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288 pp. per issue, 6 x 9,
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Founded: 1989
ISSN 0899-7667
E-ISSN 1530-888X
2008 ISI Impact Factor: 2.378

Neural Computation

June 2006, Vol. 18, No. 6, Pages 1259-1267
Posted Online April 28, 2006.
(doi:10.1162/neco.2006.18.6.1259)
© 2006 Massachusetts Institute of Technology
Correlation and Independence in the Neural Code

Shun-ichi Amari

Hiroyuki Nakahara

RIKEN Brain Science Institute, Wako-shi, Saitama, Japan

PDF (68.545 KB) PDF Plus (98.093 KB)

The decoding scheme of a stimulus can be different from the stochastic encoding scheme in the neural population coding. The stochastic fluctuations are not independent in general, but an independent version could be used for the ease of decoding. How much information is lost by using this unfaithful model for decoding? There are discussions concerning loss of information (Nirenberg & Latham, 2003; Schneidman, Bialek, & Berry, 2003). We elucidate the Nirenberg-Latham loss from the point of view of information geometry.

Cited by

A. Scaglione, G. Foffani, G. Scannella, S. Cerutti, K. A. Moxon. (2008) Mutual Information Expansion for Studying the Role of Correlations in Population Codes: How Important Are Autocorrelations?. Neural Computation 20:11, 2662-2695
Online publication date: 1-Nov-2008.
Abstract | PDF (1568 KB) | PDF Plus (258 KB) 
Si Wu, Kosuke Hamaguchi, Shun-ichi Amari. (2008) Dynamics and Computation of Continuous Attractors. Neural Computation 20:4, 994-1025
Online publication date: 1-Apr-2008.
Abstract | PDF (926 KB) | PDF Plus (944 KB) 

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