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Volumn 71, Issue 3, 2005, Pages

Noise-enhanced categorization in a recurrently reconnected neural network

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATIVE MEMORY NETWORK; NOISE-ENHANCED CATEGORIZATION; RECONNECTED NETWORK (RNN); STOCHASTIC RESONANCE;

EID: 41349111222     PISSN: 15393755     EISSN: 15502376     Source Type: Journal    
DOI: 10.1103/PhysRevE.71.036134     Document Type: Article
Times cited : (16)

References (36)
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    • H=1/[1 +exp(-x)] are given at http://www.mpipks-dresden.mpg.de/~chris. In this case, the period-two limit cycle of the RNN (Sec. III A.) is replaced by a fixed point x -0.
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    • 2 where 〉 ···〉 designates averaging over the state (note that F=2Q-C) [13].
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    • t at the previous time step was uniform. This is a property of the evolved weights.
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    • 6000) taken from the GNU scientific library with seed randomized by the CPU clock. Results analogous to those reported for UWN noise were also obtained for Gaussian-distributed noise.
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    • The behavior described in the main text was obtained in 93% of our runs for weak noise [α<0.16 (Δα=0.01)], 81% of runs for moderate noise [0.16≤α<0.29 (Δα=0.01)], and 87% of runs for strong noise [0.29 ≤α≤1.0 (Δα=0.05)]. Ten runs were performed at each noise strength.
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    • k and the corresponding eigenvectors (in a Mathematica 5.0 notepad) can be viewed at http://www.mpipks-dresden.mpg.de~chris.
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    • 25 have settled to fixed points other than the states listed above.
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    • note
    • This will also be the case when the sensory stimulus is extended in time and the sensory neurons rapidly adapt.
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    • note
    • While we evaluated the success rate of recovery of weak signals in Fig. 4(b) after 1000 iterations, in most cases fewer than 20 iterations are required to converge to the correct memory state.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.