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Attractor network

Louis Fippo Fitime, Olivier Roux, Carito Guziolowski, Loïc Paulevé
BACKGROUND: Numerous cellular differentiation processes can be captured using discrete qualitative models of biological regulatory networks. These models describe the temporal evolution of the state of the network subject to different competing transitions, potentially leading the system to different attractors. This paper focusses on the formal identification of states and transitions that are crucial for preserving or pre-empting the reachability of a given behaviour. METHODS: In the context of non-deterministic automata networks, we propose a static identification of so-called bifurcations, i...
2017: Algorithms for Molecular Biology: AMB
Meichen Yuan, Weirong Hong, Pu Li
Complex biological networks typically contain numerous parameters, and determining feasible strategies for state transition by parameter perturbation is not a trivial task. In the present study, based on dynamical and structural analyses of the biological network, we optimized strategies for controlling variables in a two-node gene regulatory network and a T-cell large granular lymphocyte signaling network associated with blood cancer by using an efficient dynamic optimization method. Optimization revealed the critical value for each decision variable to steer the system from an undesired state into a desired attractor...
July 21, 2017: Biochemical Society Transactions
Jean Simonnet, Mérie Nassar, Federico Stella, Ivan Cohen, Bertrand Mathon, Charlotte N Boccara, Richard Miles, Desdemona Fricker
Orientation in space is represented in specialized brain circuits. Persistent head direction signals are transmitted from anterior thalamus to the presubiculum, but the identity of the presubicular target neurons, their connectivity and function in local microcircuits are unknown. Here, we examine how thalamic afferents recruit presubicular principal neurons and Martinotti interneurons, and the ensuing synaptic interactions between these cells. Pyramidal neuron activation of Martinotti cells in superficial layers is strongly facilitating such that high-frequency head directional stimulation efficiently unmutes synaptic excitation...
July 20, 2017: Nature Communications
T Jiang, C-Y Jiang, J-H Shu, Y-J Xu
The molecular mechanism of nasopharyngeal carcinoma (NPC) is poorly understood and effective therapeutic approaches are needed. This research aimed to excavate the attractor modules involved in the progression of NPC and provide further understanding of the underlying mechanism of NPC. Based on the gene expression data of NPC, two specific protein-protein interaction networks for NPC and control conditions were re-weighted using Pearson correlation coefficient. Then, a systematic tracking of candidate modules was conducted on the re-weighted networks via cliques algorithm, and a total of 19 and 38 modules were separately identified from NPC and control networks, respectively...
July 10, 2017: Brazilian Journal of Medical and Biological Research, Revista Brasileira de Pesquisas Médicas e Biológicas
Alexander Rivkind, Omri Barak
Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure...
June 23, 2017: Physical Review Letters
Kyriacos Nikiforou, Pedro A M Mediano, Murray Shanahan
Continuous-time recurrent neural networks are widely used as models of neural dynamics and also have applications in machine learning. But their dynamics are not yet well understood, especially when they are driven by external stimuli. In this article, we study the response of stable and unstable networks to different harmonically oscillating stimuli by varying a parameter ρ, the ratio between the timescale of the network and the stimulus, and use the dimensionality of the network's attractor as an estimate of the complexity of this response...
2017: Cognitive Computation
V Godavarthi, V R Unni, E A Gopalakrishnan, R I Sujith
Thermoacoustic instability and lean blowout are the major challenges faced when a gas turbine combustor is operated under fuel lean conditions. The dynamics of thermoacoustic system is the result of complex nonlinear interactions between the subsystems-turbulent reactive flow and the acoustic field of the combustor. In order to study the transitions between the dynamical regimes in such a complex system, the time series corresponding to one of the dynamic variables is transformed to an ε-recurrence network...
June 2017: Chaos
Julián Hurtado-López, David F Ramirez-Moreno, Terrence J Sejnowski
We propose a mathematical model of a continuous attractor network that controls social behaviors. The model is examined with bifurcation analysis and computer simulations. The results show that the model exhibits stable steady states and thresholds for steady state transitions corresponding to some experimentally observed behaviors, such as aggression control. The performance of the model and the relation with experimental evidence are discussed.
June 29, 2017: Journal of Computational Neuroscience
James M Kunert-Graf, Eli Shlizerman, Andrew Walker, J Nathan Kutz
The neural dynamics of the nematode Caenorhabditis elegans are experimentally low-dimensional and may be understood as long-timescale transitions between multiple low-dimensional attractors. Previous modeling work has found that dynamic models of the worm's full neuronal network are capable of generating reasonable dynamic responses to certain inputs, even when all neurons are treated as identical save for their connectivity. This study investigates such a model of C. elegans neuronal dynamics, finding that a wide variety of multistable responses are generated in response to varied inputs...
2017: Frontiers in Computational Neuroscience
Annika L A Nichols, Tomáš Eichler, Richard Latham, Manuel Zimmer
How the brain effectively switches between and maintains global states, such as sleep and wakefulness, is not yet understood. We used brainwide functional imaging at single-cell resolution to show that during the developmental stage of lethargus, the Caenorhabditis elegans brain is predisposed to global quiescence, characterized by systemic down-regulation of neuronal activity. Only a few specific neurons are exempt from this effect. In the absence of external arousing cues, this quiescent brain state arises by the convergence of neuronal activities toward a fixed-point attractor embedded in an otherwise dynamic neural state space...
June 23, 2017: Science
Jens Christian Claussen, Jurgita Skiecevičienė, Jun Wang, Philipp Rausch, Tom H Karlsen, Wolfgang Lieb, John F Baines, Andre Franke, Marc-Thorsten Hütt
The analysis of microbiome compositions in the human gut has gained increasing interest due to the broader availability of data and functional databases and substantial progress in data analysis methods, but also due to the high relevance of the microbiome in human health and disease. While most analyses infer interactions among highly abundant species, the large number of low-abundance species has received less attention. Here we present a novel analysis method based on Boolean operations applied to microbial co-occurrence patterns...
June 2017: PLoS Computational Biology
Dongya Jia, Mohit Kumar Jolly, Prakash Kulkarni, Herbert Levine
Waddington's epigenetic landscape, a famous metaphor in developmental biology, depicts how a stem cell progresses from an undifferentiated phenotype to a differentiated one. The concept of "landscape" in the context of dynamical systems theory represents a high-dimensional space, in which each cell phenotype is considered as an "attractor" that is determined by interactions between multiple molecular players, and is buffered against environmental fluctuations. In addition, biological noise is thought to play an important role during these cell-fate decisions and in fact controls transitions between different phenotypes...
June 22, 2017: Cancers
Marcello A Budroni, Andrea Baronchelli, Romualdo Pastor-Satorras
Methods connecting dynamical systems and graph theory have attracted increasing interest in the past few years, with applications ranging from a detailed comparison of different kinds of dynamics to the characterization of empirical data. Here we investigate the effects of the (multi)fractal properties of a signal, common in time series arising from chaotic dynamics or strange attractors, on the topology of a suitably projected network. Relying on the box-counting formalism, we map boxes into the nodes of a network and establish analytic expressions connecting the natural measure of a box with its degree in the graph representation...
May 2017: Physical Review. E
Arian Ashourvan, Shi Gu, Marcelo G Mattar, Jean M Vettel, Danielle S Bassett
Human brain dynamics can be viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing mental states. Many physically-inspired models of these dynamics define brain states based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. We study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions...
June 7, 2017: NeuroImage
Jiagui Wu, Shu-Wei Huang, Yongjun Huang, Hao Zhou, Jinghui Yang, Jia-Ming Liu, Mingbin Yu, Guoqiang Lo, Dim-Lee Kwong, Shukai Duan, Chee Wei Wong
Chaos has revolutionized the field of nonlinear science and stimulated foundational studies from neural networks, extreme event statistics, to physics of electron transport. Recent studies in cavity optomechanics provide a new platform to uncover quintessential architectures of chaos generation and the underlying physics. Here, we report the generation of dynamical chaos in silicon-based monolithic optomechanical oscillators, enabled by the strong and coupled nonlinearities of two-photon absorption induced Drude electron-hole plasma...
June 9, 2017: Nature Communications
Maria V Sanchez-Vives, Marcello Massimini, Maurizio Mattia
Slow oscillations have been suggested as the default emergent activity of the cortical network. This is a low complexity state that integrates neuronal, synaptic, and connectivity properties of the cortex. Shaped by variations of physiological parameters, slow oscillations provide information about the underlying healthy or pathological network. We review how this default activity is shaped, how it acts as a powerful attractor, and how getting out of it is necessary for the brain to recover the levels of complexity associated with conscious states...
June 7, 2017: Neuron
Hervé Guyon, Bruno Falissard, Jean-Luc Kop
Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes...
2017: Frontiers in Psychology
Jessie Peyre, Ahmed Hamraoui, Marco Faustini, Vincent Humblot, Niki Baccile
The surface self-assembly properties of acidic sophorolipids, a bolaform microbial glycolipids with pH-responsive properties in solution, were studied based on the chemical nature of the support and pH of the solution. Sophorolipids generally form micelles in water but formation of morphologies like platelets and twisted fibers depending on pH have also been reported. The surface self-assembly was achieved using dip-coating on three different substrates namely gold, silicon(111) and TiO2 anatase. Deposition conditions (dip-coating withdrawal speed, relative humidity, temperature) were tested, and it was found that optimum self-assembly occurs at a withdrawal speed of 1 mm s(-1), T of 25 °C and relative humidity of 25%...
June 14, 2017: Physical Chemistry Chemical Physics: PCCP
Timo Nachstedt, Christian Tetzlaff
Working memory stores and processes information received as a stream of continuously incoming stimuli. This requires accurate sequencing and it remains puzzling how this can be reliably achieved by the neuronal system as our perceptual inputs show a high degree of temporal variability. One hypothesis is that accurate timing is achieved by purely transient neuronal dynamics; by contrast a second hypothesis states that the underlying network dynamics are dominated by attractor states. In this study, we resolve this contradiction by theoretically investigating the performance of the system using stimuli with differently accurate timing...
May 30, 2017: Scientific Reports
Shirley Mark, Sandro Romani, Karel Jezek, Misha Tsodyks
Hippocampal place cells represent different environments with distinct neural activity patterns. Following an abrupt switch between two familiar configurations of visual cues defining two environments, the hippocampal neural activity pattern switches almost immediately to the corresponding representation. Surprisingly, during a transient period following the switch to the new environment, occasional fast transitions of activity patterns between the representations (flickering) were observed (Jezek et al. 2011)...
May 30, 2017: Hippocampus
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