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Attractor network

Dylan R Muir
Recurrent neural network architectures can have useful computational properties, with complex temporal dynamics and input-sensitive attractor states. However, evaluation of recurrent dynamic architectures requires solving systems of differential equations, and the number of evaluations required to determine their response to a given input can vary with the input or can be indeterminate altogether in the case of oscillations or instability. In feedforward networks, by contrast, only a single pass through the network is needed to determine the response to a given input...
November 21, 2017: Neural Computation
Anthony Szedlak, Spencer Sims, Nicholas Smith, Giovanni Paternostro, Carlo Piermarocchi
Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics...
November 17, 2017: PLoS Computational Biology
Meng-Li Zheng, Nai-Kang Zhou, De-Liang Huang, Cheng-Hua Luo
PURPOSE: The purpose of this study was to explore the pathway cross-talks and key pathways in non-small cell lung cancer (NSCLC) to better understand the underlying pathological mechanism. METHODS: Integrated gene expression data, pathway data and protein-protein interaction (PPI) data were assessed to identify the pathway regulatory interactions in NSCLC, and constructed the background and disease pathway crosstalk networks, respectively. In this work, the attractor method was implemented to identified the differential pathways, and the rank product (RP) algorithm was used to determine the importance of pathways...
September 2017: Journal of B.U.ON.: Official Journal of the Balkan Union of Oncology
Michael E Hasselmo, James R Hinman, Holger Dannenberg, Chantal E Stern
Episodic memory involves coding of the spatial location and time of individual events. Coding of space and time is also relevant to working memory, spatial navigation, and the disambiguation of overlapping memory representations. Neurophysiological data demonstrate that neuronal activity codes the current, past and future location of an animal as well as temporal intervals within a task. Models have addressed how neural coding of space and time for memory function could arise, with both dimensions coded by the same neurons...
October 2017: Current Opinion in Behavioral Sciences
Alexandre A P Rodrigues
In the framework of the generalized Lotka-Volterra model, solutions representing multispecies sequential competition can be predictable with high probability. In this paper, we show that it occurs because the corresponding "heteroclinic channel" forms part of an attractor. We prove that, generically, in an attracting heteroclinic network involving a finite number of hyperbolic and non-resonant saddle-equilibria whose linearization has only real eigenvalues, the connections corresponding to the most positive expanding eigenvalues form part of an attractor (observable in numerical simulations)...
October 2017: Chaos
Thomas Rost, Moritz Deger, Martin P Nawrot
Balanced networks are a frequently employed basic model for neuronal networks in the mammalian neocortex. Large numbers of excitatory and inhibitory neurons are recurrently connected so that the numerous positive and negative inputs that each neuron receives cancel out on average. Neuronal firing is therefore driven by fluctuations in the input and resembles the irregular and asynchronous activity observed in cortical in vivo data. Recently, the balanced network model has been extended to accommodate clusters of strongly interconnected excitatory neurons in order to explain persistent activity in working memory-related tasks...
October 26, 2017: Biological Cybernetics
Lorenz Gönner, Julien Vitay, Fred H Hamker
Hippocampal place-cell sequences observed during awake immobility often represent previous experience, suggesting a role in memory processes. However, recent reports of goals being overrepresented in sequential activity suggest a role in short-term planning, although a detailed understanding of the origins of hippocampal sequential activity and of its functional role is still lacking. In particular, it is unknown which mechanism could support efficient planning by generating place-cell sequences biased toward known goal locations, in an adaptive and constructive fashion...
2017: Frontiers in Computational Neuroscience
Minkyu Choi, Jun Tani
This letter proposes a novel predictive coding type neural network model, the predictive multiple spatiotemporal scales recurrent neural network (P-MSTRNN). The P-MSTRNN learns to predict visually perceived human whole-body cyclic movement patterns by exploiting multiscale spatiotemporal constraints imposed on network dynamics by using differently sized receptive fields as well as different time constant values for each layer. After learning, the network can imitate target movement patterns by inferring or recognizing corresponding intentions by means of the regression of prediction error...
October 24, 2017: Neural Computation
Shun-Ichi Amari, Tomoko Ozeki, Ryo Karakida, Yuki Yoshida, Masato Okada
The dynamics of supervised learning play a main role in deep learning, which takes place in the parameter space of a multilayer perceptron (MLP). We review the history of supervised stochastic gradient learning, focusing on its singular structure and natural gradient. The parameter space includes singular regions in which parameters are not identifiable. One of our results is a full exploration of the dynamical behaviors of stochastic gradient learning in an elementary singular network. The bad news is its pathological nature, in which part of the singular region becomes an attractor and another part a repulser at the same time, forming a Milnor attractor...
October 24, 2017: Neural Computation
Ran Rubin, L F Abbott, Haim Sompolinsky
Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well as the robustness of attractor states of networks of neurons performing memory tasks. We find that robustness to output noise requires synaptic connections to be in a balanced regime in which excitation and inhibition are strong and largely cancel each other. We evaluate the conditions required for this regime to exist and determine the properties of networks operating within it...
October 31, 2017: Proceedings of the National Academy of Sciences of the United States of America
Edmund T Rolls, W Patrick C Mills
A fundamental question is how the cerebral neocortex operates functionally, computationally. The cerebral neocortex with its superficial and deep layers and highly developed recurrent collateral systems that provide a basis for memory-related processing might perform somewhat different computations in the superficial and deep layers. Here we take into account the quantitative connectivity within and between laminae. Using integrate-and-fire neuronal network simulations that incorporate this connectivity, we first show that attractor networks implemented in the deep layers that are activated by the superficial layers could be partly independent in that the deep layers might have a different time course, which might because of adaptation be more transient and useful for outputs from the neocortex...
October 16, 2017: Neurobiology of Learning and Memory
Jesse Sterling Bettinger
Under a variety of conditions, non-linear systems with many degrees of freedom tend to evolve towards complexity and criticality. Over the last few decades, a steady proliferation of models re: nonlinear and far-from-equilibrium thermodynamics of metastable, many-valued systems arose, serving as attributes of a 'critical' attractor landscape. Building off recent data citing trademark aspects of criticality in the brain-including: power-laws, scale-free (1/f) behavior (scale invariance, or scale independence, critical slowing, and avalanches-it has been conjectured that operating at criticality entails functional advantages such as: optimized neural computation and information processing; memory; large dynamical ranges; long-range communication; and "enhanced ability to react to highly diverse stimuli...
October 11, 2017: Progress in Biophysics and Molecular Biology
Grant Gillary, Rüdiger von der Heydt, Ernst Niebur
Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient...
December 2017: Journal of Computational Neuroscience
Vahid Rahmati, Knut Kirmse, Knut Holthoff, Lars Schwabe, Stefan J Kiebel
During neocortical development, network activity undergoes a dramatic transition from largely synchronized, so-called cluster activity, to a relatively sparse pattern around the time of eye-opening in rodents. Biophysical mechanisms underlying this sparsification phenomenon remain poorly understood. Here, we present a dynamic systems modeling study of a developing neural network that provides the first mechanistic insights into sparsification. We find that the rest state of immature networks is strongly affected by the dynamics of a transient, unstable state hidden in their firing activities, allowing these networks to either be silent or generate large cluster activity...
October 12, 2017: Scientific Reports
Jian Jiang, Xiang-Yun Yin, Xue-Wen Song, Dong Xie, Hui-Juan Xu, Jing Yang, Li-Rong Sun
PURPOSE: To extract feature ego-modules and pathways in childhood acute lymphoblastic leukemia (ALL) resistant to prednisolone treatment, and further to explore the mechanisms behind prednisolone resistance. MATERIALS AND METHODS: EgoNet algorithm was used to identify candidate ego-network modules, mainly via constructing differential co-expression network (DCN); selecting ego genes; collecting ego-network modules; refining candidate modules. Afterwards, statistical significance was calculated for these candidate modules...
October 11, 2017: Hematology (Amsterdam, Netherlands)
Rishu Kumar Singh, Sitabhra Sinha
Although interdependent systems have usually been associated with increased fragility, we show that strengthening the interdependence between dynamical processes on different networks can make them more likely to survive over long times. By coupling the dynamics of networks that in isolation exhibit catastrophic collapse with extinction of nodal activity, we demonstrate system-wide persistence of activity for an optimal range of interdependence between the networks. This is related to the appearance of attractors of the global dynamics comprising disjoint sets ("islands") of stable activity...
August 2017: Physical Review. E
Denggui Fan, Qingyun Wang, Jianzhong Su, Hongguang Xi
It is believed that thalamic reticular nucleus (TRN) controls spindles and spike-wave discharges (SWD) in seizure or sleeping processes. The dynamical mechanisms of spatiotemporal evolutions between these two types of activity, however, are not well understood. In light of this, we first use a single-compartment thalamocortical neural field model to investigate the effects of TRN on occurrence of SWD and its transition. Results show that the increasing inhibition from TRN to specific relay nuclei (SRN) can lead to the transition of system from SWD to slow-wave oscillation...
December 2017: Journal of Computational Neuroscience
Réka Albert, Biswa R Acharya, Byeong Wook Jeon, Jorge G T Zañudo, Mengmeng Zhu, Karim Osman, Sarah M Assmann
Stomata, microscopic pores in leaf surfaces through which water loss and carbon dioxide uptake occur, are closed in response to drought by the phytohormone abscisic acid (ABA). This process is vital for drought tolerance and has been the topic of extensive experimental investigation in the last decades. Although a core signaling chain has been elucidated consisting of ABA binding to receptors, which alleviates negative regulation by protein phosphatases 2C (PP2Cs) of the protein kinase OPEN STOMATA 1 (OST1) and ultimately results in activation of anion channels, osmotic water loss, and stomatal closure, over 70 additional components have been identified, yet their relationships with each other and the core components are poorly elucidated...
September 2017: PLoS Biology
Dipali Kosey, Shailza Singh
Cutaneous leishmaniasis is the most common form of lesihmaniasis, caused by Leishmania major and is spread by the bite of a sandfly .This species infects the macrophages and dendritic cells Due to multi-drug resistance, there is a need for a new therapeutic technique. Recently, a novel molecular motor of Leishmania, Myosin XXI, was classified and characterized. In addition, the drug resistance in this organism has been linked with the overexpression of ABC transporters. Systems biology aims to study the simulation and modeling of natural biological systems whereas synthetic biology deals with building novel and artificial biological parts and devices  Together they have contributed enormously to drug discovery, vaccine design and development, infectious disease detection and diagnostics...
2017: F1000Research
Janice Ryan
This exploratory, evidence-based practice research study focuses on presenting a plausible mesoscopic brain dynamics hypothesis for the benefits of treating clients with psychosocial and cognitive challenges using a mindful therapeutic approach and multi-sensory environments. After an extensive neuroscientific review of the therapeutic benefits of mindfulness, a multi-sensory environment is presented as a window of therapeutic opportunity to more quickly and efficiently facilitate the neurobiological experience of becoming more mindful or conscious of self and environment...
October 2017: Nonlinear Dynamics, Psychology, and Life Sciences
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