Purkinje cell: Synaptic activation predicts voltage control of burst-pause (Masoli & D'Angelo 2017)

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"The dendritic processing in cerebellar Purkinje cells (PCs), which integrate synaptic inputs coming from hundreds of thousands granule cells and molecular layer interneurons, is still unclear. Here we have tested a leading hypothesis maintaining that the significant PC output code is represented by burst-pause responses (BPRs), by simulating PC responses in a biophysically detailed model that allowed to systematically explore a broad range of input patterns. BPRs were generated by input bursts and were more prominent in Zebrin positive than Zebrin negative (Z+ and Z-) PCs. Different combinations of parallel fiber and molecular layer interneuron synapses explained type I, II and III responses observed in vivo. BPRs were generated intrinsically by Ca-dependent K channel activation in the somato-dendritic compartment and the pause was reinforced by molecular layer interneuron inhibition. BPRs faithfully reported the duration and intensity of synaptic inputs, such that synaptic conductance tuned the number of spikes and release probability tuned their regularity in the millisecond range. ..."
1 . Masoli S, D'Angelo E (2017) Synaptic Activation of a Detailed Purkinje Cell Model Predicts Voltage-Dependent Control of Burst-Pause Responses in Active Dendrites. Front Cell Neurosci 11:278 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Synapse;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell;
Channel(s): I Potassium; I K,Ca;
Gap Junctions:
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Bursting;
Implementer(s): Masoli, Stefano [stefano.masoli at unipv.it];
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; I K,Ca; I Potassium;
pc_param = dict()

#Conductances for all the channels with the same order as in the template

pc_param['eleak'] = -63
pc_param['LeakSoma'] = 1.1E-3
pc_param['Cav3.1Soma'] = 7e-6
pc_param['Cav2.1Soma'] = 2.2e-4 
pc_param['HCNSoma'] = 0.001
pc_param['Nav1.6Soma'] = 0.214
pc_param['Kv3.4Soma'] = 0.0515
pc_param['Kv1.1Soma'] = 0.002 
pc_param['Cav3.2Soma'] = 0.0008 
pc_param['Kca3.1Soma'] = 0.01 
pc_param['Cav3.3Soma'] = 0.0001 
pc_param['PC_KirSoma'] = 0.00003 
pc_param['Kca1.1Soma'] = 0.01 
pc_param['Kca2.2Soma'] = 1e-3 

pc_param['Cav2.1Dend'] = 1e-3 
pc_param['Kca1.1Dend'] = 3.5e-2
pc_param['Kv4.3Dend'] = 0.001
pc_param['Kv1.1Dend'] = 0.0012 
pc_param['Kv1.5Dend'] = 0.13195e-3
pc_param['Kv3.3Dend'] = 0.01 
pc_param['Cav3.3Dend'] = 0.0001 
pc_param['Cav3.2Dend'] = 0.0012 
pc_param['Kca3.1Dend'] = 0.002
pc_param['Cav3.1Dend'] = 5e-6 
pc_param['Kca2.2Dend'] = 1e-3
pc_param['PC_KirDend'] = 0.00001
pc_param['Nav1.6Dend'] = 0.016
pc_param['HCNDend'] = 0.000004 

pc_param['Cav3.1Ais'] = 8.2e-6
pc_param['Nav1.6AIS'] = 0.8
pc_param['Cav2.1AIS'] = 2.2e-4 
pc_param['Kv3.4AIS'] = 0.01 

pc_param['Kv1.1AisK'] = 0.01 

#First node Of Ranvier
pc_param['Nav1.6Nor'] = 0.03 
pc_param['Kv3.4Nor'] = 0.02  
pc_param['Cav3.1Nor'] = 1e-5 
pc_param['Cav2.1Nor'] = 2.2e-4 

#Second node Of Ranvier
pc_param['Nav1.6Nor2'] = 0.03 
pc_param['Kv3.4Nor2'] = 0.02  
pc_param['Cav3.1Nor2'] = 1e-5 
pc_param['Cav2.1Nor2'] = 2.2e-4  

#Third node Of Ranvier
pc_param['Nav1.6Nor3'] = 0.03
pc_param['Kv3.4Nor3'] = 0.02  
pc_param['Cav3.1Nor3'] = 1e-5 
pc_param['Cav2.1Nor3'] = 2.2e-4 

#Axon collateral
pc_param['Nav1.6Axoncoll'] = 0.03 
pc_param['Kv3.4Axoncoll'] = 0.02
pc_param['Cav3.1Axoncoll'] = 1e-5 
pc_param['Cav2.1Axoncoll'] = 2.2e-4 

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