Title :
Unfaithful population decoding
Author :
Wu, Si ; Chen, Danmei ; Amari, Shun-Ichi
Author_Institution :
RIKEN, Saitama, Japan
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;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.857897
