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Attractor network

Pascal H H M van Lieshout
Speech is a complex oral motor function that involves multiple articulators that need to be coordinated in space and time at relatively high movement speeds. How this is accomplished remains an important and largely unresolved empirical question. From a coordination dynamics perspective, coordination involves the assembly of coordinative units that are characterized by inherently stable coupling patterns that act as attractor states for task-specific actions. In the motor control literature, one particular model formulated by Haken et al...
May 17, 2017: Experimental Brain Research. Experimentelle Hirnforschung. Expérimentation Cérébrale
Chris Gorman, Anthony Robins, Alistair Knott
We present an investigation of the potential use of Hopfield networks to learn neurally plausible, distributed representations of category prototypes. Hopfield networks are dynamical models of autoassociative memory which learn to recreate a set of input states from any given starting state. These networks, however, will almost always learn states which were not presented during training, so called spurious states. Historically, spurious states have been an undesirable side-effect of training a Hopfield network and there has been much research into detecting and discarding these unwanted states...
April 25, 2017: Neural Networks: the Official Journal of the International Neural Network Society
Sui Huang, Fangting Li, Joseph X Zhou, Hong Qian
The notion of an attractor has been widely employed in thinking about the nonlinear dynamics of organisms and biological phenomena as systems and as processes. The notion of a landscape with valleys and mountains encoding multiple attractors, however, has a rigorous foundation only for closed, thermodynamically non-driven, chemical systems, such as a protein. Recent advances in the theory of nonlinear stochastic dynamical systems and its applications to mesoscopic reaction networks, one reaction at a time, have provided a new basis for a landscape of open, driven biochemical reaction systems under sustained chemostat...
May 2017: Journal of the Royal Society, Interface
Varsha Sreenivasan, Shakti N Menon, Sitabhra Sinha
Many natural systems including the brain comprise coupled elements that are stimulated non-uniformly. In this paper we show that heterogeneously driven networks of excitatory-inhibitory units exhibit a diverse range of collective phenomena, including the appearance of spontaneous oscillations upon coupling quiescent elements. On varying the coupling strength a previously unreported transition is seen wherein the symmetries of the synchronization patterns in the stimulated and unstimulated groups undergo mutual exchange...
May 9, 2017: Scientific Reports
P Grindrod, T E Lee
We consider a directed graph model for the human brain's neural architecture that is based on small scale, directed, strongly connected sub-graphs (SCGs) of neurons, that are connected together by a sparser mesoscopic network. We assume transmission delays within neuron-to-neuron stimulation, and that individual neurons have an excitable-refractory dynamic, with single firing 'spikes' occurring on a much faster time scale than that of the transmission delays. We demonstrate numerically that the SCGs typically have attractors that are equivalent to continual winding maps over relatively low-dimensional tori, thus representing a limit on the range of distinct behaviour...
April 2017: Royal Society Open Science
Roger Orpwood
This article argues that qualia are a likely outcome of the processing of information in local cortical networks. It uses an information-based approach and makes a distinction between information structures (the physical embodiment of information in the brain, primarily patterns of action potentials), and information messages (the meaning of those structures to the brain, and the basis of qualia). It develops formal relationships between these two kinds of information, showing how information structures can represent messages, and how information messages can be identified from structures...
2017: Frontiers in Systems Neuroscience
Sung Soo Kim, Hervé Rouault, Shaul Druckmann, Vivek Jayaraman
Ring attractors are a class of recurrent networks hypothesized to underlie the representation of heading direction. Such network structures, schematized as a ring of neurons whose connectivity depends on their heading preferences, can sustain a bump-like activity pattern whose location can be updated by continuous shifts along either turn direction. We recently reported that a population of fly neurons represents the animal's heading via bump-like activity dynamics. We combined two-photon calcium imaging in head-fixed flying flies with optogenetics to overwrite the existing population representation with an artificial one, which was then maintained by the circuit with naturalistic dynamics...
May 4, 2017: Science
Nimrod Shaham, Yoram Burak
It has been proposed that neural noise in the cortex arises from chaotic dynamics in the balanced state: in this model of cortical dynamics, the excitatory and inhibitory inputs to each neuron approximately cancel, and activity is driven by fluctuations of the synaptic inputs around their mean. It remains unclear whether neural networks in the balanced state can perform tasks that are highly sensitive to noise, such as storage of continuous parameters in working memory, while also accounting for the irregular behavior of single neurons...
May 2017: PLoS Computational Biology
Atefeh Taherian Fard, Mark A Ragan
Genome-wide regulatory networks enable cells to function, develop, and survive. Perturbation of these networks can lead to appearance of a disease phenotype. Inspired by Conrad Waddington's epigenetic landscape of cell development, we use a Hopfield network formalism to construct an attractor landscape model of disease progression based on protein- or gene-correlation networks of Parkinson's disease, glioma, and colorectal cancer. Attractors in this landscape correspond to normal and disease states of the cell...
2017: Frontiers in Genetics
Mónica L García-Gómez, Eugenio Azpeitia, Elena R Álvarez-Buylla
The study of the concerted action of hormones and transcription factors is fundamental to understand cell differentiation and pattern formation during organ development. The root apical meristem of Arabidopsis thaliana is a useful model to address this. It has a stem cell niche near its tip conformed of a quiescent organizer and stem or initial cells around it, then a proliferation domain followed by a transition domain, where cells diminish division rate before transiting to the elongation zone; here, cells grow anisotropically prior to their final differentiation towards the plant base...
April 2017: PLoS Computational Biology
A V Cano, M G Cosenza
We show that chimera states, where differentiated subsets of synchronized and desynchronized dynamical elements coexist, can emerge in networks of hyperbolic chaotic oscillators subject to global interactions. As local dynamics we employ Lozi maps, which possess hyperbolic chaotic attractors. We consider a globally coupled system of these maps and use two statistical quantities to describe its collective behavior: the average fraction of elements belonging to clusters and the average standard deviation of state variables...
March 2017: Physical Review. E
Carmen C Canavier, Ruben A Tikidji-Hamburyan
The synchronization tendencies of networks of oscillators have been studied intensely. We assume a network of all-to-all pulse-coupled oscillators in which the effect of a pulse is independent of the number of oscillators that simultaneously emit a pulse and the normalized delay (the phase resetting) is a monotonically increasing function of oscillator phase with the slope everywhere less than 1 and a value greater than 2φ-1, where φ is the normalized phase. Order switching cannot occur; the only possible solutions are globally attracting synchrony and cluster solutions with a fixed firing order...
March 2017: Physical Review. E
Anna Fedor, István Zachar, András Szilágyi, Michael Öllinger, Harold P de Vladar, Eörs Szathmáry
In this paper, we show that a neurally implemented a cognitive architecture with evolutionary dynamics can solve the four-tree problem. Our model, called Darwinian Neurodynamics, assumes that the unconscious mechanism of problem solving during insight tasks is a Darwinian process. It is based on the evolution of patterns that represent candidate solutions to a problem, and are stored and reproduced by a population of attractor networks. In our first experiment, we used human data as a benchmark and showed that the model behaves comparably to humans: it shows an improvement in performance if it is pretrained and primed appropriately, just like human participants in Kershaw et al...
2017: Frontiers in Psychology
Yunseong Kim, Sea Choi, Dongkwan Shin, Kwang-Hyun Cho
BACKGROUND: Cancer reversion, converting the phenotypes of a cancer cell into those of a normal cell, has been sporadically observed throughout history. However, no systematic analysis has been attempted so far. RESULTS: To investigate this from a systems biological perspective, we have constructed a logical network model of colorectal tumorigenesis by integrating key regulatory molecules and their interactions from previous experimental data. We identified molecular targets that can reverse cancerous cellular states to a normal state by systematically perturbing each molecular activity in the network and evaluating the resulting changes of the attractor landscape with respect to uncontrolled proliferation, EMT, and stemness...
April 5, 2017: BMC Systems Biology
Sarthak Chandra, David Hathcock, Kimberly Crain, Thomas M Antonsen, Michelle Girvan, Edward Ott
We derive a mean-field approximation for the macroscopic dynamics of large networks of pulse-coupled theta neurons in order to study the effects of different network degree distributions and degree correlations (assortativity). Using the ansatz of Ott and Antonsen [Chaos 18, 037113 (2008)], we obtain a reduced system of ordinary differential equations describing the mean-field dynamics, with significantly lower dimensionality compared with the complete set of dynamical equations for the system. We find that, for sufficiently large networks and degrees, the dynamical behavior of the reduced system agrees well with that of the full network...
March 2017: Chaos
Mohammad Bagher Khodabakhshi, Mohammad Hassan Moradi
The respiratory system dynamic is of high significance when it comes to the detection of lung abnormalities, which highlights the importance of presenting a reliable model for it. In this paper, we introduce a novel dynamic modelling method for the characterization of the lung sounds (LS), based on the attractor recurrent neural network (ARNN). The ARNN structure allows the development of an effective LS model. Additionally, it has the capability to reproduce the distinctive features of the lung sounds using its formed attractors...
March 23, 2017: Computers in Biology and Medicine
Se-Woong Park, Hamal Marino, Steven K Charles, Dagmar Sternad, Neville Hogan
Mounting evidence suggests that human motor control uses dynamic primitives, attractors of dynamic neural networks which require minimal central supervision. However, advantages for control may be offset by compromised versatility. Extending recent results showing that humans could not sustain discrete motions as duration decreased, this study tested whether smoothly rhythmic performance could be maintained as duration increased. Participants performed horizontal movements between two targets, paced by sounds that increased from 1s to 6s by 200ms per cycle and then decreased again...
March 29, 2017: Journal of Neurophysiology
Jinquan Wang, Jian Cheng, Chao Zhang, Xiaojun Li
This study was designed to identify attractor modules and further reveal the potential biological processes involving in sevoflurane-induced anesthesia in patients treated with coronary artery bypass graft (CABG) surgery. Microarray profile data (ID: E-GEOD-4386) on atrial samples obtained from patients receiving anesthetic gas sevoflurane prior to and following CABG procedure were downloaded from EMBL-EBI database for further analysis. Protein-protein interaction (PPI) networks of baseline and sevoflurane groups were inferred and reweighted according to Spearman correlation coefficient (SCC), followed by systematic modules inference using clique-merging approach...
2017: Computational and Mathematical Methods in Medicine
Eugenio Azpeitia, Stalin Muñoz, Daniel González-Tokman, Mariana Esther Martínez-Sánchez, Nathan Weinstein, Aurélien Naldi, Elena R Álvarez-Buylla, David A Rosenblueth, Luis Mendoza
No abstract text is available yet for this article.
March 22, 2017: Scientific Reports
Ying Sun, Yan Weng, Ying Zhang, Xiang Yan, Lei Guo, Jia Wang, Xin Song, Ying Yuan, Fu-Ye Chang, Chun-Ling Wang
About 90% of tuberculosis (TB) patients latently infected with Mycobacterium tuberculosis (Mtb) show no symptoms, yet have a 10% chance in lifetime to progress active TB. Nevertheless, current diagnosis approaches need improvement in efficiency and sensitivity. The objective of this work was to detect potential signatures for active TB to further improve the understanding of the biological roles of functional modules involved in this disease. First, targeted networks of active TB and control groups were established via re-weighting protein-protein interaction (PPI) networks using Pearson's correlation coefficient (PCC)...
March 18, 2017: Microbial Pathogenesis
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