Competitive Activation of a Methyl C−H Bond of Dimethylformamide at an Iridium Center

2009 ◽  
Vol 28 (14) ◽  
pp. 4229-4230 ◽  
Author(s):  
Valerie J. Scott ◽  
Lawrence M. Henling ◽  
Michael W. Day ◽  
John E. Bercaw ◽  
Jay A. Labinger
Keyword(s):  
Competitive Activation

Competitive activation of C–H and C–X bonds in gas phase reaction of Pt with CH2FCl: A DFT study

2010 ◽  
Vol 959 (1-3) ◽  
pp. 8-14 ◽  
Author(s):  
Tao Hong Li ◽  
Chuan Ming Wang ◽  
Xiao Guang Xie ◽  
Zhu Ming Jian
Keyword(s):  
Gas Phase ◽  
Dft Study ◽  
Phase Reaction ◽  
Gas Phase Reaction ◽  
Competitive Activation

Convergence of competitive activation models based on virtual lateral inhibition

1995 ◽  
Vol 8 (6) ◽  
pp. 865-875 ◽  
Author(s):  
Frank E. McFadden
Keyword(s):  
Lateral Inhibition ◽  
Competitive Activation

Learning Competition and Cooperation

1993 ◽  
Vol 5 (2) ◽  
pp. 242-259 ◽  
Author(s):  
Sungzoon Cho ◽  
James A. Reggia
Keyword(s):  
Learning Rule ◽  
Connection Strength ◽  
Context Sensitive ◽  
Competition And Cooperation ◽  
Activation Mechanisms ◽  
Exclusive Or ◽  
Functional Mechanisms ◽  
Inhibitory Interactions ◽  
Competitive Activation ◽  
Or Task

Competitive activation mechanisms introduce competitive or inhibitory interactions between units through functional mechanisms instead of inhibitory connections. A unit receives input from another unit proportional to its own activation as well as to that of the sending unit and the connection strength between the two. This, plus the finite output from any unit, induces competition among units that receive activation from the same unit. Here we present a backpropagation learning rule for use with competitive activation mechanisms and show empirically how this learning rule successfully trains networks to perform an exclusive-OR task and a diagnosis task. In particular, networks trained by this learning rule are found to outperform standard backpropagation networks with novel patterns in the diagnosis problem. The ability of competitive networks to bring about context-sensitive competition and cooperation among a set of units proved to be crucial in diagnosing multiple disorders.

scholarly journals Object Recognition’s Garden Path: Low Spatial Frequencies

2020 ◽  
Author(s):  
JOHN ROHRLICH ◽  
Tsung-Ren Huang ◽  
Thomas E. Hazy ◽  
Randall C. O'Reilly
Keyword(s):  
Significant Delay ◽  
Top Down ◽  
Wrong Answer ◽  
Garden Path ◽  
Spatial Frequencies ◽  
Low Pass ◽  
Blurred Image ◽  
Delayed Recognition ◽  
Cortical Influence ◽  
Competitive Activation

Several experiments, notably one done by Bruner and Potter (1964), have demonstrated delayed object recognition when viewing a blurred image gradually come into focus. Bruner and Potter (1964) suggested that the wrong answer is held until there is an obvious contradiction. Others have hypothesized that “competitive activation” is responsible for delayed recognition. The results of the experiments reported in this paper are consistent with a third hypothesis, that delayed recognition is due to an initial organization of image elements that is incompatible with correct recognition and that the initial grouping and figure-ground perception, among other aspects of organization, drive subsequent perception via top-down cortical pathways. A total of 7 experiments using 3 forms of degradation supported this hypothesis. Images degraded by low-pass filtering produced significant delay in recognition, while degradation by fragmentation did not, and a third form of degradation similar to fragmentation mitigated the effect. The experiments also demonstrate that if images are low-pass filtered delayed recognition occurs with presentations of as little as 100 ms and early presentations lead to delayed recognition over long intertrial intervals, at least 105 seconds. Further, support for the hypothesis that top-down cortical influence is key to this phenomenon came from an experiment showing that masking eliminates delayed recognition for short presentations. Taken together these results support a hypothesis that delayed recognition is due to errors in perceptual organization that lead to incorrect responses and these errors are fostered by low spatial frequencies.

Competitive Activation of N−C and C−H Bonds of the PNP Framework by Monovalent Rhodium and Iridium

2005 ◽  
Vol 24 (14) ◽  
pp. 3487-3499 ◽  
Author(s):  
Wei Weng ◽  
Chengyun Guo ◽  
Claudia Moura ◽  
Lin Yang ◽  
Bruce M. Foxman ◽  
...  
Keyword(s):  
Competitive Activation

A COMPETITIVE ACTIVATION NEURAL NETWORK MODEL FOR THE WEIGHTED MINIMUM VERTEX COVERING

1996 ◽  
Vol 07 (05) ◽  
pp. 607-616 ◽  
Author(s):  
A. LE GALL ◽  
V. ZISSIMOPOULOS
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Covering Problem ◽  
Stable States ◽  
Weighted Version ◽  
Minimum Cardinality ◽  
Vertex Covering ◽  
Activation Rule ◽  
Competitive Activation

We give a generalization of a neural network model originally developed to solve the minimum cardinality vertex covering problem, in order to solve the weighted version of the problem. The model is governed by a modified activation rule and we show that it has some important properties, namely convergence and irredundant covers at stable states. We present experimental results that confirm the effectiveness of the model.

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