ModelDB is moving. Check out our new site at https://modeldb.science. The corresponding page is https://modeldb.science/266864.

Multiplexed coding in Purkinje neuron dendrites (Zang and De Schutter 2021)

 Download zip file 
Help downloading and running models
Accession:266864
Neuronal firing patterns are crucial to underpin circuit level behaviors. In cerebellar Purkinje cells (PCs), both spike rates and pauses are used for behavioral coding, but the cellular mechanisms causing code transitions remain unknown. We use a well-validated PC model to explore the coding strategy that individual PCs use to process parallel fiber (PF) inputs. We find increasing input intensity shifts PCs from linear rate-coders to burst-pause timing-coders by triggering localized dendritic spikes. We validate dendritic spike properties with experimental data, elucidate spiking mechanisms, and predict spiking thresholds with and without inhibition. Both linear and burst-pause computations use individual branches as computational units, which challenges the traditional view of PCs as linear point neurons. Dendritic spike thresholds can be regulated by voltage state, compartmentalized channel modulation, between-branch interaction and synaptic inhibition to expand the dynamic range of linear computation or burst-pause computation. In addition, co-activated PF inputs between branches can modify somatic maximum spike rates and pause durations to make them carry analogue signals. Our results provide new insights into the strategies used by individual neurons to expand their capacity of information processing.
Reference:
1 . Zang Y, De Schutter E (2021) The Cellular Electrophysiological Properties Underlying Multiplexed Coding in Purkinje Cells. J Neurosci [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Dendrite; Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Cerebellum;
Cell Type(s): Cerebellum Purkinje GABA cell;
Channel(s): I T low threshold; I Na,p; I h; I Potassium; I Sodium; I p,q; I K,Ca;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Dendritic Action Potentials; Detailed Neuronal Models; Synaptic Integration; Temporal Coding; Reaction-diffusion;
Implementer(s): Zang, Yunliang ;
Search NeuronDB for information about:  Cerebellum Purkinje GABA cell; I Na,p; I T low threshold; I p,q; I h; I K,Ca; I Sodium; I Potassium;
/
purkinje_pf_source_code
mod
BK_Slow.mod *
CaP.mod *
capmax.mod *
CaT.mod *
cdp_AIS.mod *
cdp_smooth.mod *
cdp_soma.mod *
cdp_spiny.mod *
distr.mod
ih.mod *
Kv1.mod *
kv3.mod *
kv4f.mod *
kv4s.mod *
mslo.mod *
nap.mod *
narsg.mod *
peak.mod *
pkjlk.mod *
SK2.mod *
syn2.mod *
                            
TITLE Voltage-gated potassium channel from Kv3 subunits
COMMENT
Voltage-gated potassium channel with high threshold and fast activation/deactivation kinetics

KINETIC SCHEME: Hodgkin-Huxley (n^4)
n'= alpha * (1-n) - betha * n
g(v) = gbar * n^4 * ( v-ek )

The rate constants of activation (alpha) and deactivation (beta) were approximated by:

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

Parameters can, cvan, ckan, cbn, cvbn, ckbn are given in the CONSTANT block.
Values derive from least-square fits to experimental data of G/Gmax(v) and taun(v) in Martina et al. J Neurophysiol 97:563-571, 2007
Model includes a calculation of Kv gating current

Reference: Akemann et al., Biophys. J. (2009) 96: 3959-3976
Notice that there is another set of data related with Kv3 by McKay and Turner European Journal of Neuroscience, Vol. 20, pp. 729–739, 2004
in that paper, the activation threshold of Kv3 is much lower.
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

ENDCOMMENT


NEURON {
	SUFFIX Kv3
	USEION k READ ek WRITE ik
	RANGE gbar, g, ik,vshift
	GLOBAL ninf, tau
:    THREADSAFE
}

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

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

	ca = 0.22 (1/ms)
	cva = 16 (mV)
	cka = -26.5 (mV)
	cb = 0.22 (1/ms)
	cvb = 16 (mV)
	ckb = 26.5 (mV)
	
	zn = 1.9196 (1)		: valence of n-gate
}

PARAMETER {
	vshift = 0
	gbar = 0.005 (S/cm2)   <0,1e9>
}

ASSIGNED {
	celsius (degC)
	v (mV)
	
	ik (mA/cm2)
 
	ek (mV)
	g (S/cm2)
	qt (1)

	ninf (1)
	tau (ms)
	alpha (1/ms)
	beta (1/ms)
}

STATE { n }

INITIAL {
	qt = q10^((celsius-22 (degC))/10 (degC))
	rateConst(v)
	n = ninf
}

BREAKPOINT {
	SOLVE state METHOD cnexp
      g = gbar * n^4 
	ik = g * (v - ek)

}

DERIVATIVE state {
	rateConst(v)
	n' = alpha * (1-n) - beta * n
}

PROCEDURE rateConst(v (mV)) {
	alpha = qt * alphaFkt(v)
	beta = qt * betaFkt(v)
	ninf = alpha / (alpha + beta) 
	tau = 1 / (alpha + beta)
}

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

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







Loading data, please wait...