Stochastic stability of a neural model for binocular rivalry

2014 ◽  
Vol 1 ◽  
pp. 739-742
Author(s):  
Tetsuya Shimokawa ◽  
Kenji Leibnitz ◽  
Ferdinand Peper
Keyword(s):  
Binocular Rivalry ◽  
Stochastic Stability ◽  
Neural Model

Lateral Neural Model of Binocular Rivalry

2003 ◽  
Vol 15 (12) ◽  
pp. 2863-2882 ◽  
Author(s):  
Lars Stollenwerk ◽  
Mathias Bode
Keyword(s):  
Binocular Rivalry ◽  
Contrast Ratio ◽  
Neural Model ◽  
Inhibitory Neurons ◽  
Basic Block ◽  
Lateral Coupling ◽  
Astable Multivibrator ◽  
Perception Time ◽  
Time Changes ◽  
Stochastic Oscillations

This article introduces a two-dimensionally extended, neuron-based model for binocular rivalry. The basic block of the model is a certain type of astable multivibrator comprising excitatory and inhibitory neurons. Many of these blocks are laterally coupled on a medium range to provide a two-dimensional layer. Our model, like others, needs noise to reproduce typical stochastic oscillations. Due to its spatial extension, the noise has to be laterally correlated. When the contrast ratio of the pictures varies, their share of the perception time changes in a way that is known from comparable experimental data (Levelt, 1965; Mueller & Blake, 1989). This is a result of the lateral coupling and not a property of the single model block. The presentation of simple and suitable inhomogeneous stimuli leads to an easily describable perception of periodically moving pictures like propagating fronts or breathing spots. This suggests new experiments. Under certain conditions, a bifurcation from static to moving perceptions is predicted and may be checked and employed by future experiments. Recent “paradox” (Logothetis, 1999) observations of two different neuron classes in cortical areas MT (Logothetis & Schall, 1989) and V4 (Leopold & Logothetis, 1996), one that behaves alike under rivaling and nonrivaling conditions and another that drastically changes its behavior, are interpreted as being related to separate inhibitor neurons.

Neural model of temporal and stochastic properties of binocular rivalry

2000 ◽  
Vol 32-33 ◽  
pp. 843-853 ◽  
Author(s):  
George J. Kalarickal ◽  
Jonathan A. Marshall
Keyword(s):  
Binocular Rivalry ◽  
Neural Model ◽  
Stochastic Properties

No impact of affective person knowledge on visual awareness: Evidence from binocular rivalry and continuous flash suppression.

Emotion ◽  
2017 ◽  
Vol 17 (8) ◽  
pp. 1199-1207 ◽  
Author(s):  
Timo Stein ◽  
Caitlyn Grubb ◽  
Maria Bertrand ◽  
Seh Min Suh ◽  
Sara C. Verosky
Keyword(s):  
Binocular Rivalry ◽  
Visual Awareness ◽  
Continuous Flash Suppression

Oil Saturation Log Prediction Using Neural Network in New Steamflood Area

2020 ◽  
Author(s):  
A. Syahputra
Keyword(s):  
Neural Network ◽  
Water Saturation ◽  
Prediction Method ◽  
Neural Model ◽  
Training Model ◽  
Acquisition Time ◽  
Oil Saturation ◽  
Full Field ◽  
Remaining Oil ◽  
Observation Wells

Surveillance is very important in managing a steamflood project. On the current surveillance plan, Temperature and steam ID logs are acquired on observation wells at least every year while CO log (oil saturation log or SO log) every 3 years. Based on those surveillance logs, a dynamic full field reservoir model is updated quarterly. Typically, a high depletion rate happens in a new steamflood area as a function of drainage activities and steamflood injection. Due to different acquisition time, there is a possibility of misalignment or information gaps between remaining oil maps (ie: net pay, average oil saturation or hydrocarbon pore thickness map) with steam chest map, for example a case of high remaining oil on high steam saturation interval. The methodology that is used to predict oil saturation log is neural network. In this neural network method, open hole observation wells logs (static reservoir log) such as vshale, porosity, water saturation effective, and pay non pay interval), dynamic reservoir logs as temperature, steam saturation, oil saturation, and acquisition time are used as input. A study case of a new steamflood area with 16 patterns of single reservoir target used 6 active observation wells and 15 complete logs sets (temperature, steam ID, and CO log), 19 incomplete logs sets (only temperature and steam ID) since 2014 to 2019. Those data were divided as follows ~80% of completed log set data for neural network training model and ~20% of completed log set data for testing the model. As the result of neural model testing, R2 is score 0.86 with RMS 5% oil saturation. In this testing step, oil saturation log prediction is compared to actual data. Only minor data that shows different oil saturation value and overall shape of oil saturation logs are match. This neural network model is then used for oil saturation log prediction in 19 incomplete log set. The oil saturation log prediction method can fill the gap of data to better describe the depletion process in a new steamflood area. This method also helps to align steam map and remaining oil to support reservoir management in a steamflood project.

Toward a Neural Model of the Openness-Psychoticism Dimension: Functional Connectivity in the Default and Frontoparietal Control Networks

2019 ◽  
Author(s):  
Scott D. Blain ◽  
Rachael Grazioplene ◽  
Yizhou Ma ◽  
Colin G. DeYoung
Keyword(s):  
Functional Connectivity ◽  
Structural Equation ◽  
Psychotic Disorders ◽  
Neural Model ◽  
Equation Modeling ◽  
Control Networks ◽  
Psychosis Proneness ◽  
Human Connectome Project ◽  
Intrinsic Connectivity Networks ◽  
Personality Psychopathology

Psychosis proneness has been linked to heightened Openness to Experience and to cognitive deficits. Openness and psychotic disorders are associated with the default and frontoparietal networks, and the latter network is also robustly associated with intelligence. We tested the hypothesis that functional connectivity of the default and frontoparietal networks is a neural correlate of the openness-psychoticism dimension. Participants in the Human Connectome Project (N = 1003) completed measures of psychoticism, openness, and intelligence. Resting state functional magnetic resonance imaging was used to identify intrinsic connectivity networks. Structural equation modeling revealed relations among personality, intelligence, and network coherence. Psychoticism, openness, and especially their shared variance, were related positively to default network coherence and negatively to frontoparietal coherence. These associations remained after controlling for intelligence. Intelligence was positively related to frontoparietal coherence. Research suggests psychoticism and openness are linked in part through their association with connectivity in networks involving experiential simulation and cognitive control. We propose a model of psychosis risk that highlights roles of the default and frontoparietal networks. Findings echo research on functional connectivity in psychosis patients, suggesting shared mechanisms across the personality-psychopathology continuum.

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