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Attractor network

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
Meichen Yu, Arjan Hillebrand, Alida A Gouw, Cornelis J Stam
We propose a new measure, horizontal visibility graph transfer entropy (HVG-TE), to estimate the direction of information flow between pairs of time series. HVG-TE quantifies the transfer entropy between the degree sequences of horizontal visibility graphs derived from original time series. Twenty-one Rössler attractors unidirectionally coupled in the posterior-to-anterior direction were used to simulate 21-channel Electroencephalography (EEG) brain networks and validate the performance of the HVG-TE. We showed that the HVG-TE is robust to different levels of coupling strengths between the coupled Rössler attractors, a wide range of time delays, different sample sizes, the effects of noise and linear mixing, and the choice of reference for EEG data...
May 21, 2017: NeuroImage
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
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