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Data
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Cerebellar Nucleus Neuron (Steuber, Schultheiss, Silver, De Schutter & Jaeger, 2010)
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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.
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Neuron or other electrically excitable cell Show
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Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D (2011) Show
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Steuber, Volker [v.steuber at herts.ac.uk] Show
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Jaeger, Dieter [djaeger at emory.edu] Show
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v.steuber@herts.ac.uk
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Cerebellar Nucleus Neuron
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ICaT INaP IHCN
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Rebound firing
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Steuber, Volker (v.steuber@herts.ac.uk)
Jaeger, Dieter (djaeger@emory.edu)
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Volker Steuber, Nathan Schultheiss, R Angus Silver, Erik De Schutter and Dieter Jaeger (2010). The control of rebound spiking by voltage-gated currents and synaptic input in a detailed compartmental model of deep cerebellar nucleus neurons. Journal of Computational Neuroscience (epub ahead of print, PMID: 21052805).
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