Multicompartmental cerebellar granule cell model (Diwakar et al. 2009)

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Accession:116835
A detailed multicompartmental model was used to study neuronal electroresponsiveness of cerebellar granule cells in rats. Here we show that, in cerebellar granule cells, Na+ channels are enriched in the axon, especially in the hillock, but almost absent from soma and dendrites. Numerical simulations indicated that granule cells have a compact electrotonic structure allowing EPSPs to diffuse with little attenuation from dendrites to axon. The spike arose almost simultaneously along the whole axonal ascending branch and invaded the hillock, whose activation promoted spike back-propagation with marginal delay (<200 micros) and attenuation (<20 mV) into the somato-dendritic compartment. For details check the cited article.
Reference:
1 . Diwakar S, Magistretti J, Goldfarb M, Naldi G, D'Angelo E (2009) Axonal Na+ channels ensure fast spike activation and back-propagation in cerebellar granule cells J Neurophysiol 101(2):519-32 [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 cell;
Channel(s): I A; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium; I A, slow;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Active Dendrites; Detailed Neuronal Models; Axonal Action Potentials; Action Potentials; Intrinsic plasticity;
Implementer(s): Diwakar, Shyam [shyam at amrita.edu];
Search NeuronDB for information about:  Cerebellum interneuron granule cell; I A; I M; I h; I K,Ca; I Sodium; I Calcium; I Potassium; I A, slow;
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GrC
fig10
readme.html
AmpaCOD.mod *
GRC_CA.mod *
GRC_CALC.mod *
GRC_GABA.mod *
GRC_KA.mod *
GRC_KCA.mod *
GRC_KIR.mod *
GRC_KM.mod *
GRC_KV.mod *
GRC_LKG1.mod *
GRC_LKG2.mod *
GRC_NA.mod *
NmdaS.mod *
Pregen.mod *
ComPanel.hoc
Grc_Cell.hoc
mosinit.hoc
Parametri.hoc
screenshot.jpg
simple.ses
Start.hoc
                            
: $Id: pregen.mod,v 1.3 2000/05/16 11:16:56 hines Exp $
: comments at end

NEURON	{ 
  POINT_PROCESS SpikeGenerator
  RANGE y
  RANGE fast_invl, slow_invl, burst_len, start, end,delay
  RANGE noise
}

PARAMETER {
	fast_invl	= 10 (ms)	: time between spikes in a burst (msec)
	slow_invl	= 0 (ms)	: burst period (msec)
: actually, above is interburst period in conformity with original version
: see
	burst_len	= 10		: burst length (# spikes)
	start		= 50 (ms)	: start of first interburst interval
	end		= 1e10 (ms)	: time to stop bursting
	noise		= 0		: amount of randomeaness (0.0 - 1.0)
	delay		= 4
}

ASSIGNED {
	y
	burst
	event (ms)
	burst_off (ms)
	burst_on (ms)
	toff (ms)
	on
}

PROCEDURE seed(x) {
	set_seed(x)
}

INITIAL {
	on = 1
	toff = 1e9
	y = -90
	burst = 0
	event = start - slow_invl
	:
	event_time()
	while (on == 1 && event < 0) {
		event_time()
	}
	if (on == 1) {
		net_send(event, 1)
	}
}	

FUNCTION interval(mean (ms)) (ms) {
	if (mean <= 0.) {
		mean = .01 (ms) : I would worry if it were 0.
		: since mean is a local variable, if the number it is set
		: to is dimensionless, mean will be dimensionless.
	}
	if (noise == 0) {
		interval = mean
	}else{
		interval = (1. - noise)*mean + noise*(mean*exprand(1)+delay) : (delay+noise*mean*exprand(1))
	}
}

PROCEDURE event_time() {
	if (slow_invl == 0 || (burst != 0. && burst_len > 1)) {
		event = event + interval(fast_invl)
		if (event > burst_on + burst_off) {
			burst = 0.
		}
	}else{
		burst = 1.
: if slow_invl from beginning of burst to beginning of burst
:		event = event + interval(slow_invl - (burst_len-1)*fast_invl)
: use following if slow_invl is interburst interval
		event = event + interval(slow_invl)
		burst_on = event
		burst_off = interval((burst_len - 1)*fast_invl)-1e-6
	}
	if (event > end) {
		on = 0
	}
}

NET_RECEIVE (w) {
:printf("Pregen receive t=%g flag=%g\n", t, flag) 
	if (flag == 1 && on == 1) {
		y = 20
		net_event(t)
		event_time()
		net_send(event - t, 1)
		net_send(.1, 2)
	}
	if (flag == 2) {
		y = -90
	}
}

COMMENT
Presynaptic spike generator
---------------------------

This mechanism has been written to be able to use synapses in a single
neuron receiving various types of presynaptic trains.  This is a "fake"
presynaptic compartment containing a fast spike generator.  The trains
of spikes can be either periodic or noisy (Poisson-distributed), and 
either tonic or bursting.

Parameters;
   noise: 	between 0 (no noise-periodic) and 1 (fully noisy)
   fast_invl: 	fast interval, mean time between spikes (ms)
   slow_invl:	slow interval, mean burst silent period (ms), 0=tonic train
   burst_len: 	mean burst length (nb. spikes)

Written by Z. Mainen, modified by A. Destexhe, The Salk Institute

Modified by Michael Hines for use with CVode

Modified by Michael Hines to use logical event style with NET_RECEIVE
ENDCOMMENT


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