The Poisson process with dead time captures important statistical features of neural activity
© Deger et al; licensee BioMed Central Ltd. 2009
Published: 13 July 2009
Stochastic point processes are widely used in computational neuroscience to model the spiking of single neurons and neuronal populations. The choice of a particular point process is critical for statistical measures of neural activity and has impact on the subthreshold dynamics of neuron models.
For the single Poisson process with dead time and superpositions alike, we can determine the variance of shot noise driven by them with the associated spike count in a certain time window or the free membrane potential of an IF neuron. This enables us to show how empirical approximations of the Fano factor depend on the width of the counting window, and how the statistical properties of the driving point-process influence the variance of the subthreshold dynamics of neurons.
Partially funded by DIP F1.2 and BMBF Grant 01GQ0420 to the Bernstein Center for Computational Neuroscience Freiburg.
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