Q60249181
Dotaz
Zobrazit nápovědu
Local stereological techniques can be used for particle volume estimation based on information collected on a section plane through a reference point of the particle. We present methods for variability estimation of the local stereological volume estimators. This variability arises during the stereological estimation procedure and in the particle population. Both of these components can be estimated separately from planar sections. Our aim is to give a preliminary analysis of the possibility to include the particle structure interaction into the estimation procedure. For this reason, not only the section profiles, but also their locations, have to be recorded. The methods are applied for the sectional data obtained from neurons in the hippocampal brain region subiculum of four 3-month-old male Wistar rats. The proposed procedure enables one to obtain information about particle volume distribution.
- MeSH
- hipokampus cytologie MeSH
- krysa rodu rattus MeSH
- mikroskopie metody MeSH
- mozek cytologie MeSH
- pilotní projekty MeSH
- počítačové zpracování obrazu metody MeSH
- potkani Wistar MeSH
- velikost buňky MeSH
- zvířata MeSH
- Check Tag
- krysa rodu rattus MeSH
- mužské pohlaví MeSH
- zvířata MeSH
- Publikační typ
- práce podpořená grantem MeSH
We study the estimation of statistical moments of interspike intervals based on observation of spike counts in many independent short time windows. This scenario corresponds to the situation in which a target neuron occurs. It receives information from many neurons and has to respond within a short time interval. The precision of the estimation procedures is examined. As the model for neuronal activity, two examples of stationary point processes are considered: renewal process and doubly stochastic Poisson process. Both moment and maximum likelihood estimators are investigated. Not only the mean but also the coefficient of variation is estimated. In accordance with our expectations, numerical studies confirm that the estimation of mean interspike interval is more reliable than the estimation of coefficient of variation. The error of estimation increases with increasing mean interspike interval, which is equivalent to decreasing the size of window (less events are observed in a window) and with decreasing the number of neurons (lower number of windows).
