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

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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;
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purkinje_pf_source_code
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cdp_smooth.mod *
cdp_soma.mod *
cdp_spiny.mod *
distr.mod
ih.mod *
Kv1.mod *
kv3.mod *
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TITLE SK2 multi-state model Cerebellum Golgi Cell Model

COMMENT
Now I have speed up the reaction rate by 3 to compensate the diffusion factor incorporated by Sergio.
Author:Sergio Solinas, Lia Forti, Egidio DAngelo
Based on data from: Hirschberg, Maylie, Adelman, Marrion J Gen Physiol 1998
Last revised: May 2007

Published in:
             Sergio M. Solinas, Lia Forti, Elisabetta Cesana, 
             Jonathan Mapelli, Erik De Schutter and Egidio D`Angelo (2008)
             Computational reconstruction of pacemaking and intrinsic 
             electroresponsiveness in cerebellar golgi cells
             Frontiers in Cellular Neuroscience 2:2
ENDCOMMENT

NEURON{
	SUFFIX SK2
	USEION ca READ cai
	USEION k READ ek WRITE ik 
	RANGE gkbar, g, ik, tcorr,scal
:    THREADSAFE
}

UNITS {
	(mA) = (milliamp)
	(mV) = (millivolt)
	(molar) = (1/liter)
	(mM) = (millimolar)
}

PARAMETER {
	celsius  (degC)
	cai (mM)
	gkbar = 0.038 (mho/cm2)
	Q10 = 2.7 (1)
	diff = 1 (1) : diffusion factor
    scal = 1
: rates ca-indipendent
	invc1 = 80e-3  ( /ms)
	invc2 = 80e-3  ( /ms)
	invc3 = 200e-3 ( /ms)

	invo1 = 1      ( /ms)
	invo2 = 100e-3 ( /ms)
	diro1 = 160e-3 ( /ms)
	diro2 = 1.2    ( /ms)

: rates ca-dipendent
	dirc2 = 200 ( /ms-mM )
	dirc3 = 160 ( /ms-mM )
	dirc4 = 80  ( /ms-mM )

}

ASSIGNED{ 
	v	(mV) 
	ek	(mV) 
	g	(mho/cm2) 
	ik	(mA/cm2) 
	invc1_t  ( /ms)
	invc2_t  ( /ms)
	invc3_t  ( /ms)
	invo1_t  ( /ms)
	invo2_t  ( /ms)
	diro1_t  ( /ms)
	diro2_t  ( /ms)
	dirc2_t  ( /ms-mM)
	dirc3_t  ( /ms-mM)
	dirc4_t  ( /ms-mM)
	tcorr	 (1)

	dirc2_t_ca  ( /ms)
	dirc3_t_ca  ( /ms)
	dirc4_t_ca  ( /ms)
} 

STATE {
	c1
	c2
	c3
	c4
	o1
	o2
}

BREAKPOINT{ 
	SOLVE kin METHOD sparse 
	g = gkbar*(o1+o2)	:(mho/cm2)
	ik = g*(v-ek)		:(mA/cm2)
} 

INITIAL{
	rate(celsius)
	SOLVE kin STEADYSTATE sparse
} 

KINETIC kin{ 
	rates(cai/diff) 
	~c1<->c2 (dirc2_t_ca, invc1_t) 
	~c2<->c3 (dirc3_t_ca, invc2_t) 
	~c3<->c4 (dirc4_t_ca, invc3_t) 
	~c3<->o1 (diro1_t, invo1_t) 
	~c4<->o2 (diro2_t, invo2_t) 
	CONSERVE c1+c2+c3+c4+o2+o1=1 
} 

FUNCTION temper (Q10, celsius (degC)) {
	temper = Q10^((celsius -23(degC)) / 10(degC)) 
}

PROCEDURE rates(cai(mM)){
	dirc2_t_ca = dirc2_t*cai
	dirc3_t_ca = dirc3_t*cai
	dirc4_t_ca = dirc4_t*cai 
} 

PROCEDURE rate (celsius(degC)) {
	tcorr = temper (Q10,celsius)*scal
	invc1_t = invc1*tcorr
	invc2_t = invc2*tcorr
	invc3_t = invc3*tcorr
	invo1_t = invo1*tcorr
	invo2_t = invo2*tcorr
	diro1_t = diro1*tcorr
	diro2_t = diro2*tcorr
	dirc2_t = dirc2*tcorr
	dirc3_t = dirc3*tcorr
	dirc4_t = dirc4*tcorr
}

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