Configurational pattern discrimination responsible for dishabituation in common toads Bufo bufo (L.): Behavioral tests of the predictions of a neural model

1992 ◽  
Vol 170 (3) ◽  
Author(s):  
DeLiang Wang ◽  
J�rg-Peter Ewert
Keyword(s):  
Pattern Discrimination ◽  
Neural Model ◽  
Bufo Bufo ◽  
Behavioral Tests

scholarly journals Local Perceptual Learning for Motion Pattern Discrimination: a Neural Model

2010 ◽  
Vol 10 (7) ◽  
pp. 1110-1110
Author(s):  
S. Ringbauer ◽  
F. Raudies ◽  
H. Neumann
Keyword(s):  
Perceptual Learning ◽  
Pattern Discrimination ◽  
Neural Model ◽  
Motion Pattern

Alterations in the number of motoneurons containing immunoreactive calcitonin gene-related peptide(CGRP)& choline acetyltransferase(ChAT) in the cervical spinal cord of the wobbler mouse during the development of the motoneuron disease

1995 ◽  
Vol 53 ◽  
pp. 970-971
Author(s):  
L. Vacca-Galloway ◽  
Y.Q. Zhang ◽  
P. Bose ◽  
S.H. Zhang
Keyword(s):  
Spinal Cord ◽  
Early Stage ◽  
Cervical Spinal Cord ◽  
Wild Type Mouse ◽  
Reflex Response ◽  
Mouse Strains ◽  
Behavioral Tests ◽  
Wobbler Mouse ◽  
Stage 4 ◽  
Stage 1

The Wobbler mouse (wr) has been studied as a model for inherited human motoneuron diseases (MNDs). Using behavioral tests for forelimb power, walking, climbing, and the “clasp-like reflex” response, the progress of the MND can be categorized into early (Stage 1, age 21 days) and late (Stage 4, age 3 months) stages. Age-and sex-matched normal phenotype littermates (NFR/wr) were used as controls (Stage 0), as well as mice from two related wild-type mouse strains: NFR/N and a C57BI/6N. Using behavioral tests, we also detected pre-symptomatic Wobblers at postnatal ages 7 and 14 days. The mice were anesthetized and perfusion-fixed for immunocytochemical (ICC) of CGRP and ChAT in the spinal cord (C3 to C5).Using computerized morphomety (Vidas, Zeiss), the numbers of IR-CGRP labelled motoneurons were significantly lower in 14 day old Wobbler specimens compared with the controls (Fig. 1). The same trend was observed at 21 days (Stage 1) and 3 months (Stage 4). The IR-CGRP-containing motoneurons in the Wobbler specimens declined progressively with age.

Spatial Pattern Discrimination in Rats

2003 ◽  
Author(s):  
Kelly A. Digian ◽  
Michael Brown
Keyword(s):  
Spatial Pattern ◽  
Pattern Discrimination

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

Discharge Pattern Discrimination for Composite Insulation System in Vacuum

2016 ◽  
Vol 136 (9) ◽  
pp. 594-602 ◽  
Author(s):  
Fei Kong ◽  
Hiroki Kojima ◽  
Toshinori Kimura ◽  
Mitsuru Tsukima ◽  
Naoki Hayakawa
Keyword(s):  
Pattern Discrimination ◽  
Discharge Pattern ◽  
Insulation System

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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