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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2
Unfaithful Population Decoding
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
ASCII Text
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Si Wu, Danmei Chen, Shun-ichi Amari, "Unfaithful Population Decoding," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 2, pp. 2199, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2, 2000.
 
 
BibTex
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@article{ 10.1109/IJCNN.2000.857897,
author = {Si Wu and Danmei Chen and Shun-ichi Amari},
title = {Unfaithful Population Decoding},
journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
volume = {2},
year = {2000},
issn = {1098-7576},
pages = {2199},
doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.857897},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
TI - Unfaithful Population Decoding
SN - 1098-7576
SP
EP
A1 - Si Wu,
A1 - Danmei Chen,
A1 - Shun-ichi Amari,
PY - 2000
VL - 2
JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
ER -
 
Si Wu, RIKEN Brain Science Institute
Danmei Chen, RIKEN Brain Science Institute
Shun-ichi Amari, RIKEN Brain Science Institute
Unfaithful population decoding is a paradigm of the maximum likelihood inference based on a model, which is not feasible to describe the encoding process (UMLI) (Wu et al., 1999). The present paper studies the performance of UMLI, through investigating an unfaithful decoding model, which neglects the multiplicative correlation between neural activities. It shows that UMLI is a good compromise between computational complexity and decoding accuracy.
Citation:
Si Wu, Danmei Chen, Shun-ichi Amari, "Unfaithful Population Decoding," ijcnn, vol. 2, pp.2199, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2, 2000
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