scholarly journals Mechanisms of Adaptive Spatial Integration in a Neural Model of Cortical Motion Processing

2011 ◽  
pp. 110-119 ◽  
Author(s):  
Stefan Ringbauer ◽  
Stephan Tschechne ◽  
Heiko Neumann
Keyword(s):  
Neural Model ◽  
Motion Processing ◽  
Spatial Integration

Local and Global Representations of Velocity: Transparency, Opponency, and Global Direction Perception

Perception ◽  
1997 ◽  
Vol 26 (8) ◽  
pp. 995-1010 ◽  
Author(s):  
Oliver Braddick
Keyword(s):  
Visual Motion ◽  
Multiple Scales ◽  
Human Subjects ◽  
Motion Processing ◽  
Spatial Integration ◽  
Local Motion ◽  
Directional Information ◽  
Object Based ◽  
Transparent Display ◽  
Winner Take All

Human subjects can perceive global motion or motions in displays containing diverse local motions, implying representation of velocity at multiple scales. The phenomena of flexible global direction judgments, and especially of motion transparency, also raise the issue of whether the representation of velocity at any one scale is single-valued or multi-valued. A new performance-based measure of transparency confirms that the visual system represents directional information for each component of a transparent display. However, results with the locally paired random-dot display introduced by Qian et al, show that representations of multiple velocities do not coexist at the finest spatial scale of motion analysis. Functionally distinct scales of motion processing may be associated with (i) local motion detectors which show a strong winner-take-all interaction; (ii) spatial integration of local signals to disambiguate velocity; (iii) selection of reliable velocity signals as proposed in the model of Nowlan and Sejnowski; (iv) object-based or surface-based representations that are not necessarily organised in a fixed spatial matrix. These possibilities are discussed in relation to the neurobiological organisation of the visual motion pathway.

Neural Modelling of Disparity Influences in Optic-Flow Processing

Perception ◽  
1997 ◽  
Vol 26 (1_suppl) ◽  
pp. 225-225
Author(s):  
M Lappe ◽  
A Grigo
Keyword(s):  
Optic Flow ◽  
Weighting Function ◽  
Neural Model ◽  
Slight Reduction ◽  
Spatial Integration ◽  
Area Mt ◽  
Expansion Pattern ◽  
True Position ◽  
Flow Vectors ◽  
Experimental Findings

Last year, we reported a new disparity effect on optic-flow perception. It occurs when a flow field of radially moving random dots is transparently superimposed by a unidirectional motion pattern. Subjects asked to locate the centre of the expansion pattern perceive it shifted away from its true position (Duffy and Wurtz, 1993 Vision Research33 1481 – 1490). The magnitude of this shift depends on binocular disparity between the two superimposed patterns: presenting the translation in front of the expansion causes a massive reduction of the shift, presenting it behind the expansion yields only a slight reduction. To determine possible mechanisms for this interaction, we tried to model our results by modifying a previously developed neural model for optic-flow processing (Lappe and Rauschecker, 1995 Vision Research35 1619 – 1631). We compared three possible mechanisms of disparity influence. The first was based on the assumption that the absolute distance of the stimuli from the observer was known. The second was based on a disparity-based spatial integration, as found in ‘tuned’ neurons in primate visual area MT. Neither could account for our data. Finally we included a disparity-dependent weighting function in the model, assuming that distant flow vectors contribute more strongly than near ones to the processing of optic flow. This reproduced the experimental findings. Surprisingly, the optimal weighting function was similar to the disparity-dependence of the so-called ‘far-neurons’ in area MT. We conclude that the visual system uses disparity in optic-flow processing by putting special emphasis on distant flow vectors, and that far-neurons in MT might serve as a neuronal substrate for this.

scholarly journals A neural model of high-level motion processing: Line motion and formotion dynamics

1997 ◽  
Vol 37 (21) ◽  
pp. 3037-3059 ◽  
Author(s):  
Aijaz A. Baloch ◽  
Stephen Grossberg
Keyword(s):  
Neural Model ◽  
Motion Processing ◽  
Processing Line ◽  
High Level

The Palestinian Refugee Camps In Jordan: Between National Identity and Socio-economic Integration

2004 ◽  
Vol 2 (2) ◽  
pp. 198-221 ◽  
Author(s):  
Ala Al-Hamarneh
Keyword(s):  
United Nations ◽  
Collective Memory ◽  
Economic Integration ◽  
Refugee Camps ◽  
Spatial Integration ◽  
Political Activities ◽  
Cultural Traditions ◽  
The Past ◽  
Distinct Character ◽  
Palestinian Refugee

At least 50 per cent of the population of Jordan is of Palestinian origin. Some 20 per cent of the registered refugees live in ten internationally organized camps, and another 20 per cent in four locally organized camps and numerous informal camps. The camps organized by the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UNRWA) play a major role in keeping Palestinian identity alive. That identity reflects the refugees' rich cultural traditions, political activities, as well as their collective memory, and the distinct character of each camp. Over the past two decades integration of the refugees within Jordanian society has increased. This paper analyses the transformation of the identity of the camp dwellers, as well as their spatial integration in Jordan, and other historical and contemporary factors contributing to this transformation.

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

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.

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