Model Type: Neuron or other electrically excitable cell
Region(s) or Organism(s): Cerebellum
Cell Type(s): Cerebellum deep nucleus neuron
Currents: I h; I T low threshold; I L high threshold; I Na,p; I Na,t; I K,Ca; I K
Model Concept(s): Synaptic Integration
Simulation Environment: GENESIS
Implementer(s): Jaeger, Dieter [djaeger at emory.edu]
References:
Jaeger D et al. (2017). Robust Transmission of Rate Coding in the Inhibitory Purkinje Cell to Cerebellar Nuclei Pathway in Awake Mice PLOS Computational Biology.
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]
Steuber V, Jaeger D. (2013). Modeling the generation of output by the cerebellar nuclei. Neural networks : the official journal of the International Neural Network Society. 47 [PubMed]
De Schutter E, Jaeger D, Steuber V. (2004). Passive models of neurons in the deep cerebellar nuclei: the effect of reconstruction errors Neurocomputing. 58-60
Luthman J et al. (2011). STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron. Cerebellum (London, England). 10 [PubMed]