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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, roč.~13, č.~2, s.~551-568. ISSN~1547-1063. Dostupné z: https://dx.doi.org/10.3934/mbe.2016007.
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