, Tom F D; Burgess, Jenny; Wilkinson, Iain D; Hunter, Michael D

2015-01-01

Self-deception and impression-management comprise two types of deceptive, but generally socially acceptable behaviours, which are common in everyday life as well as being present in a number of psychiatric disorders. We sought to establish and dissociate the 'normal' brain substrates of self-deception and impression-management. Twenty healthy participants underwent fMRI scanning at 3T whilst completing the 'Balanced Inventory of Desirable Responding' test under two conditions: 'fake good', giving the most desirable impression possible and 'fake bad' giving an undesirable impression. Impression-management scores were more malleable to manipulation via 'faking' than self-deception scores. Response times to self-deception questions and 'fake bad' instructions were significantly longer than to impression-management questions and 'fake good' instructions respectively. Self-deception and impression-management manipulation and 'faking bad' were associated with activation of medial prefrontal cortex (mPFC) and left ventrolateral prefrontal cortex (vlPFC). Impression-management manipulation was additionally associated with activation of left dorsolateral prefrontal cortex and left posterior middle temporal gyrus. 'Faking bad' was additionally associated with activation of right vlPFC, left temporo-parietal junction and right cerebellum. There were no supra-threshold activations associated with 'faking good'. Our neuroimaging data suggest that manipulating self-deception and impression-management and more specifically 'faking bad' engages a common network comprising mPFC and left vlPFC. Shorter response times and lack of dissociable neural activations suggests that 'faking good', particularly when it comes to impression-management, may be our most practiced 'default' mode. Copyright © 2014 Elsevier Ltd. All rights reserved.

Self-tuning control of a nuclear reactor using a Gaussian function neural network

International Nuclear Information System (INIS)

Park, M.G.; Cho, N.Z.

1995-01-01

A self-tuning control method is described for a nuclear reactor system that requires only a set of input-output measurements. The use of an artificial neural network in nonlinear model-based adaptive control, both as a plant model and a controller, is investigated. A neural network called a Gaussian function network is used for one-step-ahead predictive control to track the desired plant output. The effectiveness of the controller is demonstrated by the application of the method to the power tracking control of the Korea Multipurpose Research Reactor

Neural Decoding of Bistable Sounds Reveals an Effect of Intention on Perceptual Organization.

Science.gov (United States)

Billig, Alexander J; Davis, Matthew H; Carlyon, Robert P

2018-03-14

Auditory signals arrive at the ear as a mixture that the brain must decompose into distinct sources based to a large extent on acoustic properties of the sounds. An important question concerns whether listeners have voluntary control over how many sources they perceive. This has been studied using pure high (H) and low (L) tones presented in the repeating pattern HLH-HLH-, which can form a bistable percept heard either as an integrated whole (HLH-) or as segregated into high (H-H-) and low (-L-) sequences. Although instructing listeners to try to integrate or segregate sounds affects reports of what they hear, this could reflect a response bias rather than a perceptual effect. We had human listeners (15 males, 12 females) continuously report their perception of such sequences and recorded neural activity using MEG. During neutral listening, a classifier trained on patterns of neural activity distinguished between periods of integrated and segregated perception. In other conditions, participants tried to influence their perception by allocating attention either to the whole sequence or to a subset of the sounds. They reported hearing the desired percept for a greater proportion of time than when listening neutrally. Critically, neural activity supported these reports; stimulus-locked brain responses in auditory cortex were more likely to resemble the signature of segregation when participants tried to hear segregation than when attempting to perceive integration. These results indicate that listeners can influence how many sound sources they perceive, as reflected in neural responses that track both the input and its perceptual organization. SIGNIFICANCE STATEMENT Can we consciously influence our perception of the external world? We address this question using sound sequences that can be heard either as coming from a single source or as two distinct auditory streams. Listeners reported spontaneous changes in their perception between these two interpretations while

ORGANIC ELECTRODE COATINGS FOR NEXT-GENERATION NEURAL INTERFACES

Directory of Open Access Journals (Sweden)

Ulises A Aregueta-Robles

2014-05-01

Full Text Available Traditional neuronal interfaces utilize metallic electrodes which in recent years have reached a plateau in terms of the ability to provide safe stimulation at high resolution or rather with high densities of microelectrodes with improved spatial selectivity. To achieve higher resolution it has become clear that reducing the size of electrodes is required to enable higher electrode counts from the implant device. The limitations of interfacing electrodes including low charge injection limits, mechanical mismatch and foreign body response can be addressed through the use of organic electrode coatings which typically provide a softer, more roughened surface to enable both improved charge transfer and lower mechanical mismatch with neural tissue. Coating electrodes with conductive polymers or carbon nanotubes offers a substantial increase in charge transfer area compared to conventional platinum electrodes. These organic conductors provide safe electrical stimulation of tissue while avoiding undesirable chemical reactions and cell damage. However, the mechanical properties of conductive polymers are not ideal, as they are quite brittle. Hydrogel polymers present a versatile coating option for electrodes as they can be chemically modified to provide a soft and conductive scaffold. However, the in vivo chronic inflammatory response of these conductive hydrogels remains unknown. A more recent approach proposes tissue engineering the electrode interface through the use of encapsulated neurons within hydrogel coatings. This approach may provide a method for activating tissue at the cellular scale, however several technological challenges must be addressed to demonstrate feasibility of this innovative idea. The review focuses on the various organic coatings which have been investigated to improve neural interface electrodes.

Self-Organization of Motor-Propelled Cytoskeletal Filaments at Topographically Defined Borders

Directory of Open Access Journals (Sweden)

Alf Månsson

2012-01-01

Full Text Available Self-organization phenomena are of critical importance in living organisms and of great interest to exploit in nanotechnology. Here we describe in vitro self-organization of molecular motor-propelled actin filaments, manifested as a tendency of the filaments to accumulate in high density close to topographically defined edges on nano- and microstructured surfaces. We hypothesized that this “edge-tracing” effect either (1 results from increased motor density along the guiding edges or (2 is a direct consequence of the asymmetric constraints on stochastic changes in filament sliding direction imposed by the edges. The latter hypothesis is well captured by a model explicitly defining the constraints of motility on structured surfaces in combination with Monte-Carlo simulations [cf. Nitta et al. (2006] of filament sliding. In support of hypothesis 2 we found that the model reproduced the edge tracing effect without the need to assume increased motor density at the edges. We then used model simulations to elucidate mechanistic details. The results are discussed in relation to nanotechnological applications and future experiments to test model predictions.

PLGA nanofibers blended with designer self-assembling peptides for peripheral neural regeneration

Energy Technology Data Exchange (ETDEWEB)

Nune, Manasa; Krishnan, Uma Maheswari; Sethuraman, Swaminathan, E-mail: swami@sastra.edu

2016-05-01

Electrospun nanofibers are attractive candidates for neural regeneration due to similarity to the extracellular matrix. Several synthetic polymers have been used but they lack in providing the essential biorecognition motifs on their surfaces. Self-assembling peptide nanofiber scaffolds (SAPNFs) like RADA16 and recently, designer SAPs with functional motifs RADA16-I-BMHP1 areexamples, which showed successful spinal cord regeneration. But these peptide nanofiber scaffolds have poor mechanical properties and faster degradation rates that limit their use for larger nerve defects. Hence, we have developed a novel hybrid nanofiber scaffold of polymer poly(L-lactide-co-glycolide) (PLGA) and RADA16-I-BMHP1. The scaffolds were characterized for the presence of peptides both qualitatively and quantitatively using several techniques like SEM, EDX, FTIR, CHN analysis, Circular Dichroism analysis, Confocal and thermal analysis. Peptide self-assembly was retained post-electrospinning and formed rod-like nanostructures on PLGA nanofibers. In vitro cell compatibility was studied using rat Schwann cells and their adhesion, proliferation and gene expression levels on the designed scaffolds were evaluated. Our results have revealed the significant effects of the peptide blended scaffolds on promoting Schwann cell adhesion, extension and phenotypic expression. Neural development markers (SEM3F, NRP2 & PLX1) gene expression levels were significantly upregulated in peptide blended scaffolds compared to the PLGA scaffolds. Thus the hybrid blended novel designer scaffolds seem to be promising candidates for successful and functional regeneration of the peripheral nerve. - Highlights: • A novel blended scaffold of polymer PLGA and designer self-assembling peptide RADA16-I-BMPH1 was designed • The peptide retained the self-assembling features and formed rod like nanostructures on top of PLGA nanofibers • PLGA-peptide scaffolds have promoted the Schwann cell bipolar extension and

Self-Organizing Units in an Interdisciplinary Course for Pervasive Computing Design

OpenAIRE

McNair, Lisa; Newswander, Chad; Coupey, Eloise; Dorsa, Ed; Martin, Tom; Paretti, Marie

2009-01-01

We conducted a case study of a design course that focused on bringing together students from engineering, industrial design, and marketing to use pervasive computing technologies to design, coordinate, and build a “smart” dorm room for disabled individuals. The class was loosely structured to encourage innovation, critical thinking and interdisciplinarity. In this environment, teams were created, disassembled, and re-created in a self-organizing fashion. With few norms, teams were expected to...

Identifying Emotions on the Basis of Neural Activation.

Science.gov (United States)

Kassam, Karim S; Markey, Amanda R; Cherkassky, Vladimir L; Loewenstein, George; Just, Marcel Adam

2013-01-01

We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

Self-organizing representations

Energy Technology Data Exchange (ETDEWEB)

Kohonen, T.

1983-01-01

A property which is commonplace in the brain but which has always been ignored in learning machines is the spatial order of the processing units. This order is clearly highly significant and in nature it develops gradually during the lifetime of the organism. It then serves as the basis for perceptual and cognitive processes, and memory, too. The spatial order in biological organisms is often believed to be genetically determined. It is therefore intriguing to learn that a meaningful and optimal spatial order is formed in an extremely simple self-organizing process whereby certain feature maps are formed automatically. 8 references.

Optimal system size for complex dynamics in random neural networks near criticality

Energy Technology Data Exchange (ETDEWEB)

Wainrib, Gilles, E-mail: wainrib@math.univ-paris13.fr [Laboratoire Analyse Géométrie et Applications, Université Paris XIII, Villetaneuse (France); García del Molino, Luis Carlos, E-mail: garciadelmolino@ijm.univ-paris-diderot.fr [Institute Jacques Monod, Université Paris VII, Paris (France)

2013-12-15

In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices.

Optimal system size for complex dynamics in random neural networks near criticality

International Nuclear Information System (INIS)

Wainrib, Gilles; García del Molino, Luis Carlos

2013-01-01

In this article, we consider a model of dynamical agents coupled through a random connectivity matrix, as introduced by Sompolinsky et al. [Phys. Rev. Lett. 61(3), 259–262 (1988)] in the context of random neural networks. When system size is infinite, it is known that increasing the disorder parameter induces a phase transition leading to chaotic dynamics. We observe and investigate here a novel phenomenon in the sub-critical regime for finite size systems: the probability of observing complex dynamics is maximal for an intermediate system size when the disorder is close enough to criticality. We give a more general explanation of this type of system size resonance in the framework of extreme values theory for eigenvalues of random matrices

Fuzzy-neural network in the automatic detection and volumetry of the spleen on spiral CT scans

International Nuclear Information System (INIS)

Heitmann, K.R.; Mainz Univ.; Rueckert, S.; Heussel, C.P.; Thelen, M.; Kauczor, H.U.; Uthmann, T.

2000-01-01

Purpose: To assess spleen segmentation and volumetry in spiral CT scans with and without pathological changes of splenic tissue. Methods: The image analysis software HYBRIKON is based on region growing, self-organized neural nets, and fuzzy-anatomic rules. The neural nets were trained with spiral CT data from 10 patients, not used in the following evaluation on spiral CT scans from 19 patients. An experienced radiologist verified the results. The true positive and false positive areas were compared in terms to the areas marked by the radiologist. The results were compared with a standard thresholding method. Results: The neural nets achieved a higher accuracy than the thresholding method. Correlation coefficient of the fuzzy-neural nets: 0.99 (thresholding: 0.63). Mean true positive rate: 90% (thresholding: 75%), mean false positive rate: 5% (thresholding>100%). Pitfalls were caused by accessory spleens, extreme changes in the morphology (tumors, metastases, cysts), and parasplenic masses. Conclusions: Self-organizing neural nets combined with fuzzy rules are ready for use in the automatic detection and volumetry of the spleen in spiral CT scans. (orig.) [de

The level of the transcription factor Pax6 is essential for controlling the balance between neural stem cell self-renewal and neurogenesis.

Directory of Open Access Journals (Sweden)

Stephen N Sansom

2009-06-01

Full Text Available Neural stem cell self-renewal, neurogenesis, and cell fate determination are processes that control the generation of specific classes of neurons at the correct place and time. The transcription factor Pax6 is essential for neural stem cell proliferation, multipotency, and neurogenesis in many regions of the central nervous system, including the cerebral cortex. We used Pax6 as an entry point to define the cellular networks controlling neural stem cell self-renewal and neurogenesis in stem cells of the developing mouse cerebral cortex. We identified the genomic binding locations of Pax6 in neocortical stem cells during normal development and ascertained the functional significance of genes that we found to be regulated by Pax6, finding that Pax6 positively and directly regulates cohorts of genes that promote neural stem cell self-renewal, basal progenitor cell genesis, and neurogenesis. Notably, we defined a core network regulating neocortical stem cell decision-making in which Pax6 interacts with three other regulators of neurogenesis, Neurog2, Ascl1, and Hes1. Analyses of the biological function of Pax6 in neural stem cells through phenotypic analyses of Pax6 gain- and loss-of-function mutant cortices demonstrated that the Pax6-regulated networks operating in neural stem cells are highly dosage sensitive. Increasing Pax6 levels drives the system towards neurogenesis and basal progenitor cell genesis by increasing expression of a cohort of basal progenitor cell determinants, including the key transcription factor Eomes/Tbr2, and thus towards neurogenesis at the expense of self-renewal. Removing Pax6 reduces cortical stem cell self-renewal by decreasing expression of key cell cycle regulators, resulting in excess early neurogenesis. We find that the relative levels of Pax6, Hes1, and Neurog2 are key determinants of a dynamic network that controls whether neural stem cells self-renew, generate cortical neurons, or generate basal progenitor cells

Self-organized amniogenesis by human pluripotent stem cells in a biomimetic implantation-like niche

Science.gov (United States)

Shao, Yue; Taniguchi, Kenichiro; Gurdziel, Katherine; Townshend, Ryan F.; Xue, Xufeng; Yong, Koh Meng Aw; Sang, Jianming; Spence, Jason R.; Gumucio, Deborah L.; Fu, Jianping

2017-04-01

Amniogenesis--the development of amnion--is a critical developmental milestone for early human embryogenesis and successful pregnancy. However, human amniogenesis is poorly understood due to limited accessibility to peri-implantation embryos and a lack of in vitro models. Here we report an efficient biomaterial system to generate human amnion-like tissue in vitro through self-organized development of human pluripotent stem cells (hPSCs) in a bioengineered niche mimicking the in vivo implantation environment. We show that biophysical niche factors act as a switch to toggle hPSC self-renewal versus amniogenesis under self-renewal-permissive biochemical conditions. We identify a unique molecular signature of hPSC-derived amnion-like cells and show that endogenously activated BMP-SMAD signalling is required for the amnion-like tissue development by hPSCs. This study unveils the self-organizing and mechanosensitive nature of human amniogenesis and establishes the first hPSC-based model for investigating peri-implantation human amnion development, thereby helping advance human embryology and reproductive medicine.

Seafloor classification using acoustic backscatter echo-waveform - Artificial neural network applications

Digital Repository Service at National Institute of Oceanography (India)

Chakraborty, B.; Mahale, V.; Navelkar, G.S.; Desai, R.G.P.

In this paper seafloor classifications system based on artificial neural network (ANN) has been designed. The ANN architecture employed here is a combination of Self Organizing Feature Map (SOFM) and Linear Vector Quantization (LVQ1). Currently...

The neural correlates of implicit self-relevant processing in low self-esteem: an ERP study.

Science.gov (United States)

Yang, Juan; Guan, Lili; Dedovic, Katarina; Qi, Mingming; Zhang, Qinglin

2012-08-30

Previous neuroimaging studies have shown that implicit and explicit processing of self-relevant (schematic) material elicit activity in many of the same brain regions. Electrophysiological studies on the neural processing of explicit self-relevant cues have generally supported the view that P300 is an index of attention to self-relevant stimuli; however, there has been no study to date investigating the temporal course of implicit self-relevant processing. The current study seeks to investigate the time course involved in implicit self-processing by comparing processing of self-relevant with non-self-relevant words while subjects are making a judgment about color of the words in an implicit attention task. Sixteen low self-esteem participants were examined using event-related potentials technology (ERP). We hypothesized that this implicit attention task would involve P2 component rather than the P300 component. Indeed, P2 component has been associated with perceptual analysis and attentional allocation and may be more likely to occur in unconscious conditions such as this task. Results showed that latency of P2 component, which indexes the time required for perceptual analysis, was more prolonged in processing self-relevant words compared to processing non-self-relevant words. Our results suggested that the judgment of the color of the word interfered with automatic processing of self-relevant information and resulted in less efficient processing of self-relevant word. Together with previous ERP studies examining processing of explicit self-relevant cues, these findings suggest that the explicit and the implicit processing of self-relevant information would not elicit the same ERP components. Copyright © 2012 Elsevier B.V. All rights reserved.

Cocaine self-administration abolishes associative neural encoding in the nucleus accumbens necessary for higher-order learning.

Science.gov (United States)

Saddoris, Michael P; Carelli, Regina M

2014-01-15

Cocaine use is often associated with diminished cognitive function, persisting even after abstinence from the drug. Likely targets for these changes are the core and shell of the nucleus accumbens (NAc), which are critical for mediating the rewarding aspects of drugs of abuse as well as supporting associative learning. To understand this deficit, we recorded neural activity in the NAc of rats with a history of cocaine self-administration or control subjects while they learned Pavlovian first- and second-order associations. Rats were trained for 2 weeks to self-administer intravenous cocaine or water. Later, rats learned a first-order Pavlovian discrimination where a conditioned stimulus (CS)+ predicted food, and a control (CS-) did not. Rats then learned a second-order association where, absent any food reinforcement, a novel cued (SOC+) predicted the CS+ and another (SOC-) predicted the CS-. Electrophysiological recordings were taken during performance of these tasks in the NAc core and shell. Both control subjects and cocaine-experienced rats learned the first-order association, but only control subjects learned the second-order association. Neural recordings indicated that core and shell neurons encoded task-relevant information that correlated with behavioral performance, whereas this type of encoding was abolished in cocaine-experienced rats. The NAc core and shell perform complementary roles in supporting normal associative learning, functions that are impaired after cocaine experience. This impoverished encoding of motivational behavior, even after abstinence from the drug, might provide a key mechanism to understand why addiction remains a chronically relapsing disorder despite repeated attempts at sobriety. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Field-effect transistors based on self-organized molecular nanostripes

DEFF Research Database (Denmark)

Cavallini, M.; Stoliare, P.; Moulin, J.-F.

2005-01-01

Charge transport properties in organic semiconductors depend strongly on molecular order. Here we demonstrate field-effect transistors where drain current flows through a precisely defined array of nanostripes made of crystalline and highly ordered molecules. The molecular stripes are fabricated ...... by the menisci once the critical concentration is reached and self-organizes into molecularly ordered stripes 100-200 nm wide and a few monolayers high. The charge mobility measured along the stripes is 2 orders of magnitude larger than the values measured for spin-coated thin films....... across the channel of the transistor by a stamp-assisted deposition of the molecular semiconductors from a solution. As the solvent evaporates, the capillary forces drive the solution to form menisci under the stamp protrusions. The solute precipitates only in the regions where the solution is confined...

The neuropsychology of self-reflection in psychiatric illness.

Science.gov (United States)

Philippi, Carissa L; Koenigs, Michael

2014-07-01

The development of robust neuropsychological measures of social and affective function-which link critical dimensions of mental health to their underlying neural circuitry-could be a key step in achieving a more pathophysiologically-based approach to psychiatric medicine. In this article, we summarize research indicating that self-reflection (the inward attention to personal thoughts, memories, feelings, and actions) may be a useful model for developing such a paradigm, as there is evidence that self-reflection is (1) measurable with self-report scales and performance-based tests, (2) linked to the activity of a specific neural circuit, and (3) dimensionally related to mental health and various forms of psychopathology. Copyright © 2014 Elsevier Ltd. All rights reserved.

A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization

International Nuclear Information System (INIS)

Oh, Sung-Kwun; Pedrycz, Witold

2005-01-01

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology. The underlying methodology involves mechanisms of genetic optimization, especially genetic algorithms (GAs). Let us recall that the design of the 'conventional' FPNNs uses an extended Group Method of Data Handling (GMDH) and exploits a fixed fuzzy inference type located at each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already being used in fuzzy modeling. The results reveal superiority of the proposed networks over the existing fuzzy and neural models

Self-critical perfectionism, daily stress, and disclosure of daily emotional events.

Science.gov (United States)

Richardson, Clarissa M E; Rice, Kenneth G

2015-10-01

Although disclosure of stressful events can alleviate distress, self-critical perfectionism may pose an especially strong impediment to disclosure during stress, likely contributing to poorer psychological well-being. In the current study, after completing a measure of self-critical perfectionism (the Discrepancy subscale of the Almost Perfect Scale--Revised; Slaney, Rice, Mobley, Trippi, & Ashby, 2001), 396 undergraduates completed measures of stress and disclosure at the end of each day for 1 week. Consistent with hypotheses and previous research, multilevel modeling results indicated significant intraindividual coupling of daily stress and daily disclosure where disclosure was more likely when experiencing high stress than low stress. As hypothesized, Discrepancy moderated the relationship between daily stress and daily disclosure. Individuals higher in self-critical perfectionism (Discrepancy) were less likely to engage in disclosure under high stress, when disclosure is often most beneficial, than those with lower Discrepancy scores. These results have implications for understanding the role of stress and coping in the daily lives of self-critical perfectionists. (c) 2015 APA, all rights reserved).

Ge Detector Data Classification with Neural Networks

Science.gov (United States)

Wilson, Carly; Martin, Ryan; Majorana Collaboration

2014-09-01

The Majorana Demonstrator experiment is searching for neutrinoless double beta-decay using p-type point contact PPC germanium detectors at the Sanford Underground Research Facility, in South Dakota. Pulse shape discrimination can be used in PPC detectors to distinguish signal-like events from backgrounds. This research program explored the possibility of building a self-organizing map that takes data collected from germanium detectors and classifies the events as either signal or background. Self organizing maps are a type of neural network that are self-learning and less susceptible to being biased from imperfect training data. We acknowledge support from the Office of Nuclear Physics in the DOE Office of Science, the Particle and Nuclear Astrophysics Program of the National Science Foundation and the Russian Foundation for Basic Research.

Influence of self-field on the critical current of Bi-2223/Ag tapes

International Nuclear Information System (INIS)

Lehtonen, Jorma; Korpela, Aki; Nah, Wansoo; Kang, Joonsun; Kovac, Pavol; Melisek, Tibor

2004-01-01

The knowledge of critical current density in a superconducting wire is essential in order to compute AC losses. In HTS tapes the critical current density is difficult to estimate from the measured critical current because self-field tends to reduce the current carrying capacity. In this paper the critical current is measured with a single sample and with two similar samples connected in antiparallel in order to compensate the self-field. Both types of measurement are simulated with finite element method. The simulations help to understand the relation between the measured critical current and material properties. The results suggest that in a high quality tape the self-field effect reduced the measured critical current ∼25% if compared to the real critical current at the zero external field

Interfacial self-organization of bolaamphiphiles bearing mesogenic groups: relationships between the molecular structures and their self-organized morphologies.

Science.gov (United States)

Song, Bo; Liu, Guanqing; Xu, Rui; Yin, Shouchun; Wang, Zhiqiang; Zhang, Xi

2008-04-15

This article discusses the relationship between the molecular structure of bolaamphiphiles bearing mesogenic groups and their interfacial self-organized morphology. On the basis of the molecular structures of bolaamphiphiles, we designed and synthesized a series of molecules with different hydrophobic alkyl chain lengths, hydrophilic headgroups, mesogenic groups, and connectors between the alkyl chains and the mesogenic group. Through investigating their interfacial self-organization behavior, some experiential rules are summarized: (1) An appropriate alkyl chain length is necessary to form stable surface micelles; (2) different categories of headgroups have a great effect on the interfacial self-organized morphology; (3) different types of mesogenic groups have little effect on the structure of the interfacial assembly when it is changed from biphenyl to azobenzene or stilbene; (4) the orientation of the ester linker between the mesogenic group and alkyl chain can greatly influence the interfacial self-organization behavior. It is anticipated that this line of research may be helpful for the molecular engineering of bolaamphiphiles to form tailor-made morphologies.

Self-organized dynamics in local load-sharing fiber bundle models.

Science.gov (United States)

Biswas, Soumyajyoti; Chakrabarti, Bikas K

2013-10-01

We study the dynamics of a local load-sharing fiber bundle model in two dimensions under an external load (which increases with time at a fixed slow rate) applied at a single point. Due to the local load-sharing nature, the redistributed load remains localized along the boundary of the broken patch. The system then goes to a self-organized state with a stationary average value of load per fiber along the (increasing) boundary of the broken patch (damaged region) and a scale-free distribution of avalanche sizes and other related quantities are observed. In particular, when the load redistribution is only among nearest surviving fiber(s), the numerical estimates of the exponent values are comparable with those of the Manna model. When the load redistribution is uniform along the patch boundary, the model shows a simple mean-field limit of this self-organizing critical behavior, for which we give analytical estimates of the saturation load per fiber values and avalanche size distribution exponent. These are in good agreement with numerical simulation results.

Quantitative analysis of volatile organic compounds using ion mobility spectra and cascade correlation neural networks

Science.gov (United States)

Harrington, Peter DEB.; Zheng, Peng

1995-01-01

Ion Mobility Spectrometry (IMS) is a powerful technique for trace organic analysis in the gas phase. Quantitative measurements are difficult, because IMS has a limited linear range. Factors that may affect the instrument response are pressure, temperature, and humidity. Nonlinear calibration methods, such as neural networks, may be ideally suited for IMS. Neural networks have the capability of modeling complex systems. Many neural networks suffer from long training times and overfitting. Cascade correlation neural networks train at very fast rates. They also build their own topology, that is a number of layers and number of units in each layer. By controlling the decay parameter in training neural networks, reproducible and general models may be obtained.

Neural stem cells encapsulated in a functionalized self-assembling peptide hydrogel for brain tissue engineering.

Science.gov (United States)

Cheng, Tzu-Yun; Chen, Ming-Hong; Chang, Wen-Han; Huang, Ming-Yuan; Wang, Tzu-Wei

2013-03-01

Brain injury is almost irreparable due to the poor regenerative capability of neural tissue. Nowadays, new therapeutic strategies have been focused on stem cell therapy and supplying an appropriate three dimensional (3D) matrix for the repair of injured brain tissue. In this study, we specifically linked laminin-derived IKVAV motif on the C-terminal to enrich self-assembling peptide RADA(16) as a functional peptide-based scaffold. Our purpose is providing a functional self-assembling peptide 3D hydrogel with encapsulated neural stem cells to enhance the reconstruction of the injured brain. The physiochemical properties reported that RADA(16)-IKVAV can self-assemble into nanofibrous morphology with bilayer β-sheet structure and become gelationed hydrogel with mechanical stiffness similar to brain tissue. The in vitro results showed that the extended IKVAV sequence can serve as a signal or guiding cue to direct the encapsulated neural stem cells (NSCs) adhesion and then towards neuronal differentiation. Animal study was conducted in a rat brain surgery model to demonstrate the damage in cerebral neocortex/neopallium loss. The results showed that the injected peptide solution immediately in situ formed the 3D hydrogel filling up the cavity and bridging the gaps. The histological analyses revealed the RADA(16)-IKVAV self-assembling peptide hydrogel not only enhanced survival of encapsulated NSCs but also reduced the formation of glial astrocytes. The peptide hydrogel with IKVAV extended motifs also showed the support of encapsulated NSCs in neuronal differentiation and the improvement in brain tissue regeneration after 6 weeks post-transplantation. Copyright © 2012 Elsevier Ltd. All rights reserved.

Identifying Emotions on the Basis of Neural Activation.

Directory of Open Access Journals (Sweden)

Karim S Kassam

Full Text Available We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1 neural activation of the same individual in other trials, 2 neural activation of other individuals who experienced similar trials, and 3 neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

Neural network applied to elemental archaeological Marajoara ceramic compositions

International Nuclear Information System (INIS)

Toyota, Rosimeiri G.; Munita, Casimiro S.; Boscarioli, Clodis

2009-01-01

In the last decades several analytical techniques have been used in archaeological ceramics studies. However, instrumental neutron activation analysis, INAA, employing gamma-ray spectrometry seems to be the most suitable technique because it is a simple analytical method in its purely instrumental form. The purpose of this work was to determine the concentration of Ce, Co, Cr, Cs, Eu, Fe, Hf, K, La, Lu, Na, Nd, Rb, Sb, Sc, Sm, Ta, Tb, Th, U, Yb, and Zn in 160 original marajoara ceramic fragments by INAA. Marajoara ceramics culture was sophisticated and well developed. This culture reached its peak during the V and XIV centuries in Marajo Island located on the Amazon River delta area in Brazil. The purpose of the quantitative data was to identify compositionally homogeneous groups within the database. Having this in mind, the data set was first converted to base-10 logarithms to compensate for the differences in magnitude between major elements and trace elements, and also to yield a closer to normal distribution for several trace elements. After that, the data were analyzed using the Mahalanobis distance and using the lambda Wilks as critical value to identify the outliers. The similarities among the samples were studied by means of cluster analysis, principal components analysis and discriminant analysis. Additional confirmation of these groups was made by using elemental concentration bivariate plots. The results showed that there were two very well defined groups in the data set. In addition, the database was studied using artificial neural network with unsupervised learning strategy known as self-organizing maps to classify the marajoara ceramics. The experiments carried out showed that self-organizing maps artificial neural network is capable of discriminating ceramic fragments like multivariate statistical methods, and, again the results showed that the database was formed by two groups. (author)

Explaining the “how” of self-esteem development : The self-organizing self-esteem model

NARCIS (Netherlands)

de Ruiter, Naomi M.P.; van Geert, Paul L.C.; Kunnen, E. Saskia

2017-01-01

The current article proposes a theoretical model of self-esteem called the Self-Organizing Self-Esteem (SOSE) model. The model provides an integrative framework for conceptualizing and understanding the intrinsic dynamics of self-esteem and the role of the context across 3 levels of development: The

Study on tube critical heat flux data treatment with artificial neural networks

International Nuclear Information System (INIS)

Han Lang; Shan Jianqiang

2005-01-01

Prediction of the Critical Heat Flux (CHF) are analyzed by Artificial Neural Networks (ANN) to a CHF database for upward flow of water in uniformly heated vertical round tubes. The analysis is performed with three viewpoints hypothesis, i.e. for fixed inlet condition, fixed exit condition and local condition. Half of 6941 from CHF database data is trained through ANN, the trained ANN predicts the total CHF data better than any other conventional correlations, showing RMS error of 6.6%, 10.39% and 21.39%, respectively. (author)

Self-organization in metal complexes

International Nuclear Information System (INIS)

Radecka-Paryzek, W.

1999-01-01

Inorganic self-organization involves the spontaneous generation of well-defined supramolecular architectures from metal ions and organic ligands. The basic concept of supramolecular chemistry is a molecular recognition. When the substrate are metal ions, recognition is expressed in the stability and selectivity of metal ion complexation by organic ligands and depends on the geometry of the ligand and on their binding sites that it contains. The combination of the geometric features of the ligand units and the coordination geometries of the metal ions provides very efficient tool for the synthesis of novel, intriguing and highly sophisticated species such as catenanes, box structures, double and triple helicates with a variety of interesting properties. The article will focus on the examples of inorganic self-organization involving the templating as a first step for the assembly of supramolecular structures of high complexity. (author)

Critical power for self-focusing of optical beam in absorbing media

Science.gov (United States)

Qi, Pengfei; Zhang, Lin; Lin, Lie; Zhang, Nan; Wang, Yan; Liu, Weiwei

2018-04-01

Self-focusing effects are of central importance for most nonlinear optical effects. The critical power for self-focusing is commonly investigated theoretically without considering a material’s absorption. Although this is practicable for various materials, investigating the critical power for self-focusing in media with non-negligible absorption is also necessary, because this is the situation usually met in practice. In this paper, the simple analytical expressions describing the relationships among incident power, absorption coefficient and focal position are provided by a simple physical model based on the Fermat principle. Expressions for the absorption dependent critical power are also derived; these can play important roles in experimental and applied research on self-focusing-related nonlinear optical phenomena in absorbing media. Numerical results, based on the nonlinear wave equation—and which can predict experimental results perfectly—are also presented, and agree quantitatively with the analytical results proposed in this paper.

APPLYING PRINCIPAL COMPONENT ANALYSIS, MULTILAYER PERCEPTRON AND SELF-ORGANIZING MAPS FOR OPTICAL CHARACTER RECOGNITION

Directory of Open Access Journals (Sweden)

Khuat Thanh Tung

2016-11-01

Full Text Available Optical Character Recognition plays an important role in data storage and data mining when the number of documents stored as images is increasing. It is expected to find the ways to convert images of typewritten or printed text into machine-encoded text effectively in order to support for the process of information handling effectively. In this paper, therefore, the techniques which are being used to convert image into editable text in the computer such as principal component analysis, multilayer perceptron network, self-organizing maps, and improved multilayer neural network using principal component analysis are experimented. The obtained results indicated the effectiveness and feasibility of the proposed methods.

Replicable Expansion and Differentiation of Neural Precursors from Adult Canine Skin

Directory of Open Access Journals (Sweden)

Thomas Duncan

2017-08-01

Full Text Available Repopulation of brain circuits by neural precursors is a potential therapeutic strategy for neurodegenerative disorders; however, choice of cell is critical. Previously, we introduced a two-step culture system that generates a high yield of neural precursors from small samples of adult canine skin. Here, we probe their gene and protein expression profiles in comparison with dermal fibroblasts and brain-derived neural stem cells and characterize their neuronal potential. To date, we have produced >50 skin-derived neural precursor (SKN lines. SKNs can be cultured in a highly replicable fashion and uniformly express a panel of identifying markers. Upon differentiation, they self-upregulate neural specification genes, generating neurons with basic electrophysiological functionality. This unique population of neural precursors, derived from mature skin, overcomes many of the practical issues that have limited clinical translation of alternative cell types. Easily accessible, neuronally committed, and patient specific, SKNs may have potential for the treatment of brain disorders.

Hierarchical self-organization of non-cooperating individuals.

Directory of Open Access Journals (Sweden)

Tamás Nepusz

Full Text Available Hierarchy is one of the most conspicuous features of numerous natural, technological and social systems. The underlying structures are typically complex and their most relevant organizational principle is the ordering of the ties among the units they are made of according to a network displaying hierarchical features. In spite of the abundant presence of hierarchy no quantitative theoretical interpretation of the origins of a multi-level, knowledge-based social network exists. Here we introduce an approach which is capable of reproducing the emergence of a multi-levelled network structure based on the plausible assumption that the individuals (representing the nodes of the network can make the right estimate about the state of their changing environment to a varying degree. Our model accounts for a fundamental feature of knowledge-based organizations: the less capable individuals tend to follow those who are better at solving the problems they all face. We find that relatively simple rules lead to hierarchical self-organization and the specific structures we obtain possess the two, perhaps most important features of complex systems: a simultaneous presence of adaptability and stability. In addition, the performance (success score of the emerging networks is significantly higher than the average expected score of the individuals without letting them copy the decisions of the others. The results of our calculations are in agreement with a related experiment and can be useful from the point of designing the optimal conditions for constructing a given complex social structure as well as understanding the hierarchical organization of such biological structures of major importance as the regulatory pathways or the dynamics of neural networks.

Unraveling atomic-level self-organization at the plasma-material interface

Science.gov (United States)

Allain, J. P.; Shetty, A.

2017-07-01

The intrinsic dynamic interactions at the plasma-material interface and critical role of irradiation-driven mechanisms at the atomic scale during exposure to energetic particles require a priori the use of in situ surface characterization techniques. Characterization of ‘active’ surfaces during modification at atomic-scale levels is becoming more important as advances in processing modalities are limited by an understanding of the behavior of these surfaces under realistic environmental conditions. Self-organization from exposure to non-equilibrium and thermalized plasmas enable dramatic control of surface morphology, topography, composition, chemistry and structure yielding the ability to tune material properties with an unprecedented level of control. Deciphering self-organization mechanisms of nanoscale morphology (e.g. nanodots, ripples) and composition on a variety of materials including: compound semiconductors, semiconductors, ceramics, polymers and polycrystalline metals via low-energy ion-beam assisted plasma irradiation are critical to manipulate functionality in nanostructured systems. By operating at ultra-low energies near the damage threshold, irradiation-driven defect engineering can be optimized and surface-driven mechanisms controlled. Tunability of optical, electronic, magnetic and bioactive properties is realized by reaching metastable phases controlled by atomic-scale irradiation-driven mechanisms elucidated by novel in situ diagnosis coupled to atomistic-level computational tools. Emphasis will be made on tailored surface modification from plasma-enhanced environments on particle-surface interactions and their subsequent modification of hard and soft matter interfaces. In this review, we examine current trends towards in situ and in operando surface and sub-surface characterization to unravel atomic-scale mechanisms at the plasma-material interface. This work will emphasize on recent advances in the field of plasma and ion

Self-organizing networks

DEFF Research Database (Denmark)

Marchetti, Nicola; Prasad, Neeli R.; Johansson, Johan

2010-01-01

In this paper, a general overview of Self-Organizing Networks (SON), and the rationale and state-of-the-art of wireless SON are first presented. The technical and business requirements are then briefly treated, and the research challenges within the field of SON are highlighted. Thereafter, the r...

Self-organization processes and nanocluster formation in crystal lattices by low-energy ion irradiation

International Nuclear Information System (INIS)

Tereshko, I.; Abidzina, V.; Tereshko, A.; Glushchenko, V.; Elkin, I.

2007-01-01

The goal of this paper is to study self-organization processes that cause nanostructural evolution in nonlinear crystal media. The subjects of the investigation were nonlinear homogeneous and heterogeneous atom chains. The method of computer simulation was used to investigate the interaction between low-energy ions and crystal lattices. It was based on the conception of three-dimensional lattice as a nonlinear atom chain system. We showed that that in homogeneous atom chains critical energy needed for self-organization processes development is less than for nonlinear atom chain with already embedded clusters. The possibility of nanostructure formation was studied by a molecular dynamics method of nonlinear oscillations in atomic oscillator systems of crystal lattices after their low-energy ion irradiation. (authors)

Prediction of ozone tropospheric degradation rate constant of organic compounds by using artificial neural networks

International Nuclear Information System (INIS)

Fatemi, M.H.

2006-01-01

Ozone tropospheric degradation of organic compound is very important in environmental chemistry. The lifetime of organic chemicals in the atmosphere can be calculated from the knowledge of the rate constant of their reaction with free radicals such as OH and NO 3 or O 3 . In the present work, the rate constant for the tropospheric degradation of 137 organic compounds by reaction with ozone, the least widely and successfully modeled degradation process, are predicted by quantitative structure activity relationships modeling based on a variety of theoretical descriptors, which screened and selected by genetic algorithm variable subset selection procedure. These descriptors which can be used as inputs for generated artificial neural networks are; HOMO-LUMO gap, number of double bonds, number of single bonds, maximum net charge on C atom, minimum (>0.1) bond order of C atom and Minimum e-e repulsion of H atom. After generation, optimization and training of artificial neural network, network was used for the prediction of log KO 3 for the validation set. The root mean square error for the neural network calculated log KO 3 for training, prediction and validation set are 0.357, 0.460 and 0.481, respectively, which are smaller than those obtained by multiple linear regressions model (1.217, 0.870 and 0.968, respectively). Results obtained reveal the reliability and good predictivity of neural network model for the prediction of ozone tropospheric degradations rate constant of organic compounds

Self-organizing periodicity in development: organ positioning in plants.

Science.gov (United States)

Bhatia, Neha; Heisler, Marcus G

2018-02-08

Periodic patterns during development often occur spontaneously through a process of self-organization. While reaction-diffusion mechanisms are often invoked, other types of mechanisms that involve cell-cell interactions and mechanical buckling have also been identified. Phyllotaxis, or the positioning of plant organs, has emerged as an excellent model system to study the self-organization of periodic patterns. At the macro scale, the regular spacing of organs on the growing plant shoot gives rise to the typical spiral and whorled arrangements of plant organs found in nature. In turn, this spacing relies on complex patterns of cell polarity that involve feedback between a signaling molecule - the plant hormone auxin - and its polar, cell-to-cell transport. Here, we review recent progress in understanding phyllotaxis and plant cell polarity and highlight the development of new tools that can help address the remaining gaps in our understanding. © 2018. Published by The Company of Biologists Ltd.

Domination, self-determination and circular organizing

NARCIS (Netherlands)

Romme, A.G.L.

2002-01-01

The emergence of self-organizing forms of control, based on the idea of self-determination, have challenged traditional forms of control based on the concept of domination. As such, self-determination has been put forward as an alternative rather than as a complement to domination. This paper

Fault tolerance of artificial neural networks with applications in critical systems

Science.gov (United States)

Protzel, Peter W.; Palumbo, Daniel L.; Arras, Michael K.

1992-01-01

This paper investigates the fault tolerance characteristics of time continuous recurrent artificial neural networks (ANN) that can be used to solve optimization problems. The principle of operations and performance of these networks are first illustrated by using well-known model problems like the traveling salesman problem and the assignment problem. The ANNs are then subjected to 13 simultaneous 'stuck at 1' or 'stuck at 0' faults for network sizes of up to 900 'neurons'. The effects of these faults is demonstrated and the cause for the observed fault tolerance is discussed. An application is presented in which a network performs a critical task for a real-time distributed processing system by generating new task allocations during the reconfiguration of the system. The performance degradation of the ANN under the presence of faults is investigated by large-scale simulations, and the potential benefits of delegating a critical task to a fault tolerant network are discussed.

Cellular Neural Network Method for Critical Slab with Albedo Boundary Condition

International Nuclear Information System (INIS)

Pirouzmanda, A.; Hadada, K.; Suh, K. Y.

2010-01-01

The neutron transport problems have been studied theoretically and numerically for years. A number of researchers have studied the criticality problems of one-speed neutrons in homogeneous slabs and spheres using various methods. The Chebyshev polynomial approximation method (T N method) has lately been developed and improved for the neutron transport equation in slab geometry. The one-speed time-dependent neutron transport equation using the Cellular Neural Network (CNN) for the vacuum boundary condition has previously been solved. In this paper, we demonstrate the capacity of CNN in calculating the critical slab thickness for different boundary conditions and its variation with moments N. The architecture of the CNN has already been dealt with thoroughly. Essentially, the CNN is used to model a first-order system of the partial differential equations (PDEs). The original equations in the T N approximation are also a set of PDEs. The CNN approach lends itself to analog VLSI implementation. In this study, the CNN model is implemented using the HSpice software package

To the problem of improving normative legal basis of organization of local self-government in Russia

Directory of Open Access Journals (Sweden)

Olga Bazhenova

2017-01-01

Full Text Available The subject. The crisis of local self-government actualizes the problem of effectiveness oflegal regulation of the issues of its organization.The purpose of the paper is evaluation of the effectiveness of the regulatory framework forthe organization of local government.The methodology of research includes systematic analysis, formal legal method, interpretationof legislation.The results and scope of application. The crisis of local self-government actualizes the problemof effectiveness of legal regulation of the issues of its organization. Evaluation of theeffectiveness of the regulatory framework for the organization of local government is reducedto two questions: what are the limits of state legal regulation and what are the beginningof the division of powers on the organization of local government between the RussianFederation and the subjects of the Russian Federation.Recognizing the optimal legislative approach to the definition of the limits of state regulation,which assumes the creation at the federal level of a full-fledged legal mechanism for the implementationof local self-government, subject to its combination with the beginning of municipalself-regulation, the author criticizes the legislative approach to delineating the powersto organize local self-government between the Federation and the subjects of the Federation.Conclusions. Due to the legislative formula, according to which the scope of regional powersdepends on the discretion of the federal legislator, the local self-government turned out tobe "hostage" to the emerging federal relations.

Real-time neural network-based self-tuning control of a nonlinear electro-hydraulic servomotor

Energy Technology Data Exchange (ETDEWEB)

Canelon, J.I.; Ortega, A.G. [Univ. del Zulia, Maracaibo, Zulia (Venezuela, Bolivarian Republic of). School of Electrical Engineering; Shieh, L.S. [Houston Univ., Houston, TX (United States). Dept. of Electrical and Computer Engineering; Bastidas, J.I. [Univ. del Zulia, Maracaibo, Zulia (Venezuela, Bolivarian Republic of). School of Mechanical Engineering; Zhang, Y.; Akujuobi, C.M. [Prairie View A and M Univ., Prairie View, TX (United States). Center of Excellence for Communication Systems Technology Research and Dept. of Engineering Technology

2010-08-13

For high power applications, hydraulic actuators offer many advantages over electromagnetic actuators, including higher torque/mass ratios; smaller control gains; excellent torque capability; filtered high frequency noise; better heat transfer characteristics; smaller size; higher speed of response of the servomechanism; cheaper hardware; and higher reliability. Therefore, any application that requires a large force applied smoothly by an actuator is a candidate for hydraulic power. Examples of such applications include vehicle steering and braking systems; roll mills; drilling rigs; heavy duty crane and presses; and industrial robots and actuators for aircraft control surfaces such as ailerons and flaps. It is extremely important to create effective control strategies for hydraulic systems. This paper outlined the real-time implementation of a neural network-based approach, for self-tuning control of the angular position of a nonlinear electro-hydraulic servomotor. Using an online training algorithm, a neural network autoregressive moving-average model with exogenous input (ARMAX) model of the system was identified and continuously updated and an optimal linear ARMAX model was determined. The paper briefly depicted the neural network-based self-tuning control approach and a description of the experimental equipment (hardware and software) was presented including the implementation details. The experimental results were discussed and conclusions were summarized. It was found that the approach proved to be very effective in the control of this fast dynamics system, outperforming a fine tuned PI controller. Therefore, although the self-tuning approach was computationally demanding, it was feasible for real-time implementation. 22 refs., 6 figs.

10th Workshop on Self-Organizing Maps

CERN Document Server

Schleif, Frank-Michael; Kaden, Marika; Lange, Mandy

2014-01-01

The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first WSOM-workshop 1997 in Helsinki this workshop focuses on newest results in the field of supervised and unsupervised vector quantization like self-organizing maps for data mining and data classification.   This 10th WSOM brought together more than 50 researchers, experts and practitioners in the beautiful small town Mittweida in Saxony (Germany) nearby the mountains Erzgebirge to discuss new developments in the field of unsupervised self-organizing vector quantization systems and learning vector quantization approaches for classification. The book contains the accepted papers of the workshop after a careful review process as well as summaries of the invited talks.   Among these book chapters there are excellent examples of the use of self-organizing maps in agriculture, ...

A self-organized learning strategy for object recognition by an embedded line of attraction

Science.gov (United States)

Seow, Ming-Jung; Alex, Ann T.; Asari, Vijayan K.

2012-04-01

For humans, a picture is worth a thousand words, but to a machine, it is just a seemingly random array of numbers. Although machines are very fast and efficient, they are vastly inferior to humans for everyday information processing. Algorithms that mimic the way the human brain computes and learns may be the solution. In this paper we present a theoretical model based on the observation that images of similar visual perceptions reside in a complex manifold in an image space. The perceived features are often highly structured and hidden in a complex set of relationships or high-dimensional abstractions. To model the pattern manifold, we present a novel learning algorithm using a recurrent neural network. The brain memorizes information using a dynamical system made of interconnected neurons. Retrieval of information is accomplished in an associative sense. It starts from an arbitrary state that might be an encoded representation of a visual image and converges to another state that is stable. The stable state is what the brain remembers. In designing a recurrent neural network, it is usually of prime importance to guarantee the convergence in the dynamics of the network. We propose to modify this picture: if the brain remembers by converging to the state representing familiar patterns, it should also diverge from such states when presented with an unknown encoded representation of a visual image belonging to a different category. That is, the identification of an instability mode is an indication that a presented pattern is far away from any stored pattern and therefore cannot be associated with current memories. These properties can be used to circumvent the plasticity-stability dilemma by using the fluctuating mode as an indicator to create new states. We capture this behavior using a novel neural architecture and learning algorithm, in which the system performs self-organization utilizing a stability mode and an instability mode for the dynamical system. Based

Self-Organization Activities of College Students: Challenges and Opportunities

Science.gov (United States)

Shmurygina, Natalia; Bazhenova, Natalia; Bazhenov, Ruslan; Nikolaeva, Natalia; Tcytcarev, Andrey

2016-01-01

The article provides the analysis of self-organization activities of college students related to their participation in youth associations activities. The purpose of research is to disclose a degree of students' activities demonstration based on self-organization processes, assessment of existing self-organization practices of the youth,…

SA72. Neural Correlates of Self-Reflection in Schizophrenia: A Functional Magnetic Resonance Imaging Study

Science.gov (United States)

Hiremath, Chaitra; Dey, Avyarthana

2017-01-01

Abstract Background: Self-reflection is the process of conscious evaluation of one’s traits, abilities, and attitudes. Deficient self-reflective processes might underlie lack of insight into schizophrenia. The limited research literature on the neural correlates of self-reflection in schizophrenia is inconclusive. In this study, we investigated the neural correlates of self-reflection in schizophrenia patients attending a tertiary care hospital in India. Methods: Nineteen male schizophrenia patients (mean age = 32.68 ± 7.11, mean years of education =15.21 ± 1.93) and 19 male healthy controls (mean age = 26.96 ± 4.67, mean years of education = 18.11 ± 3.13) participated in the study. Participants performed a previously validated self-reflection task while undergoing functional magnetic resonance imaging (fMRI; 3-Tesla). The task comprised of 144 words subdivided into 4 domains: Self-reflection, Other-reflection, Affect labeling, and Perceptual. The task was presented as 3 runs of 8 blocks each. The images were preprocessed and analyzed using SPM-12. After preprocessing, contrasts comparing Self-reflection with the other domains were modeled at the individual subject level. In second-level analysis, the first-level contrasts were entered into a 2-sample t test to compare patient and healthy control groups. The results were thresholded at P Self-reflection > Other-reflection contrast, schizophrenia patients demonstrated greater activation of right and left superior parietal lobules (BA 5 and 7), right inferior parietal lobule (BA 39), left parahippocampal gyrus (BA 36), and left premotor cortex (BA 6). For the Self-reflection > Affect labeling contrast, patients showed greater activation of precuneus (BA 7) and right inferior occipital gyrus (BA 19), and lesser activation of left inferior frontal gyrus (BA 45 and 47). And for the Self-reflection > Perceptual contrast, patients showed greater activation of left middle frontal gyrus (BA 10

Cognitive control in adolescence: neural underpinnings and relation to self-report behaviors.

Directory of Open Access Journals (Sweden)

Jessica R Andrews-Hanna

Full Text Available Adolescence is commonly characterized by impulsivity, poor decision-making, and lack of foresight. However, the developmental neural underpinnings of these characteristics are not well established.To test the hypothesis that these adolescent behaviors are linked to under-developed proactive control mechanisms, the present study employed a hybrid block/event-related functional Magnetic Resonance Imaging (fMRI Stroop paradigm combined with self-report questionnaires in a large sample of adolescents and adults, ranging in age from 14 to 25. Compared to adults, adolescents under-activated a set of brain regions implicated in proactive top-down control across task blocks comprised of difficult and easy trials. Moreover, the magnitude of lateral prefrontal activity in adolescents predicted self-report measures of impulse control, foresight, and resistance to peer pressure. Consistent with reactive compensatory mechanisms to reduced proactive control, older adolescents exhibited elevated transient activity in regions implicated in response-related interference resolution.Collectively, these results suggest that maturation of cognitive control may be partly mediated by earlier development of neural systems supporting reactive control and delayed development of systems supporting proactive control. Importantly, the development of these mechanisms is associated with cognitive control in real-life behaviors.

Flattening the organization: implementing self-directed work groups.

Science.gov (United States)

Brandon, G M

1996-01-01

In response to tremendous growth of managed care and threats to financial stability and job security, the Greater Baltimore Medical Center (GBMC) restructured itself into independent business units. The radiology department at GBMC resolved to reduce cost per unit-of-service, improve service, determine optimal staffing levels and reduce the number of layers of organization. It was decided to achieve those goals by implementing self-directed work groups. Staff buy-in was critical to success of the project. To begin, the staff was educated intensively about current trends in healthcare, managed care and potential changes in the job market. The radiology department was allowed to reduce the size of its staff through attrition and worked hard to focus staff concern on the impact each individual could have on the bottom line and the resultant effect on job security. Self-directed work groups were designed on a matrix that used small "service teams" in combinations to form larger "work groups." Actual work and daily activities occur at the service team level; information exchange and major decisions occue at the work group level. Seventeen months after beginning the project and 10 months after implementation, the organization has flattened, staff members have adjusted well to new roles, there have been no lay-offs, and the matrix system of small and large groups have proved particularly valuable.

Neural responses to feedback information produced by self-generated or other-generated decision-making and their impairment in schizophrenia.

Science.gov (United States)

Toyomaki, Atsuhito; Hashimoto, Naoki; Kako, Yuki; Murohashi, Harumitsu; Kusumi, Ichiro

2017-01-01

Several studies of self-monitoring dysfunction in schizophrenia have focused on the sense of agency to motor action using behavioral and psychophysiological techniques. So far, no study has ever tried to investigate whether the sense of agency or causal attribution for external events produced by self-generated decision-making is abnormal in schizophrenia. The purpose of this study was to investigate neural responses to feedback information produced by self-generated or other-generated decision-making in a multiplayer gambling task using even-related potentials and electroencephalogram synchronization. We found that the late positive component and theta/alpha synchronization were increased in response to feedback information in the self-decision condition in normal controls, but that these responses were significantly decreased in patients with schizophrenia. These neural activities thus reflect the self-reference effect that affects the cognitive appraisal of external events following decision-making and their impairment in schizophrenia.

Soft-Cliff Retreat, Self-Organized Critical Phenomena in the Limit of Predictability?

Science.gov (United States)

Paredes, Carlos; Godoy, Clara; Castedo, Ricardo

2015-03-01

The coastal erosion along the world's coastlines is a natural process that occurs through the actions of marine and subaerial physico-chemical phenomena, waves, tides, and currents. The development of cliff erosion predictive models is limited due to the complex interactions between environmental processes and material properties over a wide range of temporal and spatial scales. As a result of this erosive action, gravity driven mass movements occur and the coastline moves inland. Like other studied earth natural and synthetically modelled phenomena characterized as self-organized critical (SOC), the recession of the cliff has a seemingly random, sporadic behavior, with a wide range of yearly recession rate values probabilistically distributed by a power-law. Usually, SOC systems are defined by a number of scaling features in the size distribution of its parameters and on its spatial and/or temporal pattern. Particularly, some previous studies of derived parameters from slope movements catalogues, have allowed detecting certain SOC features in this phenomenon, which also shares the recession of cliffs. Due to the complexity of the phenomenon and, as for other natural processes, there is no definitive model of recession of coastal cliffs. In this work, various analysis techniques have been applied to identify SOC features in the distribution and pattern to a particular case: the Holderness shoreline. This coast is a great case study to use when examining coastal processes and the structures associated with them. It is one of World's fastest eroding coastlines (2 m/yr in average, max observed 22 m/yr). Cliffs, ranging from 2 m up to 35 m in height, and made up of glacial tills, mainly compose this coast. It is this soft boulder clay that is being rapidly eroded and where coastline recession measurements have been recorded by the Cliff Erosion Monitoring Program (East Riding of Yorkshire Council, UK). The original database has been filtered by grouping contiguous

Criticality predicts maximum irregularity in recurrent networks of excitatory nodes.

Directory of Open Access Journals (Sweden)

Yahya Karimipanah

Full Text Available A rigorous understanding of brain dynamics and function requires a conceptual bridge between multiple levels of organization, including neural spiking and network-level population activity. Mounting evidence suggests that neural networks of cerebral cortex operate at a critical regime, which is defined as a transition point between two phases of short lasting and chaotic activity. However, despite the fact that criticality brings about certain functional advantages for information processing, its supporting evidence is still far from conclusive, as it has been mostly based on power law scaling of size and durations of cascades of activity. Moreover, to what degree such hypothesis could explain some fundamental features of neural activity is still largely unknown. One of the most prevalent features of cortical activity in vivo is known to be spike irregularity of spike trains, which is measured in terms of the coefficient of variation (CV larger than one. Here, using a minimal computational model of excitatory nodes, we show that irregular spiking (CV > 1 naturally emerges in a recurrent network operating at criticality. More importantly, we show that even at the presence of other sources of spike irregularity, being at criticality maximizes the mean coefficient of variation of neurons, thereby maximizing their spike irregularity. Furthermore, we also show that such a maximized irregularity results in maximum correlation between neuronal firing rates and their corresponding spike irregularity (measured in terms of CV. On the one hand, using a model in the universality class of directed percolation, we propose new hallmarks of criticality at single-unit level, which could be applicable to any network of excitable nodes. On the other hand, given the controversy of the neural criticality hypothesis, we discuss the limitation of this approach to neural systems and to what degree they support the criticality hypothesis in real neural networks. Finally

Complex Systems and Self-organization Modelling

CERN Document Server

Bertelle, Cyrille; Kadri-Dahmani, Hakima

2009-01-01

The concern of this book is the use of emergent computing and self-organization modelling within various applications of complex systems. The authors focus their attention both on the innovative concepts and implementations in order to model self-organizations, but also on the relevant applicative domains in which they can be used efficiently. This book is the outcome of a workshop meeting within ESM 2006 (Eurosis), held in Toulouse, France in October 2006.

The neural signature of self-concept development in adolescence: The role of domain and valence distinctions

Directory of Open Access Journals (Sweden)

R. van der Cruijsen

2018-04-01

Full Text Available Neuroimaging studies in adults showed that cortical midline regions including medial prefrontal cortex (mPFC and posterior parietal cortex (PPC are important in self-evaluations. The goals of this study were to investigate the contribution of these regions to self-evaluations in late childhood, adolescence, and early adulthood, and to examine whether these differed per domain (academic, physical and prosocial and valence (positive versus negative. Also, we tested whether this activation changes across adolescence. For this purpose, participants between ages 11–21-years (N = 150 evaluated themselves on trait sentences in an fMRI session. Behaviorally, adolescents rated their academic traits less positively than children and young adults. The neural analyses showed that evaluating self-traits versus a control condition was associated with increased activity in mPFC (domain-general effect, and positive traits were associated with increased activity in ventral mPFC (valence effect. Self-related mPFC activation increased linearly with age, but only for evaluating physical traits. Furthermore, an adolescent-specific decrease in striatum activation for positive self traits was found. Finally, we found domain-specific neural activity for evaluating traits in physical (dorsolateral PFC, dorsal mPFC and academic (PPC domains. Together, these results highlight the importance of domain distinctions when studying self-concept development in late childhood, adolescence, and early adulthood. Keywords: Self, fMRI, Adolescence, Development, Medial prefrontal cortex, Self-concept

Self-Organized Construction with Continuous Building Material

DEFF Research Database (Denmark)

Heinrich, Mary Katherine; Wahby, Mostafa; Divband Soorati, Mohammad

2016-01-01

Self-organized construction with continuous, structured building material, as opposed to modular units, offers new challenges to the robot-based construction process and lends the opportunity for increased flexibility in constructed artifact properties, such as shape and deformation. As an example...... investigation, we look at continuous filaments organized into braided structures, within the context of bio-hybrids constructing architectural artifacts. We report the result of an early swarm robot experiment. The robots successfully constructed a braid in a self-organized process. The construction process can...... be extended by using different materials and by embedding sensors during the self-organized construction directly into the braided structure. In future work, we plan to apply dedicated braiding robot hardware and to construct sophisticated 3-d structures with local variability in patterns of filament...

Modeling self-organization of novel organic materials

Science.gov (United States)

Sayar, Mehmet

In this thesis, the structural organization of oligomeric multi-block molecules is analyzed by computational analysis of coarse-grained models. These molecules form nanostructures with different dimensionalities, and the nanostructured nature of these materials leads to novel structural properties at different length scales. Previously, a number of oligomeric triblock rodcoil molecules have been shown to self-organize into mushroom shaped noncentrosymmetric nanostructures. Interestingly, thin films of these molecules contain polar domains and a finite macroscopic polarization. However, the fully polarized state is not the equilibrium state. In the first chapter, by solving a model with dipolar and Ising-like short range interactions, we show that polar domains are stable in films composed of aggregates as opposed to isolated molecules. Unlike classical molecular systems, these nanoaggregates have large intralayer spacings (a ≈ 6 nm), leading to a reduction in the repulsive dipolar interactions that oppose polar order within layers. This enables the formation of a striped pattern with polar domains of alternating directions. The energies of the possible structures at zero temperature are computed exactly and results of Monte Carlo simulations are provided at non-zero temperatures. In the second chapter, the macroscopic polarization of such nanostructured films is analyzed in the presence of a short range surface interaction. The surface interaction leads to a periodic domain structure where the balance between the up and down domains is broken, and therefore films of finite thickness have a net macroscopic polarization. The polarization per unit volume is a function of film thickness and strength of the surface interaction. Finally, in chapter three, self-organization of organic molecules into a network of one dimensional objects is analyzed. Multi-block organic dendron rodcoil molecules were found to self-organize into supramolecular nanoribbons (threads) and

Protein Kinase-A Inhibition Is Sufficient to Support Human Neural Stem Cells Self-Renewal.

Science.gov (United States)

Georges, Pauline; Boissart, Claire; Poulet, Aurélie; Peschanski, Marc; Benchoua, Alexandra

2015-12-01

Human pluripotent stem cell-derived neural stem cells offer unprecedented opportunities for producing specific types of neurons for several biomedical applications. However, to achieve it, protocols of production and amplification of human neural stem cells need to be standardized, cost effective, and safe. This means that small molecules should progressively replace the use of media containing cocktails of protein-based growth factors. Here we have conducted a phenotypical screening to identify pathways involved in the regulation of hNSC self-renewal. We analyzed 80 small molecules acting as kinase inhibitors and identified compounds of the 5-isoquinolinesulfonamide family, described as protein kinase A (PKA) and protein kinase G inhibitors, as candidates to support hNSC self-renewal. Investigating the mode of action of these compounds, we found that modulation of PKA activity was central in controlling the choice between self-renewal or terminal neuronal differentiation of hNSC. We finally demonstrated that the pharmacological inhibition of PKA using the small molecule HA1004 was sufficient to support the full derivation, propagation, and long-term maintenance of stable hNSC in absence of any other extrinsic signals. Our results indicated that tuning of PKA activity is a core mechanism regulating hNSC self-renewal and differentiation and delineate the minimal culture media requirement to maintain undifferentiated hNSC in vitro. © 2015 AlphaMed Press.

The role of intergenerational similarity and parenting in adolescent self-criticism: An actor-partner interdependence model.

Science.gov (United States)

Bleys, Dries; Soenens, Bart; Boone, Liesbet; Claes, Stephan; Vliegen, Nicole; Luyten, Patrick

2016-06-01

Research investigating the development of adolescent self-criticism has typically focused on the role of either parental self-criticism or parenting. This study used an actor-partner interdependence model to examine an integrated theoretical model in which achievement-oriented psychological control has an intervening role in the relation between parental and adolescent self-criticism. Additionally, the relative contribution of both parents and the moderating role of adolescent gender were examined. Participants were 284 adolescents (M = 14 years, range = 12-16 years) and their parents (M = 46 years, range = 32-63 years). Results showed that only maternal self-criticism was directly related to adolescent self-criticism. However, both parents' achievement-oriented psychological control had an intervening role in the relation between parent and adolescent self-criticism in both boys and girls. Moreover, one parent's achievement-oriented psychological control was not predicted by the self-criticism of the other parent. Copyright © 2016 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Neural networks underlying language and social cognition during self-other processing in Autism spectrum disorders.

Science.gov (United States)

Kana, Rajesh K; Sartin, Emma B; Stevens, Carl; Deshpande, Hrishikesh D; Klein, Christopher; Klinger, Mark R; Klinger, Laura Grofer

2017-07-28

The social communication impairments defining autism spectrum disorders (ASD) may be built upon core deficits in perspective-taking, language processing, and self-other representation. Self-referential processing entails the ability to incorporate self-awareness, self-judgment, and self-memory in information processing. Very few studies have examined the neural bases of integrating self-other representation and semantic processing in individuals with ASD. The main objective of this functional MRI study is to examine the role of language and social brain networks in self-other processing in young adults with ASD. Nineteen high-functioning male adults with ASD and 19 age-sex-and-IQ-matched typically developing (TD) control participants made "yes" or "no" judgments of whether an adjective, presented visually, described them (self) or their favorite teacher (other). Both ASD and TD participants showed significantly increased activity in the medial prefrontal cortex (MPFC) during self and other processing relative to letter search. Analyses of group differences revealed significantly reduced activity in left inferior frontal gyrus (LIFG), and left inferior parietal lobule (LIPL) in ASD participants, relative to TD controls. ASD participants also showed significantly weaker functional connectivity of the anterior cingulate cortex (ACC) with several brain areas while processing self-related words. The LIFG and IPL are important regions functionally at the intersection of language and social roles; reduced recruitment of these regions in ASD participants may suggest poor level of semantic and social processing. In addition, poor connectivity of the ACC may suggest the difficulty in meeting the linguistic and social demands of this task in ASD. Overall, this study provides new evidence of the altered recruitment of the neural networks underlying language and social cognition in ASD. Published by Elsevier Ltd.

Quantum self-organization and nuclear collectivities

Science.gov (United States)

Otsuka, T.; Tsunoda, Y.; Togashi, T.; Shimizu, N.; Abe, T.

2018-02-01

The quantum self-organization is introduced as one of the major underlying mechanisms of the quantum many-body systems. In the case of atomic nuclei as an example, two types of the motion of nucleons, single-particle states and collective modes, dominate the structure of the nucleus. The outcome of the collective mode is determined basically by the balance between the effect of the mode-driving force (e.g., quadrupole force for the ellipsoidal deformation) and the resistance power against it. The single-particle energies are one of the sources to produce such resistance power: a coherent collective motion is more hindered by larger gaps between relevant single particle states. Thus, the single-particle state and the collective mode are “enemies” each other. However, the nuclear forces are demonstrated to be rich enough so as to enhance relevant collective mode by reducing the resistance power by changing singleparticle energies for each eigenstate through monopole interactions. This will be verified with the concrete example taken from Zr isotopes. Thus, when the quantum self-organization occurs, single-particle energies can be self-organized, being enhanced by (i) two quantum liquids, e.g., protons and neutrons, (ii) two major force components, e.g., quadrupole interaction (to drive collective mode) and monopole interaction (to control resistance). In other words, atomic nuclei are not necessarily like simple rigid vases containing almost free nucleons, in contrast to the naïve Fermi liquid picture. Type II shell evolution is considered to be a simple visible case involving excitations across a (sub)magic gap. The quantum self-organization becomes more important in heavier nuclei where the number of active orbits and the number of active nucleons are larger. The quantum self-organization is a general phenomenon, and is expected to be found in other quantum systems.

Involvement of extracellular factors in maintaining self-renewal of neural stem cell by nestin.

Science.gov (United States)

Di, Chun Guang; Xiang, Andy Peng; Jia, Lei; Liu, Jun Feng; Lahn, Bruce T; Ma, Bao Feng

2014-07-09

Nestin knockout leads to embryonic lethality and self-renewal deficiency in neural stem cells (NSCs). However, how nestin maintains self-renewal remains uncertain. Here, we used the dosage effect of nestin in heterozygous mice (Nes+/-) to study self-renewal of NSCs. With existing extracellular signaling in vivo or in vitro, nestin levels do not affect proliferation ability or apoptosis when compared between Nes+/- and Nes+/+ NSCs. However, self-renewal ability of Nes+/- NSCs is impaired when plated at a low cell density and completely lost at a clonal density. This deficiency in self-renewal at a clonal density is rescued using a medium conditioned by Nes+/+ NSCs. In addition, the Akt signaling pathway is altered at low density and reversed by conditioned medium. Our data show that secreted factors contribute toward maintaining self-renewal of NSCs by nestin, potentially through Akt signaling.

Comparison between genetic algorithm and self organizing map to detect botnet network traffic

Science.gov (United States)

Yugandhara Prabhakar, Shinde; Parganiha, Pratishtha; Madhu Viswanatham, V.; Nirmala, M.

2017-11-01

In Cyber Security world the botnet attacks are increasing. To detect botnet is a challenging task. Botnet is a group of computers connected in a coordinated fashion to do malicious activities. Many techniques have been developed and used to detect and prevent botnet traffic and the attacks. In this paper, a comparative study is done on Genetic Algorithm (GA) and Self Organizing Map (SOM) to detect the botnet network traffic. Both are soft computing techniques and used in this paper as data analytics system. GA is based on natural evolution process and SOM is an Artificial Neural Network type, uses unsupervised learning techniques. SOM uses neurons and classifies the data according to the neurons. Sample of KDD99 dataset is used as input to GA and SOM.

Relativistic fluid theories - Self organization

International Nuclear Information System (INIS)

Mahajan, S.M.; Hazeltine, R.D.; Yoshida, Z.

2003-01-01

Developments in two distinct but related subjects are reviewed: 1) Formulation and investigation of closed fluid theories which transcend the limitations of standard magnetohydrodynamics (MHD), in particular, theories which are valid in the long mean free path limit and in which pressure anisotropy, heat flow, and arbitrarily strong sheared flows are treated consistently, and 2) Exploitation of the two-fluid theories to derive new plasma configurations in which the flow-field is a co-determinant of the overall dynamics; some of these states belong to the category of self-organized relaxed states. Physical processes which may provide a route to self-organization and complexity are also explored. (author)

Love flows downstream: mothers' and children's neural representation similarity in perceiving distress of self and family.

Science.gov (United States)

Lee, Tae-Ho; Qu, Yang; Telzer, Eva H

2017-12-01

The current study aimed to capture empathy processing in an interpersonal context. Mother-adolescent dyads (N = 22) each completed an empathy task during fMRI, in which they imagined the target person in distressing scenes as either themselves or their family (i.e. child for the mother, mother for the child). Using multi-voxel pattern approach, we compared neural pattern similarity for the self and family conditions and found that mothers showed greater perceptual similarity between self and child in the fusiform face area (FFA), representing high self-child overlap, whereas adolescents showed significantly less self-mother overlap. Adolescents' pattern similarity was dependent upon family relationship quality, such that they showed greater self-mother overlap with higher relationship quality, whereas mothers' pattern similarity was independent of relationship quality. Furthermore, adolescents' perceptual similarity in the FFA was associated with increased social brain activation (e.g. temporal parietal junction). Mediation analyses indicated that high relationship quality was associated with greater social brain activation, which was mediated by greater self-mother overlap in the FFA. Our findings suggest that adolescents show more distinct neural patterns in perceiving their own vs their mother's distress, and such distinction is sensitive to mother-child relationship quality. In contrast, mothers' perception for their own and child's distress is highly similar and unconditional. © The Author (2017). Published by Oxford University Press.

PERSONALITY PREDISPOSITIONS IN CHINESE ADOLESCENTS: THE RELATION BETWEEN SELF-CRITICISM, DEPENDENCY, AND PROSPECTIVE INTERNALIZING SYMPTOMS

Science.gov (United States)

Cohen, Joseph R.; Young, Jami F.; Hankin, Benjamin L.; Yao, Shuqiao; Zhu, Xiong Zhao; Abela, John R.Z.

2015-01-01

The present study examined the prospective relation between two personality predispositions, self-criticism and dependency, and internalizing symptoms. Specifically, it was examined whether self-criticism and dependency predicted symptoms of depression and social anxiety, and if a moderation (e.g. diathesis-stress) or mediation model best explained the relation between the personality predispositions and emotional distress in Chinese adolescents. Participants included 1,150 adolescents (597 females and 553 males) from mainland China. Participants completed self-report measures of self-criticism, dependency, and neuroticism at baseline, and self-report measures of negative events, depressive symptoms, and social anxiety symptoms once a month for six months. Findings showed that self-criticism predicted depressive symptoms, while dependency predicted social anxiety symptoms. In addition, support was found for a mediation model, as opposed to a moderation model, with achievement stressors mediating the relation between self-criticism and depressive symptoms. Overall, these findings highlight new developmental pathways for the development of depression and social anxiety symptoms in mainland Chinese adolescents. Implications for cross-cultural developmental psychopathology research are discussed. PMID:25798026

Neural basis of distorted self-face recognition in social anxiety disorder.

Science.gov (United States)

Kim, Min-Kyeong; Yoon, Hyung-Jun; Shin, Yu-Bin; Lee, Seung-Koo; Kim, Jae-Jin

2016-01-01

The observer perspective causes patients with social anxiety disorder (SAD) to excessively inspect their performance and appearance. This study aimed to investigate the neural basis of distorted self-face recognition in non-social situations in patients with SAD. Twenty patients with SAD and 20 age- and gender-matched healthy controls participated in this fMRI study. Data were acquired while participants performed a Composite Face Evaluation Task, during which they had to press a button indicating how much they liked a series of self-faces, attractively transformed self-faces, and attractive others' faces. Patients had a tendency to show more favorable responses to the self-face and unfavorable responses to the others' faces compared with controls, but the two groups' responses to the attractively transformed self-faces did not differ. Significant group differences in regional activity were observed in the middle frontal and supramarginal gyri in the self-face condition (patients self-face condition (patients > controls); and the middle frontal, supramarginal, and angular gyri in the attractive others' face condition (patients > controls). Most fronto-parietal activities during observation of the self-face were negatively correlated with preference scores in patients but not in controls. Patients with SAD have a positive point of view of their own face and experience self-relevance for the attractively transformed self-faces. This distorted cognition may be based on dysfunctions in the frontal and inferior parietal regions. The abnormal engagement of the fronto-parietal attentional network during processing face stimuli in non-social situations may be linked to distorted self-recognition in SAD.

Self-Organized Transport System

Science.gov (United States)

2009-09-28

This report presents the findings of the simulation model for a self-organized transport system where traffic lights communicate with neighboring traffic lights and make decisions locally to adapt to traffic conditions in real time. The model is insp...

Optical electronics self-organized integration and applications

CERN Document Server

Yoshimura, Tetsuzo

2012-01-01

IntroductionFrom Electronics to Optical ElectronicsAnalysis Tools for Optical CircuitsSelf-Organized Optical Waveguides: Theoretical AnalysisSelf-Organized Optical Waveguides: Experimental DemonstrationsOptical Waveguide Films with Vertical Mirrors 3-D Optical Circuits with Stacked Waveguide Films Heterogeneous Thin-Film Device IntegrationOptical Switches OE Hardware Built by Optical ElectronicsIntegrated Solar Energy Conversion SystemsFuture Challenges.

Neural responses to feedback information produced by self-generated or other-generated decision-making and their impairment in schizophrenia.

Directory of Open Access Journals (Sweden)

Atsuhito Toyomaki

Full Text Available Several studies of self-monitoring dysfunction in schizophrenia have focused on the sense of agency to motor action using behavioral and psychophysiological techniques. So far, no study has ever tried to investigate whether the sense of agency or causal attribution for external events produced by self-generated decision-making is abnormal in schizophrenia. The purpose of this study was to investigate neural responses to feedback information produced by self-generated or other-generated decision-making in a multiplayer gambling task using even-related potentials and electroencephalogram synchronization. We found that the late positive component and theta/alpha synchronization were increased in response to feedback information in the self-decision condition in normal controls, but that these responses were significantly decreased in patients with schizophrenia. These neural activities thus reflect the self-reference effect that affects the cognitive appraisal of external events following decision-making and their impairment in schizophrenia.

Critical dynamics in associative memory networks

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Maximilian eUhlig

2013-07-01

Full Text Available Critical behavior in neural networks is characterized by scale-free avalanche size distributions and can be explained by self-regulatory mechanisms. Theoretical and experimental evidence indicates that information storage capacity reaches its maximum in the critical regime. We study the effect of structural connectivity formed by Hebbian learning on the criticality of network dynamics. The network endowed with Hebbian learning only does not allow for simultaneous information storage and criticality. However, the critical regime is can be stabilized by short-term synaptic dynamics in the form of synaptic depression and facilitation or, alternatively, by homeostatic adaptation of the synaptic weights. We show that a heterogeneous distribution of maximal synaptic strengths does not preclude criticality if the Hebbian learning is alternated with periods of critical dynamics recovery. We discuss the relevance of these findings for the flexibility of memory in aging and with respect to the recent theory of synaptic plasticity.

Complexity in plasma: From self-organization to geodynamo

International Nuclear Information System (INIS)

Sato, T.

1996-01-01

A central theme of open-quote open-quote Complexity close-quote close-quote is the question of the creation of ordered structure in nature (self-organization). The assertion is made that self-organization is governed by three key processes, i.e., energy pumping, entropy expulsion and nonlinearity. Extensive efforts have been done to confirm this assertion through computer simulations of plasmas. A system exhibits markedly different features in self-organization, depending on whether the energy pumping is instantaneous or continuous, or whether the produced entropy is expulsed or reserved. The nonlinearity acts to bring a nonequilibrium state into a bifurcation, thus resulting in a new structure along with an anomalous entropy production. As a practical application of our grand view of self-organization a preferential generation of a dipole magnetic field is successfully demonstrated. copyright 1996 American Institute of Physics

Critical Care Organizations in Academic Medical Centers in North America: A Descriptive Report.

Science.gov (United States)

Pastores, Stephen M; Halpern, Neil A; Oropello, John M; Kostelecky, Natalie; Kvetan, Vladimir

2015-10-01

With the exception of a few single-center descriptive reports, data on critical care organizations are relatively sparse. The objectives of our study were to determine the structure, governance, and experience to date of established critical care organizations in North American academic medical centers. A 46-item survey questionnaire was electronically distributed using Survey Monkey to the leadership of 27 identified critical care organizations in the United States and Canada between September 2014 and February 2015. A critical care organization had to be headed by a physician and have primary governance over the majority, if not all, of the ICUs in the medical center. We received 24 responses (89%). The majority of the critical care organizations (83%) were called departments, centers, systems, or operations committees. Approximately two thirds of respondents were from larger (> 500 beds) urban institutions, and nearly 80% were primary university medical centers. On average, there were six ICUs per academic medical center with a mean of four ICUs under critical care organization governance. In these ICUs, intensivists were present in-house 24/7 in 49%; advanced practice providers in 63%; hospitalists in 21%; and telemedicine coverage in 14%. Nearly 60% of respondents indicated that they had a separate hospital budget to support data management and reporting, oversight of their ICUs, and rapid response teams. The transition from the traditional model of ICUs within departmentally controlled services or divisions to a critical care organization was described as gradual in 50% and complete in only 25%. Nearly 90% indicated that their critical care organization governance structure was either moderately or highly effective; a similar number suggested that their critical care organizations were evolving with increasing domain and financial control of the ICUs at their respective institutions. Our survey of the very few critical care organizations in North American

Chaos-driven decay of nuclear giant resonances: Quantum route to self-organization

International Nuclear Information System (INIS)

Drozdz, S.; Nishizaki, S.; Wambach, J.

1994-01-01

The influence of background states with increasing level of complexity on the strength distribution of the isoscalar and isovector giant quadrupole resonance in 40 Ca is studied. It is found that the background characteristics, typical for chaotic systems, strongly affect the fluctuation properties of the strength distribution. In particular, the small components of the wave function obey a scaling law analogous to self-organized systems at the critical state. This appears to be consistent with the Porter-Thomas distribution of the transition strength

Accumulation of Content Validation Evidence for the Critical Thinking Self-Assessment Scale.

Science.gov (United States)

Nair, Girija Gopinathan; Hellsten, Laurie-Ann M; Stamler, Lynnette Leeseberg

2017-04-01

Critical thinking skills (CTS) are essential for nurses; assessing students' acquisition of these skills is a mandate of nursing curricula. This study aimed to develop a self-assessment instrument of critical thinking skills (Critical Thinking Self-Assessment Scale [CTSAS]) for students' self-monitoring. An initial pool of 196 items across 6 core cognitive skills and 16 subskills were generated using the American Philosophical Association definition of CTS. Experts' content review of the items and their ratings provided evidence of content relevance using the item-level content validity index (I-CVI) and Aiken's content validity coefficient (VIk). 115 items were retained (range of I-CVI values = .70 to .94 and range of VIk values = .69-.95; significant at pself-assessment purposes.

Critical heat flux prediction by using radial basis function and multilayer perceptron neural networks: A comparison study

International Nuclear Information System (INIS)

Vaziri, Nima; Hojabri, Alireza; Erfani, Ali; Monsefi, Mehrdad; Nilforooshan, Behnam

2007-01-01

Critical heat flux (CHF) is an important parameter for the design of nuclear reactors. Although many experimental and theoretical researches have been performed, there is not a single correlation to predict CHF because it is influenced by many parameters. These parameters are based on fixed inlet, local and fixed outlet conditions. Artificial neural networks (ANNs) have been applied to a wide variety of different areas such as prediction, approximation, modeling and classification. In this study, two types of neural networks, radial basis function (RBF) and multilayer perceptron (MLP), are trained with the experimental CHF data and their performances are compared. RBF predicts CHF with root mean square (RMS) errors of 0.24%, 7.9%, 0.16% and MLP predicts CHF with RMS errors of 1.29%, 8.31% and 2.71%, in fixed inlet conditions, local conditions and fixed outlet conditions, respectively. The results show that neural networks with RBF structure have superior performance in CHF data prediction over MLP neural networks. The parametric trends of CHF obtained by the trained ANNs are also evaluated and results reported

The role of hierarchy in self-organizing systems

NARCIS (Netherlands)

Ollfen, van W.; Romme, A.G.L.

1995-01-01

This paper discusses the role of hierarchy in human systems. Two kinds of self-organizing processes are distinguished: conservative and dissipative self-organization. The former leads to rather stable, specialistic systems, whereas the latter leads to continuously changing generalistic systems. When

Nuclear Criticality Safety Organization qualification program. Revision 4

International Nuclear Information System (INIS)

Carroll, K.J.; Taylor, R.G.; Worley, C.A.

1997-01-01

The Nuclear Criticality Safety Organization (NCSO) is committed to developing and maintaining a staff of highly qualified personnel to meet the current and anticipated needs in Nuclear Criticality Safety (NCS) at the Oak Ridge Y-12 Plant. This document defines the Qualification Program to address the NCSO technical and managerial qualification as required by the Y-12 Training Implementation Matrix (TIM). It is implemented through a combination of LMES plant-wide training courses and professional nuclear criticality safety training provided within the organization. This Qualification Program is applicable to technical and managerial NCSO personnel, including temporary personnel, sub-contractors and/or LMES employees on loan to the NCSO, who perform the NCS tasks or serve NCS-related positions as defined in sections 5 and 6 of this program

Self-organized computation with unreliable, memristive nanodevices

International Nuclear Information System (INIS)

Snider, G S

2007-01-01

Nanodevices have terrible properties for building Boolean logic systems: high defect rates, high variability, high death rates, drift, and (for the most part) only two terminals. Economical assembly requires that they be dynamical. We argue that strategies aimed at mitigating these limitations, such as defect avoidance/reconfiguration, or applying coding theory to circuit design, present severe scalability and reliability challenges. We instead propose to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations. The key idea is to exploit memristive nanodevice behavior to cheaply implement adaptive, recurrent networks, useful for complex pattern recognition problems. Pulse-based communication allows the designer to make trade-offs between power consumption and processing speed. Self-organization sidesteps the scalability issues of characterization, compilation and configuration. Network dynamics supplies a graceful response to device death. We present simulation results of such a network-a self-organized spatial filter array-that demonstrate its performance as a function of defects and device variation

Maternal Diabetes Alters Expression of MicroRNAs that Regulate Genes Critical for Neural Tube Development

Directory of Open Access Journals (Sweden)

Seshadri Ramya

2017-07-01

Full Text Available Maternal diabetes is known to cause neural tube defects (NTDs in embryos and neuropsychological deficits in infants. Several metabolic pathways and a plethora of genes have been identified to be deregulated in developing brain of embryos by maternal diabetes, although the exact mechanism remains unknown. Recently, miRNAs have been shown to regulate genes involved in brain development and maturation. Therefore, we hypothesized that maternal diabetes alters the expression of miRNAs that regulate genes involved in biological pathways critical for neural tube development and closure during embryogenesis. To address this, high throughput miRNA expression profiling in neural stem cells (NSCs isolated from the forebrain of embryos from normal or streptozotocin-induced diabetic pregnancy was carried out. It is known that maternal diabetes results in fetal hypoglycemia/hyperglycemia or hypoxia. Hence, NSCs from embryos of control pregnant mice were exposed to low or high glucose or hypoxia in vitro. miRNA pathway analysis revealed distinct deregulation of several biological pathways, including axon guidance pathway, which are critical for brain development in NSCs exposed to different treatments. Among the differentially expressed miRNAs, the miRNA-30 family members which are predicted to target genes involved in brain development was upregulated in NSCs from embryos of diabetic pregnancy when compared to control. miRNA-30b was found to be upregulated while its target gene Sirtuin 1 (Sirt1, as revealed by luciferase assay, was down regulated in NSCs from embryos of diabetic pregnancy. Further, overexpression of miRNA-30b in NSCs, resulted in decreased expression of Sirt1 protein, and altered the neuron/glia ratio. On the other hand, siRNA mediated knockdown of Sirt1 in NSCs promoted astrogenesis, indicating that miRNA-30b alters lineage specification via Sirt1. Overall, these results suggest that maternal diabetes alters the genes involved in neural tube

Deliberative Self-Organizing Traffic Lights with Elementary Cellular Automata

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Jorge L. Zapotecatl

2017-01-01

Full Text Available Self-organizing traffic lights have shown considerable improvements compared to traditional methods in computer simulations. Self-organizing methods, however, use sophisticated sensors, increasing their cost and limiting their deployment. We propose a novel approach using simple sensors to achieve self-organizing traffic light coordination. The proposed approach involves placing a computer and a presence sensor at the beginning of each block; each such sensor detects a single vehicle. Each computer builds a virtual environment simulating vehicle movement to predict arrivals and departures at the downstream intersection. At each intersection, a computer receives information across a data network from the computers of the neighboring blocks and runs a self-organizing method to control traffic lights. Our simulations showed a superior performance for our approach compared with a traditional method (a green wave and a similar performance (close to optimal compared with a self-organizing method using sophisticated sensors but at a lower cost. Moreover, the developed sensing approach exhibited greater robustness against sensor failures.

Dissociable neural processes underlying risky decisions for self versus other

Directory of Open Access Journals (Sweden)

Daehyun eJung

2013-03-01

Full Text Available Previous neuroimaging studies on decision making have mainly focused on decisions on behalf of oneself. Considering that people often make decisions on behalf of others, it is intriguing that there is little neurobiological evidence on how decisions for others differ from those for self. Thus, the present study focused on the direct comparison between risky decisions for self and those for other using functional magnetic resonance imaging (fMRI. Participants (N = 23 were asked to perform a gambling task for themselves (decision-for-self condition or for another person (decision-for-other condition while in the scanner. Their task was to choose between a low-risk option (i.e., win or lose 10 points and a high-risk option (i.e., win or lose 90 points. The winning probabilities of each option varied from 17% to 83%. Compared to choices for others, choices for self were more risk-averse at lower winning probability and more risk-seeking at higher winning probability, perhaps due to stronger affective process during risky decision for self compared to other. The brain activation pattern changed according to the target of the decision, such that reward-related regions were more active in the decision-for-self condition than in the decision-for-other condition, whereas brain regions related to the theory of mind (ToM showed greater activation in the decision-for-other condition than in the decision-for-self condition. A parametric modulation analysis reflecting each individual’s decision model revealed that activation of the amygdala and the dorsomedial prefrontal cortex (DMPFC were associated with value computation for self and for other, respectively, during a risky financial decision. The present study suggests that decisions for self and other may recruit fundamentally distinctive neural processes, which can be mainly characterized by dominant affective/impulsive and cognitive/regulatory processes, respectively.

Dependency and self-criticism in post-partum depression and anxiety: a case control study.

Science.gov (United States)

Vliegen, Nicole; Luyten, Patrick

2009-01-01

This study investigates the role of self-criticism and dependency in inpatient post-partum depressed women (n = 55) and non-depressed controls (n = 37) as well as the relationship between both personality dimensions and severity of depression and anxiety. As expected, mothers with post-partum depression showed not only increased levels of depression but also anxiety compared with non-depressed mothers. Furthermore, they had significantly higher levels of self-criticism, but not of dependency. In the post-partum depressed mothers, both personality dimensions were positively associated with severity of depression. However, in non-depressed mothers, self-criticism was positively associated with depression, while there was an inverse relationship between dependency and severity of depression. In both samples, self-criticism, but not dependency, was related to state anxiety. The cross-sectional nature of this study limits the ability to draw causal conclusions. The study was based on self-report and conducted in relatively small samples.

Implications of behavioral architecture for the evolution of self-organized division of labor.

Directory of Open Access Journals (Sweden)

A Duarte

Full Text Available Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization.

Implications of behavioral architecture for the evolution of self-organized division of labor.

Science.gov (United States)

Duarte, A; Scholtens, E; Weissing, F J

2012-01-01

Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization.

Self-consistent signal-to-noise analysis of the statistical behavior of analog neural networks and enhancement of the storage capacity

Science.gov (United States)

Shiino, Masatoshi; Fukai, Tomoki

1993-08-01

Based on the self-consistent signal-to-noise analysis (SCSNA) capable of dealing with analog neural networks with a wide class of transfer functions, enhancement of the storage capacity of associative memory and the related statistical properties of neural networks are studied for random memory patterns. Two types of transfer functions with the threshold parameter θ are considered, which are derived from the sigmoidal one to represent the output of three-state neurons. Neural networks having a monotonically increasing transfer function FM, FM(u)=sgnu (||u||>θ), FM(u)=0 (||u||memory patterns), implying the reduction of the number of spurious states. The behavior of the storage capacity with changing θ is qualitatively the same as that of the Ising spin neural networks with varying temperature. On the other hand, the nonmonotonic transfer function FNM, FNM(u)=sgnu (||u||=θ) gives rise to remarkable features in several respects. First, it yields a large enhancement of the storage capacity compared with the Amit-Gutfreund-Sompolinsky (AGS) value: with decreasing θ from θ=∞, the storage capacity αc of such a network is increased from the AGS value (~=0.14) to attain its maximum value of ~=0.42 at θ~=0.7 and afterwards is decreased to vanish at θ=0. Whereas for θ>~1 the storage capacity αc coincides with the value αc~ determined by the SCSNA as the upper bound of α ensuring the existence of retrieval solutions, for θr≠0 (i.e., finite width of the local field distribution), which is implied by the order-parameter equations of the SCSNA, disappears at a certain critical loading rate α0, and for αr=0+). As a consequence, memory retrieval without errors becomes possible even in the saturation limit α≠0. Results of the computer simulations on the statistical properties of the novel phase with αstorage capacity is also analyzed for the two types of networks. It is conspicuous for the networks with FNM, where the self-couplings increase the stability of

Time-lapse imaging of neural development: zebrafish lead the way into the fourth dimension.

Science.gov (United States)

Rieger, Sandra; Wang, Fang; Sagasti, Alvaro

2011-07-01

Time-lapse imaging is often the only way to appreciate fully the many dynamic cell movements critical to neural development. Zebrafish possess many advantages that make them the best vertebrate model organism for live imaging of dynamic development events. This review will discuss technical considerations of time-lapse imaging experiments in zebrafish, describe selected examples of imaging studies in zebrafish that revealed new features or principles of neural development, and consider the promise and challenges of future time-lapse studies of neural development in zebrafish embryos and adults. Copyright © 2011 Wiley-Liss, Inc.

Hedgehog regulates Norrie disease protein to drive neural progenitor self-renewal.

Science.gov (United States)

McNeill, Brian; Mazerolle, Chantal; Bassett, Erin A; Mears, Alan J; Ringuette, Randy; Lagali, Pamela; Picketts, David J; Paes, Kim; Rice, Dennis; Wallace, Valerie A

2013-03-01

Norrie disease (ND) is a congenital disorder characterized by retinal hypovascularization and cognitive delay. ND has been linked to mutations in 'Norrie Disease Protein' (Ndp), which encodes the secreted protein Norrin. Norrin functions as a secreted angiogenic factor, although its role in neural development has not been assessed. Here, we show that Ndp expression is initiated in retinal progenitors in response to Hedgehog (Hh) signaling, which induces Gli2 binding to the Ndp promoter. Using a combination of genetic epistasis and acute RNAi-knockdown approaches, we show that Ndp is required downstream of Hh activation to induce retinal progenitor proliferation in the retina. Strikingly, Ndp regulates the rate of cell-cycle re-entry and not cell-cycle kinetics, thereby uncoupling the self-renewal and cell-cycle progression functions of Hh. Taken together, we have uncovered a cell autonomous function for Ndp in retinal progenitor proliferation that is independent of its function in the retinal vasculature, which could explain the neural defects associated with ND.

Educational strategies associated with development of problem-solving, critical thinking, and self-directed learning.

Science.gov (United States)

Hendricson, William D; Andrieu, Sandra C; Chadwick, D Gregory; Chmar, Jacqueline E; Cole, James R; George, Mary C; Glickman, Gerald N; Glover, Joel F; Goldberg, Jerold S; Haden, N Karl; Meyerowitz, Cyril; Neumann, Laura; Pyle, Marsha; Tedesco, Lisa A; Valachovic, Richard W; Weaver, Richard G; Winder, Ronald L; Young, Stephen K; Kalkwarf, Kenneth L

2006-09-01

This article was developed for the Commission on Change and Innovation in Dental Education (CCI), established by the American Dental Education Association. CCI was created because numerous organizations within organized dentistry and the educational community have initiated studies or proposed modifications to the process of dental education, often working to achieve positive and desirable goals but without coordination or communication. The fundamental mission of CCI is to serve as a focal meeting place where dental educators and administrators, representatives from organized dentistry, the dental licensure community, the Commission on Dental Accreditation, the ADA Council on Dental Education and Licensure, and the Joint Commission on National Dental Examinations can meet and coordinate efforts to improve dental education and the nation's oral health. One of the objectives of the CCI is to provide guidance to dental schools related to curriculum design. In pursuit of that objective, this article summarizes the evidence related to this question: What are educational best practices for helping dental students acquire the capacity to function as an entry-level general dentist or to be a better candidate to begin advanced studies? Three issues are addressed, with special emphasis on the third: 1) What constitutes expertise, and when does an individual become an expert? 2) What are the differences between novice and expert thinking? and 3) What educational best practices can help our students acquire mental capacities associated with expert function, including critical thinking and self-directed learning? The purpose of this review is to provide a benchmark that faculty and academic planners can use to assess the degree to which their curricula include learning experiences associated with development of problem-solving, critical thinking, self-directed learning, and other cognitive skills necessary for dental school graduates to ultimately become expert performers as

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

Science.gov (United States)

Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

A Critical Test of Self-Enhancement, Exposure, and Self-Categorization Explanations for First- and Third-Person Perceptions

Science.gov (United States)

Reid, Scott A.; Byrne, Sahara; Brundidge, Jennifer S.; Shoham, Mirit D.; Marlow, Mikaela L.

2007-01-01

The third-person perception is the tendency for people to believe that others are more influenced by media content than themselves (W. P. Davison, 1983). The current study provides a critical test of self-enhancement, exposure, and self-categorization explanations for first- (i.e., self more influenced than others) and third-person perceptions.…

Activations of the dorsolateral prefrontal cortex and thalamus during agentic self-evaluation are negatively associated with trait self-esteem.

Science.gov (United States)

Jiang, Ke; Wu, Shi; Shi, Zhenhao; Liu, Mingyan; Peng, Maoying; Shen, Yang; Yang, Juan

2018-08-01

Individual self-esteem is dominated more by agency than by communion. However, prior research has mainly focused on one's agentic/communal self-evaluation, while little is known about how one endorses others' agentic/communal evaluation of the self. The present study investigated the associations between trait self-esteem and fundamental dimensions of social cognition, i.e. agency vs. communion, during both self-evaluation and endorsement of others' evaluation of oneself. We also investigated the neural mechanisms underlying the relationship between trait self-esteem and agentic self-evaluation. Behavioral results revealed that self-esteem was positively correlated with the agentic ratings from self-evaluation and endorsement of others' evaluation of the self, and that the agentic self-evaluation was a significant full mediator between self-esteem and endorsement of others' agentic evaluation. Whole-brain regression analysis revealed that self-esteem was negatively correlated with right dorsolateral prefrontal and bilateral thalamic response to agentic self-evaluation. A possible interpretation is that low self-esteem people both hold a more self-critical attitude about the self and have less certainty or clarity of their self-concepts than high self-esteem people do. These findings have important implication for understanding the neural and cognitive mechanisms underlying self-esteem's effect on one's agentic self-evaluations. Copyright © 2018 Elsevier B.V. All rights reserved.