Grossberg S. 1976. Adaptive pattern classification and universal recoding. I. Parallel development and coding of neural feature detectors. Biol Cybern 23:121--134. Home/Search Document Not in Database Summary Related Articles Check |
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Sequential Information Processing using Time-Delay Connections.. - Tijsseling (Correct)
....(1) Learning in a CALM network occurs by modifying the weights between modules. Connections between two modules are full, which means that every R node in one module is connected to every R node in the other. Weight updates proceed iteratively according to variation of Grossberg s learning rule [56]: w ij (t 1) t a i [K max w ij (t) a j L w ij (t) K min w if (t)a f (2) in which a f , a i , and a j stand for a f (t) a i (t) a j (t) respectively; w ij (t) is the interweight between R nodes j and i (from j to i, both in different modules) w if (t) is the interweight from a neighboring ....
....of the E node, which is rarely suitable, since at the start of the competition all R nodes are active, but learning should not yet occur; and at the end of competition learning should only consolidate existing representations. To prevent this fast increase to maximum weight value, Tijsseling [56] introduced a Gaussian function with the idea is that weight modification is low when arousal is either at a minimum or at a maximum and high in between. In other words, only learn when learning is necessary: t d w E 1.0 a E 0.5 0.25 (4) Because an interweight should be confined to the ....
S. Grossberg, Adaptive pattern classification and universal recoding, II: Feedback, expectation, olfaction, and illusions, Biological Cybernetics, 23, pp. 187-202, 1976.
PageRank: Quantitative Model of Interaction Information Retrieval - Dominich (Correct)
....principle, that the amount of activity of any artificial neuron depends on its weighted input, on the activity levels of artificial neurons connecting to it, and on inhibitory mechanisms. This idea gave birth to a huge literature and many applications, especially due to results obtained in, e.g. [14, 18, 22]. The qualitative model of I R is based on the above principle which it applies to IR. Inhibitory mechanisms are not assumed, and the principle of I R can be formulated as follows: the activity level of an object is determined by the activity levels of objects which are linked to it. 2 3.2.1.2. ....
Grossberg, S. (1976). Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors. Biological Cybertnetics, 23, pp: 121-134
Biological Model Of Vision For An Artificial System That.. - Michael Blackburn And (1989) (Correct)
....intemeurons [22] While the strength of the conical input may be less than that from the retina, through this pathway the cortex can selectively reinforce the pattem of input it receives and suppress other components. A similar idea has been used in the adaptive resonance model of Grossberg [26] and in the alopex model of Harth and Urmikrishnan [27] Primary Visual Cortex Figure 5 represents our preliminary model of the primary visual cortex. Input to the model cortex is presently limited to the thalamic projections, only because we do not yet have Parvoceltular Magnocellular PGN LGN ....
S. Grossberg, "Adaptive pattern classification and universal recoding: II. feedback, expectation, oilaction, illusions," Biological Cybernetics. vol. 23, pp. 187-202, 1976.
Control of the Penicillin Production Using Fuzzy.. - Sánchez, Bravo.. (Correct)
....out on Antibi6ticos pilot plant, to obtain an IMC controller for biomass, and several soft sensors for important variables in the process. Finally, section 4 presents the conclusions. 2. REVIEW OF FasArt FasArt architecture FasArt [4] is a hybrid system based on Adaptive Resonance Theory (ART) [15] family of neural networks, which also combines the advantages of fuzzy sets theory [24] Its architecture is similar to that of Fuzzy ARTMAP [10] as shown in figure 2. Two unsupervised modules (ART and ARTB) cluster input and output respectively, governed by several sintony parameters: ....
S. Grossberg, "Adaptive pattern classification and universal recoding. II: Feedback expectation, olfaction and illusions". Biological Cybernetics, no. 23, pp. 187-202, 1976
Comparison of Computational Models of Familiarity - Discrimination In The (Correct)
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Grossberg S. 1976. Adaptive pattern classification and universal recoding. I. Parallel development and coding of neural feature detectors. Biol Cybern 23:121--134.
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Grossberg, S. (1976) Adaptive Pattern Classification and Universal Recoding: Parallel Development and Coding of Neural Feature Detectors. Biological Cybernetics, 23, 121 -- 134.
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S. Grossberg, "Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors," Biological Cybernetics, vol. 23, pp. 121--134, 1976, reprinted in Anderson and Rosenfeld, 1988.
Constructive Feedforward ART Clustering Networks - Part I - Baraldi, Alpaydin (2002) (8 citations) (Correct)
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S. Grossberg, "Adaptive pattern classification and universal recoding, I: Parallel development and coding of neural feature detectors," Biol. Cybern., vol. 23, pp. 121--134, 1976.
Recognizing and Predicting Context - Learning From User (2004) (Correct)
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S. Grossberg. Adaptive pattern classification and universal recoding: Parallel development and coding of neural feature detectors. Biological Cybernetics, 23:121--134, 1976.
Recognizing and Predicting Context by Learning from User.. - Mayrhofer, Radi, Ferscha (2003) (Correct)
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S. Grossberg. Adaptive pattern classification and universal recoding: Parallel development and coding of neural feature detectors. Biological Cybernetics, 23:121--134, 1976.
An Associative Memory for Autonomous Agents - Bisler (2004) (Correct)
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S. Grossberg, "Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors," Biological Cybernetics, vol. 23, pp. 121--134, 1976.
Evaluating Quality of Text Clustering with ART1 - Massey Royal Military (Correct)
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S.Grossberg. "Adaptive pattern classification and universal recording : I. Parallel development and coding of neural feature detectors", Biological Cybernetics, Vol 23, 1976, pp 121-134.
Neural Comput Applic (1999)8:163--176 - Springer-Verlag London Limited (Correct)
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Grossberg S. Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural detectors. Biological Cybernetics 1976; 23: 121--134
Determination of Clustering Tendency With ART Neural - Networks Louis Massey (Correct)
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Grossberg, S. (1976). "Adaptive pattern classification and universal recording : I. Parallel development and coding of neural feature detectors", Biological Cybernetics, Vol 23, pp 121-134.
Sequential Information Processing using Time-Delay Connections.. - Tijsseling (Correct)
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S. Grossberg, Adaptive pattern classification and universal recoding, II: Feedback, expectation, olfaction, and illusions, Biological Cybernetics, 23, pp. 187-202, 1976.
Self-Organization in Artificial Intelligence and the Brain - Zsolt (Correct)
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Grossberg, S. 1976. "Adaptive pattern classification and universal recording, 2: Feedback, expectation, elfaction and ellusions". Biological Cybernetics, 23:187--202.
Self-Organization in Artificial Intelligence and the Brain - Zsolt (Correct)
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Grossberg, S. 1976. "Adaptive pattern classification and universal recording, 1: Parallel development and coding of neural feature detectors". Biological Cybernetics, 23:121--134.
A Pattern Reordering Approach Based on - Ambiguity Detection For (2003) (Correct)
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S. Grossberg, "Adaptive Pattern Classification and Universal Recoding: II. Feedback, Oscillation, Olfaction, and Illusions," Biological Cybernetics, vol. 23, pp. 187-207, 1976.
A Pattern Reordering Approach Based on - Ambiguity Detection For (2003) (Correct)
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S. Grossberg, "Adaptive Pattern Classification and Universal Recoding: I. Parallel Development and Coding of Neural Detectors," Biological Cybernetics, vol. 23, pp. 121-134, 1976.
A Neural Network Model with Dopamine-Like Reinforcement Signal .. - Suri, Schultz (1999) (3 citations) (Correct)
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Grossberg S. (1976) Adaptive pattern classification and universal recording: I. Parallel development and coding of neural feature detectors. Biol. Cybern. 23, 121--134.
A Dynamic Adaptive Self-Organising Hybrid Model for Text.. - Hung, Wermter (2003) (1 citation) (Correct)
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S. Grossberg, "Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors", Biological Cybernetics, vol. 23, 1976, pp. 121-131.
A Self-Organising Hybrid Model for Dynamic Text Clustering - Hung, Wermter (2003) (Correct)
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Grossberg, S. Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors. Biological Cybernetics, 1976, 23:121-131
Control and Learning of Ambience By an Intelligent - Rutishauser, Joller, Douglas (2004) (Correct)
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S. Grossberg, "Adaptive pattern classification and universal recoding: I. parallel development and coding of neural feature detectors," Biological Cybernetics, vol. 23, pp. 121--134, 1976.
Frequency Sensitive Competitive Learning for Clustering on.. - Banerjee, Ghosh (2002) (Correct)
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S. Grossberg. Adaptive pattern classification and universal recoding: 1. Parallel development and coding of neural feature detectors. Biological Cybernetics, 23:121--134, 1976.
Applied Intelligence 17, 7-10,2002 - Guest Editorial For (2002) (Correct)
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Grossberg, S. "Adaptive pattern classification and universal recoding. I. Parallel development and coding of neural feature detectors." Biological Cybernetics , vol. 23, pp. 121-134, 1976.
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