Unfaithful population decoding

Si Wu; Danmei Chen; Shun ichi Amari

Unfaithful population decoding

Si Wu
, Danmei Chen
, Shun ichi Amari

RIKEN

Research output: Contribution to conference › Paper › peer-review

3 Scopus citations

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). 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.

Original language	English
Pages	199-204
Number of pages	6
State	Published - 2000
Externally published	Yes
Event	International Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy Duration: 24 Jul 2000 → 27 Jul 2000

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'2000)
City	Como, Italy
Period	24/07/00 → 27/07/00

Cite this

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title = "Unfaithful population decoding",

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). 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.",

author = "Si Wu and Danmei Chen and Amari, \{Shun ichi\}",

year = "2000",

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AU - Wu, Si

AU - Chen, Danmei

AU - Amari, Shun ichi

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AB - 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). 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.

UR - https://www.scopus.com/pages/publications/0033703462

M3 - 論文

AN - SCOPUS:0033703462

SP - 199

EP - 204

T2 - International Joint Conference on Neural Networks (IJCNN'2000)

Y2 - 24 July 2000 through 27 July 2000

ER -

Unfaithful population decoding

Abstract

Conference

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