Active dendrites and spike propagation in a hippocampal interneuron (Saraga et al 2003)

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We create multi-compartment models of an Oriens-Lacunosum/Moleculare (O-LM) hippocampal interneuron using passive properties, channel kinetics, densities and distributions specific to this cell type, and explore its signaling characteristics. We find that spike initiation depends on both location and amount of input, as well as the intrinsic properties of the interneuron. Distal synaptic input always produces strong back-propagating spikes whereas proximal input could produce both forward and back-propagating spikes depending on the input strength. Please see paper for more details.
1 . Saraga F, Wu CP, Zhang L, Skinner FK (2003) Active dendrites and spike propagation in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons. J Physiol 552:673-89 [PubMed]
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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:
Cell Type(s): Hippocampus CA1 interneuron oriens alveus GABA cell;
Channel(s): I Na,t; I A; I K; I h;
Gap Junctions:
Receptor(s): AMPA;
Simulation Environment: NEURON;
Model Concept(s): Action Potential Initiation; Dendritic Action Potentials; Active Dendrites; Influence of Dendritic Geometry; Detailed Neuronal Models; Action Potentials;
Implementer(s): Saraga, Fernanda [Fernanda.Saraga at];
Search NeuronDB for information about:  Hippocampus CA1 interneuron oriens alveus GABA cell; AMPA; I Na,t; I A; I K; I h;
: pregen.mod,v 1.4 1999/01/22 18:47:54 hines Exp
: comments at end

  POINT_PROCESS SpikeGenerator
  RANGE x, spk, lastspk
  RANGE fast_invl, slow_invl, burst_len, start, end
  RANGE noise
  GLOBAL dummy : prevent vectorization for use with CVODE

	fast_invl	= 1 (ms)	: time between spikes in a burst (msec)
	slow_invl	= 200 (ms)	: burst period (msec)
: actually, above is interburst period in conformity with original version
: see
	burst_len	= 1		: burst length (# spikes)
	start		= 100 (ms)	: start of first interburst interval
	end		= 1e10 (ms)	: time to stop bursting
	noise		= 0		: amount of randomeaness (0.0 - 1.0)

	event (ms)
	burst_off (ms)
	burst_on (ms)
	toff (ms)

PROCEDURE seed(x) {

	toff = 1e9
	x = -90
	burst = 0
	event = start - slow_invl
	while (event < 0) {

	SOLVE generate METHOD cvode_t

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
		interval = (1. - noise)*mean + 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.
		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) {
		event = -1e5

PROCEDURE generate() {
	if (at_time(event)) {
		x = 20
		toff = event + .1
	if (at_time(toff)) {
		x = -90

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.

   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