This is the GENESIS 2.3 implementation of a multi-compartmental deep cerebellar nucleus (DCN) neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.
Model Type: Neuron or other electrically excitable cell
Region(s) or Organism(s): Cerebellum
Cell Type(s): Cerebellum deep nucleus neuron
Simulation Environment: GENESIS
Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D. (2011). Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells. Journal of computational neuroscience. 30 [PubMed]