Cerebellar granule cell (Masoli et al 2020)

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Accession:265584
"The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed that fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage."
Reference:
1 . Masoli S, Tognolina M, Laforenza U, Moccia F, D'Angelo E (2020) Parameter tuning differentiates granule cell subtypes enriching transmission properties at the cerebellum input stage. Commun Biol 3:222 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum interneuron granule GLU cell;
Channel(s): Ca pump; I Na, leak; I Calcium;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; Python;
Model Concept(s): Action Potentials; Calcium dynamics; Synaptic Integration;
Implementer(s): Masoli, Stefano [stefano.masoli at unipv.it];
Search NeuronDB for information about:  Cerebellum interneuron granule GLU cell; AMPA; NMDA; I Calcium; I Na, leak; Ca pump;
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Granule_cell_2020
03_GrC_2020_adapting
mod_files
cdp5_CR.mod *
GRANULE_Ampa_det_vi.mod *
GRANULE_Nmda_det_vi.mod *
GRC_CA.mod *
GRC_KM.mod *
GRC_NA.mod *
GRC_NA_FHF.mod *
Kca11.mod *
Kca22.mod *
Kir23.mod *
Kv11.mod *
Kv15.mod *
Kv22.mod *
Kv34.mod *
Kv43.mod *
Leak.mod *
                            
TITLE Voltage-gated low threshold potassium current from Kv1 subunits

COMMENT

NEURON implementation of a potassium channel from Kv1.1 subunits
Kinetical scheme: Hodgkin-Huxley m^4, no inactivation

Experimental data taken from:
Human Kv1.1 expressed in xenopus oocytes: Zerr et al., J Neurosci 18, 2842, 2848, 1998
Vhalf = -28.8 +- 2.3 mV; k = 8.1+- 0.9 mV

The voltage dependency of the rate constants was approximated by:

alpha = ca * exp(-(v+cva)/cka)
beta = cb * exp(-(v+cvb)/ckb)

Parameters ca, cva, cka, cb, cvb, ckb
were determined from least square-fits to experimental data of G/Gmax(v) and tau(v).
Values are defined in the CONSTANT block.
Model includes calculation of Kv gating current

Reference: Akemann et al., Biophys. J. (2009) 96: 3959-3976

Laboratory for Neuronal Circuit Dynamics
RIKEN Brain Science Institute, Wako City, Japan
http://www.neurodynamics.brain.riken.jp

Date of Implementation: April 2007
Contact: akemann@brain.riken.jp

Suffix from Kv1 to Kv1_1

ENDCOMMENT


NEURON {
    THREADSAFE
	SUFFIX Kv1_1
	USEION k READ ek WRITE ik
	NONSPECIFIC_CURRENT i
	RANGE g, gbar, ik, i , igate, nc
	RANGE ninf, taun
	RANGE gateCurrent, gunit
}

UNITS {
	(mV) = (millivolt)
	(mA) = (milliamp)
	(nA) = (nanoamp)
	(pA) = (picoamp)
	(S)  = (siemens)
	(nS) = (nanosiemens)
	(pS) = (picosiemens)
	(um) = (micron)
	(molar) = (1/liter)
	(mM) = (millimolar)		
}

CONSTANT {
	e0 = 1.60217646e-19 (coulombs)
	q10 = 2.7

	ca = 0.12889 (1/ms)
	cva = 45 (mV)
	cka = -33.90877 (mV)

	cb = 0.12889 (1/ms)
      cvb = 45 (mV)
	ckb = 12.42101 (mV) 

	zn = 2.7978 (1)		: valence of n-gate         
}

PARAMETER {
	gateCurrent = 0 (1)	: gating currents ON = 1 OFF = 0
	
	gbar = 0.004 (S/cm2)   <0,1e9>
	gunit = 16 (pS)		: unitary conductance 
}


ASSIGNED {
 	celsius (degC)
	v (mV)	

	ik (mA/cm2)
	i (mA/cm2)
	igate (mA/cm2) 
	ek (mV)
	g  (S/cm2)
	nc (1/cm2)			: membrane density of channel
	
	ninf (1)
	taun (ms)
	alphan (1/ms)
	betan (1/ms)
	qt (1)
}

STATE { n }

INITIAL {
	nc = (1e12) * gbar / gunit
	qt = q10^((celsius-22 (degC))/10 (degC))
	rates(v)
	n = ninf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
      g = gbar * n^4 
	ik = g * (v - ek)
	igate = nc * (1e6) * e0 * 4 * zn * ngateFlip()

	if (gateCurrent != 0) { 
		i = igate
	}
}

DERIVATIVE states {
	rates(v)
	n' = (ninf-n)/taun 
}

PROCEDURE rates(v (mV)) {
	alphan = alphanfkt(v)
	betan = betanfkt(v)
	ninf = alphan/(alphan+betan) 
	taun = 1/(qt*(alphan + betan))       
}

FUNCTION alphanfkt(v (mV)) (1/ms) {
	alphanfkt = ca * exp(-(v+cva)/cka) 
}

FUNCTION betanfkt(v (mV)) (1/ms) {
	betanfkt = cb * exp(-(v+cvb)/ckb)
}

FUNCTION ngateFlip() (1/ms) {
	ngateFlip = (ninf-n)/taun 
}




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