Extracting information measures from limited experimental samples, such as those normally available when using data recorded in vivo from mammalian cortical neurons, is known to be plagued by a systematic error, which tends to bias the estimate upward. We calculate here the average of the bias, under certain conditions, as an asymptotic expansion in the inverse of the size of the data sample. The result agrees with numerical simulations, and is applicable, as an additive correction term, to measurements obtained under such conditions. Moreover, we discuss the implications for measurements obtained through other usual procedures.

The Upward Bias in Measures of Information Derived from Limited Data Samples / Treves, A.; Panzeri, S.. - In: NEURAL COMPUTATION. - ISSN 0899-7667. - 7:2(1995), pp. 399-407. [10.1162/neco.1995.7.2.399]

The Upward Bias in Measures of Information Derived from Limited Data Samples

Treves, A.;Panzeri, S.
1995

Abstract

Extracting information measures from limited experimental samples, such as those normally available when using data recorded in vivo from mammalian cortical neurons, is known to be plagued by a systematic error, which tends to bias the estimate upward. We calculate here the average of the bias, under certain conditions, as an asymptotic expansion in the inverse of the size of the data sample. The result agrees with numerical simulations, and is applicable, as an additive correction term, to measurements obtained under such conditions. Moreover, we discuss the implications for measurements obtained through other usual procedures.
7
2
399
407
https://www.mitpressjournals.org/doi/10.1162/neco.1995.7.2.399
Treves, A.; Panzeri, S.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11767/85676
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