Effect of spontaneous activity on stimulus detection in a simple neuronal model

Marie  Levakova

doi:10.3934/mbe.2016007

Article Contents

2016, Volume 13, Issue 3: 551-568. Doi: 10.3934/mbe.2016007

This issue Previous Article Fluctuation scaling in neural spike trains Next Article A model based rule for selecting spiking thresholds in neuron models

Effect of spontaneous activity on stimulus detection in a simple neuronal model

Marie Levakova^1,

1.
Department of Mathematics and Statistics, Faculty of Science, Masaryk University, Kotlarska 2a, 611 37 Brno

Received: February 28, 2015

Revised: September 30, 2015

Published: December 2015

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Abstract

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.

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Mathematics Subject Classification: Primary: 62F10, 62P10; Secondary: 60K05.

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