DocumentCode
2260581
Title
Unfaithful population decoding
Author
Wu, Si ; Chen, Danmei ; Amari, Shun-Ichi
Author_Institution
RIKEN, Saitama, Japan
Volume
2
fYear
2000
fDate
2000
Firstpage
199
Abstract
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
Keywords
Brain models; Computational complexity; Decoding; Inference mechanisms; Neural nets; computational complexity; decoding accuracy; maximum likelihood inference; neural activities; unfaithful population decoding; Biological information theory; Biological system modeling; Brain modeling; Computational complexity; Computational efficiency; Computational modeling; Encoding; Fluctuations; Maximum likelihood decoding; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.857897
Filename
857897
Link To Document