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 *
                            
: nap.mod is a persistent Na+ current from
: Baker 2005, parameter assignments and formula's from page 854

NEURON {
	SUFFIX nap
    USEION na READ ena WRITE ina
	RANGE gbar,ina
:    THREADSAFE
}

UNITS {
	(S) = (siemens)
	(mV) = (millivolts)
	(mA) = (milliamp)
}
CONSTANT {
q10  =2.7
}
PARAMETER {
	gbar = 2.2630e-04 :3.7(nS)/1635(um^2)
:	ena= 65 (mV)

	A_amp = 17.235 (/ms) : A for alpha m persis
	B_amp = 27.58 (mV)
	C_amp = -11.47 (mV)

	A_bmp = 17.235 (/ms) : A for beta m persis
	B_bmp = 86.2 (mV)
	C_bmp = 19.8 (mV)
}

ASSIGNED {
	v	(mV) : NEURON provides this
	i	(mA/cm2)
	g	(S/cm2)
	tau_m	(ms)
	minf
	hinf
	ena
	ina
	qt
}

STATE { m h }

BREAKPOINT {
	SOLVE states METHOD cnexp
	g = gbar * m^3
	ina = g * (v-ena)
}

INITIAL {
qt = q10^((celsius-22 (degC))/10 (degC))
	: assume that equilibrium has been reached
	m = alpham(v)/(alpham(v)+betam(v))
}

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

FUNCTION alpham(Vm (mV)) (/ms) {
	alpham=A_amp/(1+exp((Vm+B_amp)/C_amp))
}

FUNCTION betam(Vm (mV)) (/ms) {
	betam=A_bmp/(1+exp((Vm+B_bmp)/C_bmp))
}

FUNCTION rates(Vm (mV)) (/ms) {
	tau_m = 1.0 / (alpham(Vm) + betam(Vm))/qt
	minf = 1.0/(1+exp(-(Vm+66)/5))
}













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