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Equilibrating errors: reliable estimation of information transmission rates in biological systems with spectral analysis-based methods

Ignatova, I.; French, A. S.; Immonen, Esa-Ville LU ; Frolov, R. and Weckstrom, M. (2014) In Biological Cybernetics 108. p.305-320
Abstract
Shannon's seminal approach to estimating information capacity is widely used to quantify information processing by biological systems. However, the Shannon information theory, which is based on power spectrum estimation, necessarily contains two sources of error: time delay bias error and random error. These errors are particularly important for systems with relatively large time delay values and for responses of limited duration, as is often the case in experimental work. The window function type and size chosen, as well as the values of inherent delays cause changes in both the delay bias and random errors, with possibly strong effect on the estimates of system properties. Here, we investigated the properties of these errors using... (More)
Shannon's seminal approach to estimating information capacity is widely used to quantify information processing by biological systems. However, the Shannon information theory, which is based on power spectrum estimation, necessarily contains two sources of error: time delay bias error and random error. These errors are particularly important for systems with relatively large time delay values and for responses of limited duration, as is often the case in experimental work. The window function type and size chosen, as well as the values of inherent delays cause changes in both the delay bias and random errors, with possibly strong effect on the estimates of system properties. Here, we investigated the properties of these errors using white-noise simulations and analysis of experimental photoreceptor responses to naturalistic and white-noise light contrasts. Photoreceptors were used from several insect species, each characterized by different visual performance, behavior, and ecology. We show that the effect of random error on the spectral estimates of photoreceptor performance (gain, coherence, signal-to-noise ratio, Shannon information rate) is opposite to that of the time delay bias error: the former overestimates information rate, while the latter underestimates it. We propose a new algorithm for reducing the impact of time delay bias error and random error, based on discovering, and then using that size of window, at which the absolute values of these errors are equal and opposite, thus cancelling each other, allowing minimally biased measurement of neural coding. (Less)
Please use this url to cite or link to this publication:
author
publishing date
type
Contribution to journal
publication status
published
subject
in
Biological Cybernetics
volume
108
pages
305 - 320
publisher
Springer
external identifiers
  • scopus:84901821072
ISSN
1432-0770
DOI
10.1007/s00422-014-0598-2
language
English
LU publication?
no
id
b2776301-41b3-46e1-acae-cc65958293b5 (old id 5431776)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/24692025
date added to LUP
2015-05-25 14:47:22
date last changed
2017-01-01 03:34:00
@article{b2776301-41b3-46e1-acae-cc65958293b5,
  abstract     = {Shannon's seminal approach to estimating information capacity is widely used to quantify information processing by biological systems. However, the Shannon information theory, which is based on power spectrum estimation, necessarily contains two sources of error: time delay bias error and random error. These errors are particularly important for systems with relatively large time delay values and for responses of limited duration, as is often the case in experimental work. The window function type and size chosen, as well as the values of inherent delays cause changes in both the delay bias and random errors, with possibly strong effect on the estimates of system properties. Here, we investigated the properties of these errors using white-noise simulations and analysis of experimental photoreceptor responses to naturalistic and white-noise light contrasts. Photoreceptors were used from several insect species, each characterized by different visual performance, behavior, and ecology. We show that the effect of random error on the spectral estimates of photoreceptor performance (gain, coherence, signal-to-noise ratio, Shannon information rate) is opposite to that of the time delay bias error: the former overestimates information rate, while the latter underestimates it. We propose a new algorithm for reducing the impact of time delay bias error and random error, based on discovering, and then using that size of window, at which the absolute values of these errors are equal and opposite, thus cancelling each other, allowing minimally biased measurement of neural coding.},
  author       = {Ignatova, I. and French, A. S. and Immonen, Esa-Ville and Frolov, R. and Weckstrom, M.},
  issn         = {1432-0770},
  language     = {eng},
  pages        = {305--320},
  publisher    = {Springer},
  series       = {Biological Cybernetics},
  title        = {Equilibrating errors: reliable estimation of information transmission rates in biological systems with spectral analysis-based methods},
  url          = {http://dx.doi.org/10.1007/s00422-014-0598-2},
  volume       = {108},
  year         = {2014},
}