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Attractor network

Alain Nogaret, Alastair King
Inhibitory neural networks are found to encode high volumes of information through delayed inhibition. We show that inhibition delay increases storage capacity through a Stirling transform of the minimum capacity which stabilizes locally coherent oscillations. We obtain both the exact and asymptotic formulas for the total number of dynamic attractors. Our results predict a (ln2)^{-N}-fold increase in capacity for an N-neuron network and demonstrate high-density associative memories which host a maximum number of oscillations in analog neural devices...
March 2018: Physical Review. E
Feibo Jiang, Li Dong, Qianwei Dai
The traditional artificial neural network (ANN) inversion of electrical resistivity imaging (ERI) based on gradient descent algorithm is known to be inept for its low computation efficiency and does not ensure global convergence. In order to solve above problems, a kernel principal component wavelet neural network (KPCWNN) trained by an improved shuffled frog leaping algorithm (ISFLA) method is proposed in this study. An additional kernel principal component (KPC) layer is applied to reduce the dimensionality of apparent resistivity data and increase the computational efficiency of wavelet neural network (WNN)...
April 24, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Guillaume Hennequin, Yashar Ahmadian, Daniel B Rubin, Máté Lengyel, Kenneth D Miller
Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states ("attractors") or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic "stabilized supralinear network"), best explains these modulations...
May 16, 2018: Neuron
Kathryn Hedrick, Kechen Zhang
The theory of attractor neural networks has been influential in our understanding of the neural processes underlying spatial, declarative, and episodic memory. Many theoretical studies focus on the inherent properties of an attractor, such as its structure and capacity. Relatively little is known about how an attractor neural network responds to external inputs, which often carry conflicting information about a stimulus. In this paper we analyze the behavior of an attractor neural network driven by two conflicting external inputs...
May 16, 2018: Journal of Mathematical Neuroscience
Xiao Gan, Réka Albert
Analyzing the long-term behaviors (attractors) of dynamic models of biological networks can provide valuable insight. We propose a general method that can find the attractors of multilevel discrete dynamical systems by extending a method that finds the attractors of a Boolean network model. The previous method is based on finding stable motifs, subgraphs whose nodes' states can stabilize on their own. We extend the framework from binary states to any finite discrete levels by creating a virtual node for each level of a multilevel node, and describing each virtual node with a quasi-Boolean function...
April 2018: Physical Review. E
Julian D Schwab, Hans A Kestler
A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior-so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors...
2018: Frontiers in Physiology
Viola Folli, Giorgio Gosti, Marco Leonetti, Giancarlo Ruocco
We study with numerical simulation the possible limit behaviors of synchronous discrete-time deterministic recurrent neural networks composed of N binary neurons as a function of a network's level of dilution and asymmetry. The network dilution measures the fraction of neuron couples that are connected, and the network asymmetry measures to what extent the underlying connectivity matrix is asymmetric. For each given neural network, we study the dynamical evolution of all the different initial conditions, thus characterizing the full dynamical landscape without imposing any learning rule...
April 16, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Sang-Mok Choo, Byunghyun Ban, Jae Il Joo, Kwang-Hyun Cho
BACKGROUND: Controlling complex molecular regulatory networks is getting a growing attention as it can provide a systematic way of driving any cellular state to a desired cell phenotypic state. A number of recent studies suggested various control methods, but there is still deficiency in finding out practically useful control targets that ensure convergence of any initial network state to one of attractor states corresponding to a desired cell phenotype. RESULTS: To find out practically useful control targets, we introduce a new concept of phenotype control kernel (PCK) for a Boolean network, defined as the collection of all minimal sets of control nodes having their fixed state values that can generate all possible control sets which eventually drive any initial state to one of attractor states corresponding to a particular cell phenotype of interest...
April 5, 2018: BMC Systems Biology
Qinbin He, Zhile Xia, Bin Lin
Boolean network models provide an efficient way for studying gene regulatory networks. The main dynamics of a Boolean network is determined by its attractors. Attractor calculation plays a key role for analyzing Boolean gene regulatory networks. An approach of attractor calculation was proposed in this study, which combined the predecessor approach and the logic unsatisfiability approach to accelerate attractor calculation. The proposed algorithm is effective to calculate all attractors for large-scale Boolean gene regulatory networks even the networks with a relatively large average degree...
March 29, 2018: Journal of Theoretical Biology
D Ibáñez-Soria, J Garcia-Ojalvo, A Soria-Frisch, G Ruffini
Generalized synchronization between coupled dynamical systems is a phenomenon of relevance in applications that range from secure communications to physiological modelling. Here, we test the capabilities of reservoir computing and, in particular, echo state networks for the detection of generalized synchronization. A nonlinear dynamical system consisting of two coupled Rössler chaotic attractors is used to generate temporal series consisting of time-locked generalized synchronized sequences interleaved with unsynchronized ones...
March 2018: Chaos
Daebeom Park, Ho-Sung Lee, Jun Hyuk Kang, Seon-Myeong Kim, Jeong-Ryeol Gong, Kwang-Hyun Cho
Apoptosis and hypertrophy of cardiomyocytes are the primary causes of heart failure (HF), a global leading cause of death, and are regulated through the complicated intracellular signaling network, limiting the development of effective treatments due to its complexity. To identify effective therapeutic strategies for HF at a system level, we develop a large-scale comprehensive mathematical model of the cardiac signaling network by integrating all available experimental evidence. Attractor landscape analysis of the network model identifies distinct sets of control nodes that effectively suppress apoptosis and hypertrophy of cardiomyocytes under ischemic or pressure overload-induced HF, the two major types of HF...
March 20, 2018: Journal of Molecular Cell Biology
Diego Fasoli, Anna Cattani, Stefano Panzeri
Despite their biological plausibility, neural network models with asymmetric weights are rarely solved analytically, and closed-form solutions are available only in some limiting cases or in some mean-field approximations. We found exact analytical solutions of an asymmetric spin model of neural networks with arbitrary size without resorting to any approximation, and we comprehensively studied its dynamical and statistical properties. The network had discrete time evolution equations and binary firing rates, and it could be driven by noise with any distribution...
March 22, 2018: Neural Computation
Stalin Muñoz, Miguel Carrillo, Eugenio Azpeitia, David A Rosenblueth
Boolean networks are important models of biochemical systems, located at the high end of the abstraction spectrum. A number of Boolean gene networks have been inferred following essentially the same method. Such a method first considers experimental data for a typically underdetermined "regulation" graph. Next, Boolean networks are inferred by using biological constraints to narrow the search space, such as a desired set of (fixed-point or cyclic) attractors. We describe Griffin , a computer tool enhancing this method...
2018: Frontiers in Genetics
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
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...
March 28, 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 15, 2018: Nature
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