Passive models of neurons in the deep cerebellar nuclei: the effect of reconstruction errors

The goal ofthis study was to determine how the .t ofpassive parameters in a compartmental model varies depending on the precise morphological reconstruction ofthe neuron. We performed whole-cell recordings of deep cerebellar nucleus neurons in brain slices, reconstructed the neuronal morphologies and converted them into detailed compartmental models. A genetic algorithm was used to .nd the best .t ofspeci.c capacitance CM, membrane resistance RM and axial resistivity RA ofthe model with recordings f rom the same cell. We then introduced morphological alterations that represented the likely consequence ofshrinkage artef acts and reconstruction errors. We found that the optimal .ts of passive parameters change as much as 173% with such morphological alterations. In addition, dendrites cut during slicing could a$ect the value of RM, but not CM or RA. c