A detailed Purkinje cell model (Masoli et al 2015)

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Accession:229585
The Purkinje cell is one of the most complex type of neuron in the central nervous system and is well known for its massive dendritic tree. The initiation of the action potential was theorized to be due to the high calcium channels presence in the dendritic tree but, in the last years, this idea was revised. In fact, the Axon Initial Segment, the first section of the axon was seen to be critical for the spontaneous generation of action potentials. The model reproduces the behaviours linked to the presence of this fundamental sections and the interplay with the other parts of the neuron.
Reference:
1 . Masoli S, Solinas S, D'Angelo E (2015) Action potential processing in a detailed Purkinje cell model reveals a critical role for axonal compartmentalization. Front Cell Neurosci 9:47 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Axon;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell;
Channel(s): I Sodium; I Calcium; I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s): Cav2.1 CACNA1A; Cav3.1 CACNA1G; Cav3.2 CACNA1H; Cav3.3 CACNA1I; Nav1.6 SCN8A; Kv1.1 KCNA1; Kv1.5 KCNA5; Kv3.3 KCNC3; Kv3.4 KCNC4; Kv4.3 KCND3; KCa1.1 KCNMA1; KCa2.2 KCNN2; KCa3.1 KCNN4; Kir2.1 KCNJ2; HCN1;
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Bursting; Detailed Neuronal Models; Action Potentials; Action Potential Initiation; Axonal Action Potentials;
Implementer(s): Masoli, Stefano [stefano.masoli at unipv.it]; Solinas, Sergio [solinas at unipv.it];
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; I Na,t; I K; I Sodium; I Calcium;
TITLE Cerebellum Granule Cell Model

COMMENT
	Reference: Theta-Frequency Bursting and Resonance in Cerebellar Granule Cells:Experimental
	Evidence and Modeling of a Slow K+-Dependent Mechanism
	Egidio D'Angelo,Thierry Nieus,Arianna Maffei,Simona Armano,Paola Rossi,Vanni Taglietti,
	Andrea Fontana and Giovanni Naldi

Suffix from Ubc_Kir to Kir2_3
ENDCOMMENT
 
NEURON { 
	SUFFIX Kir2_3
	USEION k READ ek WRITE ik 
	RANGE gkbar, ik, g, alpha_d, beta_d, ek
	RANGE Aalpha_d, Kalpha_d, V0alpha_d
	RANGE Abeta_d, Kbeta_d, V0beta_d
	RANGE d_inf, tau_d 
} 
 
UNITS { 
	(mA) = (milliamp) 
	(mV) = (millivolt) 
} 
 
PARAMETER { 
	Aalpha_d = 0.13289 (/ms)

	
	Kalpha_d = -24.3902 (mV)

	V0alpha_d = -83.94 (mV)
	Abeta_d = 0.16994 (/ms)

	
	Kbeta_d = 35.714 (mV)

	V0beta_d = -83.94 (mV)
	v (mV) 
	gkbar = 0.0009 (mho/cm2) 
	ek (mV) 
	celsius = 30 (degC) 
} 

STATE { 
	d 
} 

ASSIGNED { 
	ik (mA/cm2) 
	d_inf 
	tau_d (ms) 
	g (mho/cm2) 
	alpha_d (/ms) 
	beta_d (/ms) 
} 
 
INITIAL { 
	rate(v) 
	d = d_inf 
} 
 
BREAKPOINT { 
	SOLVE states METHOD derivimplicit
	g = gkbar*d   
	ik = g*(v - ek) 
	alpha_d = alp_d(v) 
	beta_d = bet_d(v) 
} 
 
DERIVATIVE states { 
	rate(v) 
	d' =(d_inf - d)/tau_d 
} 
 
FUNCTION alp_d(v(mV))(/ms) { LOCAL Q10
	Q10 = 3^((celsius-20(degC))/10(degC))
	alp_d = Q10*Aalpha_d*exp((v-V0alpha_d)/Kalpha_d) 
} 
 
FUNCTION bet_d(v(mV))(/ms) { LOCAL Q10
	Q10 = 3^((celsius-20(degC))/10(degC))
	bet_d = Q10*Abeta_d*exp((v-V0beta_d)/Kbeta_d) 
} 
 
PROCEDURE rate(v (mV)) {LOCAL a_d, b_d 
	TABLE d_inf, tau_d  
	DEPEND Aalpha_d, Kalpha_d, V0alpha_d, 
	       Abeta_d, Kbeta_d, V0beta_d, celsius FROM -100 TO 100 WITH 200 
	a_d = alp_d(v)  
	b_d = bet_d(v) 
	tau_d = 1/(a_d + b_d) 
	d_inf = a_d/(a_d + b_d) 
} 


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