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
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 P-type calcium channel

COMMENT

According to Benton&Raman data
lower threshold but relatively large time constant compared with Sungho's model (According to Bruce Bean)
Also the ssa is steep. In this model, it is better not to shift the SSA to the left.
time speeded up by 2 times May 9 2016 (no longer)
ENDCOMMENT

NEURON {
	SUFFIX newCaP
	USEION ca READ cai, cao WRITE ica
	RANGE pcabar, ica,vshift,kt
	GLOBAL minf, taum
	GLOBAL monovalConc, monovalPerm
:	THREADSAFE
}

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

CONSTANT {
	q10 = 3
	F = 9.6485e4 (coulombs)
	R = 8.3145 (joule/kelvin)

:	cv = 19 (mV)
:	ck = 5.5 (mV)
    cv = 30.5 (mV)
    ck = 4.113 (mV)
}

PARAMETER {
	v (mV)
	celsius (degC)

	cai (mM)
	cao (mM)
    vshift =0
	pcabar = 6e-5 (cm/s)
	monovalConc = 140 (mM)
	monovalPerm = 0
	kt=1
}

ASSIGNED {
	qt
	ica (mA/cm2)
      minf 
	taum (ms)
	T (kelvin)
	E (volt)
	zeta
}

STATE { m }

INITIAL {
	qt = q10^((celsius-22 (degC))/10 (degC))
	T = kelvinfkt( celsius )
	rates(v)
	m = minf
}

BREAKPOINT {
	SOLVE states METHOD cnexp
	ica = (1e3) * pcabar * m * ghk(v, cai, cao, 2)
}

DERIVATIVE states {
	rates(v)
	m' = (minf-m)/taum
}

FUNCTION ghk( v (mV), ci (mM), co (mM), z )  (coulombs/cm3) { 
	E = (1e-3) * v
      zeta = (z*F*E)/(R*T)	
	
	: ci = ci + (monovalPerm) * (monovalConc) :Monovalent permeability

	if ( fabs(1-exp(-zeta)) < 1e-6 ) {
	ghk = (1e-6) * (z*F) * (ci - co*exp(-zeta)) * (1 + zeta/2)
	} else {
	ghk = (1e-6) * (z*zeta*F) * (ci - co*exp(-zeta)) / (1-exp(-zeta))
	}
}

PROCEDURE rates( v (mV) ) {
	minf = 1 / ( 1 + exp(-(v+cv+vshift)/ck) )
	taum = (1e3) * taumfkt(v)/qt/kt
}

FUNCTION taumfkt( v (mV) ) (s) {
	UNITSOFF

    taumfkt = (0.0002 + 0.0007031 * exp(-((v+30+vshift)/14)^2))				:Raman data
:     taumfkt = (0.00002 + 0.00065 * exp(-((v+vshift)/40)^2))								:data from Biophysical Journal 108,2015: 578-584 David Naranjo
	UNITSON
}

FUNCTION kelvinfkt( t (degC) )  (kelvin) {
	UNITSOFF
	kelvinfkt = 273.19 + t
	UNITSON
}

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