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Attractor network

Evgeni V Nikolaev, Sahand Jamal Rahi, Eduardo D Sontag
This article uncovers a remarkable behavior in two biochemical systems that commonly appear as components of signal transduction pathways in systems biology. These systems have globally attracting steady states when unforced, so they might have been considered uninteresting from a dynamical standpoint. However, when subject to a periodic excitation, strange attractors arise via a period-doubling cascade. Quantitative analyses of the corresponding discrete chaotic trajectories are conducted numerically by computing largest Lyapunov exponents, power spectra, and autocorrelation functions...
March 13, 2018: Biophysical Journal
Ivan Grbatinić, Nebojsa Milosevic, Dusica Maric
The aim of this study is to determine whether the dentate neurons can be translaminary neuromorphotopologically classified as ventrolateral or dorsomedial type. Adult human dentate 2D binary interneuron images are used for the purposes of the analysis. The analysis is performed on the real and the virtual neuron sample. The total of 29 parameters is used. They can be divided into the classes: neuron surface, shape, length, branching and complexity. The clustering is performed through the algorithm consisted from of the steps of predictor extraction (matrix attractor analysis/non-negative matrix factorization and cluster analysis of predictor factors, separate unifactor analysis/Student's t-test and MANOVA) and multivariate cluster analysis set (cluster analysis, principal component analysis, factor analysis with pro/varimax rotation, Fisher's linear discriminant analysis and feed-forward backpropagation artificial neural networks)...
2018: Journal of Integrative Neuroscience
Arnaud Poret, Carito Guziolowski
In a previous article, an algorithm for identifying therapeutic targets in Boolean networks modelling pathological mechanisms was introduced. In the present article, the improvements made on this algorithm, named kali, are described. These improvements are (i) the possibility to work on asynchronous Boolean networks, (ii) a finer assessment of therapeutic targets and (iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system, such as a Boolean network, are associated with the phenotypes of the modelled biological system...
February 2018: Royal Society Open Science
Stefano Coletta, Markus Frey, Khaled Nasr, Patricia Preston-Ferrer, Andrea Burgalossi
In order to support navigation, the firing of head-direction (HD) neurons must be tightly anchored to the external space. Indeed, inputs from external landmarks can rapidly reset the preferred direction of HD cells. Landmark stimuli have often been simulated as excitatory inputs from 'visual cells' (encoding landmark information) to the HD attractor network; when excitatory visual inputs are sufficiently strong, preferred directions switch abruptly to the landmark location. In the present work, we tested whether mimicking such inputs via juxtacellular stimulation would be sufficient for shifting the tuning of individual presubicular HD cells, recorded in passively-rotated male rats...
February 27, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Osama Shiraz Shah, Muhammad Faizyab Ali Chaudhary, Hira Anees Awan, Fizza Fatima, Zainab Arshad, Bibi Amina, Maria Ahmed, Hadia Hameed, Muhammad Furqan, Shareef Khalid, Amir Faisal, Safee Ullah Chaudhary
Boolean modelling of biological networks is a well-established technique for abstracting dynamical biomolecular regulation in cells. Specifically, decoding linkages between salient regulatory network states and corresponding cell fate outcomes can help uncover pathological foundations of diseases such as cancer. Attractor landscape analysis is one such methodology which converts complex network behavior into a landscape of network states wherein each state is represented by propensity of its occurrence. Towards undertaking attractor landscape analysis of Boolean networks, we propose an Attractor Landscape Analysis Toolbox (ATLANTIS) for cell fate discovery, from biomolecular networks, and reprogramming upon network perturbation...
February 23, 2018: Scientific Reports
Zhuo Chen, Yi Luo, Nima Mesgarani
Despite the overwhelming success of deep learning in various speech processing tasks, the problem of separating simultaneous speakers in a mixture remains challenging. Two major difficulties in such systems are the arbitrary source permutation and unknown number of sources in the mixture. We propose a novel deep learning framework for single channel speech separation by creating attractor points in high dimensional embedding space of the acoustic signals which pull together the time-frequency bins corresponding to each source...
March 2017: Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing
Masayuki Ushio, Chih-Hao Hsieh, Reiji Masuda, Ethan R Deyle, Hao Ye, Chun-Wei Chang, George Sugihara, Michio Kondoh
Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics...
February 7, 2018: Nature
Joshua P Bassett, Thomas J Wills, Francesca Cacucci
Head direction (HD) cells are neurons found in an extended cortical and subcortical network that signal the orientation of an animal's head relative to its environment [1-3]. They are a fundamental component of the wider circuit of spatially responsive hippocampal formation neurons that make up the neural cognitive map of space [4]. During post-natal development, HD cells are the first among spatially modulated neurons in the hippocampal circuit to exhibit mature firing properties [5, 6], but before eye opening, HD cell responses in rat pups have low directional information and are directionally unstable [7, 8]...
January 31, 2018: Current Biology: CB
Xiaochen Mao, Jianqiao Sun, Shaofan Li
This paper studies the dynamical behaviors of a pair of FitzHugh-Nagumo neural networks with bidirectional delayed couplings. It presents a detailed analysis of delay-independent and delay-dependent stabilities and the existence of bifurcated oscillations. Illustrative examples are performed to validate the analytical results and to discover interesting phenomena. It is shown that the network exhibits a variety of complicated activities, such as multiple stability switches, the coexistence of periodic and quasi-periodic oscillations, the coexistence of periodic and chaotic orbits, and the coexisting chaotic attractors...
January 2018: Chaos
R E Heyman, A K Wojda, J M Eddy, N C Haydt, J F Geiger, A M Smith Slep
Over 1 in 5 dental patients report moderate to severe dental fear. Although the efficacy of cognitive-behavioral treatment (CBT) for dental fear has been examined in over 20 randomized controlled trials-with 2 meta-analyses finding strong average effect sizes ( d > 1)-CBT has received almost no dissemination beyond the specialty clinics that tested it. The challenge, then, is not how to treat dental fear but how to disseminate and implement such an evidence-based treatment in a way that recognizes the rewards and barriers in the US health care system...
February 2018: Advances in Dental Research
Zhuocheng Xiao, Jiwei Zhang, Andrew T Sornborger, Louis Tao
Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next...
November 2017: Physical Review. E
Peter Ashwin, Jennifer Creaser, Krasimira Tsaneva-Atanasova
It is well known that the addition of noise to a multistable dynamical system can induce random transitions from one stable state to another. For low noise, the times between transitions have an exponential tail and Kramers' formula gives an expression for the mean escape time in the asymptotic limit. If a number of multistable systems are coupled into a network structure, a transition at one site may change the transition properties at other sites. We study the case of escape from a "quiescent" attractor to an "active" attractor in which transitions back can be ignored...
November 2017: Physical Review. E
R I M Dunbar, Padraig Mac Carron, Susanne Shultz
Primate groups vary considerably in size across species. Nonetheless, the distribution of mean species group size has a regular scaling pattern with preferred sizes approximating 2.5, 5, 15, 30 and 50 individuals (although strepsirrhines lack the latter two), with a scaling ratio of approximately 2.5 similar to that observed in human social networks. These clusters appear to form distinct social grades that are associated with rapid evolutionary change, presumably in response to intense environmental selection pressures...
January 2018: Biology Letters
Christopher J Hillar, Ngoc M Tran
The Hopfield recurrent neural network is a classical auto-associative model of memory, in which collections of symmetrically coupled McCulloch-Pitts binary neurons interact to perform emergent computation. Although previous researchers have explored the potential of this network to solve combinatorial optimization problems or store reoccurring activity patterns as attractors of its deterministic dynamics, a basic open problem is to design a family of Hopfield networks with a number of noise-tolerant memories that grows exponentially with neural population size...
January 16, 2018: Journal of Mathematical Neuroscience
Călin-Adrian Popa, Eva Kaslik
The existence of multiple exponentially stable equilibrium states and periodic solutions is investigated for Hopfield-type quaternion-valued neural networks (QVNNs) with impulsive effects and both time-dependent and distributed delays. Employing Brouwer's and Leray-Schauder's fixed point theorems, suitable Lyapunov functionals and impulsive control theory, sufficient conditions are given for the existence of 16n attractors, showing a substantial improvement in storage capacity, compared to real-valued or complex-valued neural networks...
December 18, 2017: Neural Networks: the Official Journal of the International Neural Network Society
Arnau Montagud, Pauline Traynard, Loredana Martignetti, Eric Bonnet, Emmanuel Barillot, Andrei Zinovyev, Laurence Calzone
Mathematical models can serve as a tool to formalize biological knowledge from diverse sources, to investigate biological questions in a formal way, to test experimental hypotheses, to predict the effect of perturbations and to identify underlying mechanisms. We present a pipeline of computational tools that performs a series of analyses to explore a logical model's properties. A logical model of initiation of the metastatic process in cancer is used as a transversal example. We start by analysing the structure of the interaction network constructed from the literature or existing databases...
December 8, 2017: Briefings in Bioinformatics
Edmund T Rolls
A quantitative computational theory of the operation of the hippocampus as an episodic memory system is described. The CA3 system operates as a single attractor or autoassociation network (1) to enable rapid one-trial associations between any spatial location (place in rodents or spatial view in primates) and an object or reward and (2) to provide for completion of the whole memory during recall from any part. The theory is extended to associations between time and object or reward to implement temporal order memory, which is also important in episodic memory...
December 7, 2017: Cell and Tissue Research
Oliver L C Rourke, Daniel A Butts
The ability of sensory networks to transiently store information on the scale of seconds can confer many advantages in processing time-varying stimuli. How a network could store information on such intermediate time scales, between typical neurophysiological time scales and those of long-term memory, is typically attributed to persistent neural activity. An alternative mechanism which might allow for such information storage is through temporary modifications to the neural connectivity which decay on the same second-long time scale as the underlying memories...
2017: PloS One
Taiping Zeng, Bailu Si
It is a challenge to build robust simultaneous localization and mapping (SLAM) system in dynamical large-scale environments. Inspired by recent findings in the entorhinal-hippocampal neuronal circuits, we propose a cognitive mapping model that includes continuous attractor networks of head-direction cells and conjunctive grid cells to integrate velocity information by conjunctive encodings of space and movement. Visual inputs from the local view cells in the model provide feedback cues to correct drifting errors of the attractors caused by the noisy velocity inputs...
2017: Frontiers in Neurorobotics
Parul Maheshwari, Réka Albert
BACKGROUND: Cellular behaviors are governed by interaction networks among biomolecules, for example gene regulatory and signal transduction networks. An often used dynamic modeling framework for these networks, Boolean modeling, can obtain their attractors (which correspond to cell types and behaviors) and their trajectories from an initial state (e.g. a resting state) to the attractors, for example in response to an external signal. The existing methods however do not elucidate the causal relationships between distant nodes in the network...
December 6, 2017: BMC Systems Biology
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