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@article{1331586,
author = {Leváková, Marie},
article_location = {SPRINGFIELD},
article_number = {2},
doi = {http://dx.doi.org/10.3934/mbe.2016007},
keywords = {Fisher information; latency coding; spontaneous activity; renewal process; neuroscience},
language = {eng},
issn = {1547-1063},
journal = {Mathematical Biosciences and Engineering},
title = {Effect of spontaneous activity on stimulus detection in a simple neuronal model},
url = {https://aimsciences.org/journals/displayArticlesnew.jsp?paperID=12217},
volume = {13},
year = {2016}
}
TY - JOUR
ID - 1331586
AU - Leváková, Marie
PY - 2016
TI - Effect of spontaneous activity on stimulus detection in a simple neuronal model
JF - Mathematical Biosciences and Engineering
VL - 13
IS - 2
SP - 551-568
EP - 551-568
PB - AMER INST MATHEMATICAL SCIENCES
SN - 15471063
KW - Fisher information
KW - latency coding
KW - spontaneous activity
KW - renewal process
KW - neuroscience
UR - https://aimsciences.org/journals/displayArticlesnew.jsp?paperID=12217
L2 - https://aimsciences.org/journals/displayArticlesnew.jsp?paperID=12217
N2 - It is studied what level of a continuous-valued signal is optimally estimable on the basis of first-spike latency neuronal data. When a spontaneous neuronal activity is present, the first spike after the stimulus onset may be caused either by the stimulus itself, or it may be a result of the prevailing spontaneous activity. Under certain regularity conditions, Fisher information is the inverse of the variance of the best estimator. It can be considered as a function of the signal intensity and then indicates accuracy of the estimation for each signal level. The Fisher information is normalized with respect to the time needed to obtain an observation. The accuracy of signal level estimation is investigated in basic discharge patterns modelled by a Poisson and a renewal process and the impact of the complex interaction between spontaneous activity and a delay of the response is shown.
ER -
LEVÁKOVÁ, Marie. Effect of spontaneous activity on stimulus detection in a simple neuronal model. \textit{Mathematical Biosciences and Engineering}. SPRINGFIELD: AMER INST MATHEMATICAL SCIENCES, 2016, vol.~13, No~2, p.~551-568. ISSN~1547-1063. Available from: https://dx.doi.org/10.3934/mbe.2016007.
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