CA1 pyramidal neuron: synaptic plasticity during theta cycles (Saudargiene et al. 2015)

 Download zip file 
Help downloading and running models
Accession:157157
This NEURON code implements a microcircuit of CA1 pyramidal neuron and consists of a detailed model of CA1 pyramidal cell and four types of inhibitory interneurons (basket, bistratified, axoaxonic and oriens lacunosum-moleculare cells). Synaptic plasticity during theta cycles at a synapse in a single spine on the stratum radiatum dendrite of the CA1 pyramidal cell is modeled using a phenomenological model of synaptic plasticity (Graupner and Brunel, PNAS 109(20):3991-3996, 2012). The code is adapted from the Poirazi CA1 pyramidal cell (ModelDB accession number 20212) and the Cutsuridis microcircuit model (ModelDB accession number 123815)
Reference:
1 . Saudargiene A, Cobb S, Graham BP (2015) A computational study on plasticity during theta cycles at Schaffer collateral synapses on CA1 pyramidal cells in the hippocampus. Hippocampus 25:208-18 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse; Dendrite;
Brain Region(s)/Organism:
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA1 basket cell; Hippocampus CA1 bistratified cell; Hippocampus CA1 axo-axonic cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Long-term Synaptic Plasticity; STDP;
Implementer(s): Saudargiene, Ausra [ausra.saudargiene at gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell;
/
SaudargieneEtAl2015
readme.html
ANsyn.mod *
bgka.mod *
bistableGB_DOWNUP.mod
burststim2.mod *
cad.mod
cadiffus.mod *
cagk.mod *
cal.mod *
calH.mod *
car.mod *
cat.mod *
ccanl.mod *
d3.mod *
gabaa.mod *
gabab.mod *
glutamate.mod *
gskch.mod *
h.mod
hha_old.mod *
hha2.mod *
hNa.mod *
IA.mod
ichan2.mod
Ih.mod *
kadbru.mod
kadist.mod *
kapbru.mod
kaprox.mod *
Kaxon.mod *
kca.mod *
Kdend.mod *
km.mod *
Ksoma.mod *
LcaMig.mod *
my_exp2syn.mod *
Naaxon.mod *
Nadend.mod *
nap.mod
Nasoma.mod *
nca.mod *
nmda.mod *
nmdaca.mod *
regn_stim.mod *
somacar.mod *
STDPE2Syn.mod *
apical-non-trunk-list.hoc
apical-tip-list.hoc
apical-tip-list-addendum.hoc
apical-trunk-list.hoc
axoaxonic_cell17S.hoc
axon-sec-list.hoc
BasalPath.hoc
basal-paths.hoc
basal-tree-list.hoc
basket_cell17S.hoc
bistratified_cell13S.hoc
burst_cell.hoc
current-balance.hoc *
main.hoc
map-segments-to-3d.hoc *
mod_func.c
mosinit.hoc
ObliquePath.hoc *
oblique-paths.hoc
olm_cell2.hoc
pattsN100S20P5_single.dat
PC.ses
peri-trunk-list.hoc
pyramidalNeuron.hoc
randomLocation.hoc
ranstream.hoc
screenshot.png
soma-list.hoc
stim_cell.hoc *
vector-distance.hoc
                            
: $Id: netstim.mod 1887 2007-11-19 12:34:00Z hines $
: comments at end
: Modified from NetStim so that spikes are Gaussian distributed around
: regular spike times (BPG 14-1-09)
: Spikes outside regular interval are moved to just inside the interval
: (this will distort the distribution, so noise level should be chosen
: so that this does not happen very often!!)


NEURON	{ 
  ARTIFICIAL_CELL RegnStim
  RANGE interval, number, start
  RANGE noise
  POINTER donotuse
}

PARAMETER {
	interval	= 10 (ms) <1e-9,1e9>: time between spikes (msec)
	number	= 10 <0,1e9>	: number of spikes (independent of noise)
	start		= 50 (ms)	: start of first spike
	noise		= 0 <0,1>	: amount of randomness (0.0 - 1.0)
}

ASSIGNED {
	event (ms)
	on
	ispike
	tspike	: regular spike time
	donotuse
}

PROCEDURE seed(x) {
	set_seed(x)
}

INITIAL {
	on = 0 : off
	tspike = start
	ispike = 0
	if (noise < 0) {
		noise = 0
	}
	if (noise > 1) {
		noise = 1
	}
	if (start >= 0 && number > 0) {
		on = 1
		: randomize the first spike 
		event = start + noise*interval*erand()
		: but not earlier than 0
		if (event < 0) {
			event = 0
		}
		net_send(event, 3)
	}
}	

PROCEDURE init_sequence(t(ms)) {
	if (number > 0) {
		on = 1
		event = 0
		ispike = 0
	}
}

FUNCTION invl(mean (ms)) (ms) {
	if (mean <= 0.) {
		mean = .01 (ms) : I would worry if it were 0.
	}
	if (noise == 0) {
		invl = mean
	}else{
:		invl = (1. - noise)*mean + noise*mean*erand()
		invl = tspike + mean + noise*mean*erand() - t
		if (invl <= 0) {
			invl = .01 (ms)	: reset to small interval
		}
:		if (t+invl >= tspike+mean) {
:			invl = tspike + mean - t - .01
:		}
	}
	tspike = tspike + mean
}
VERBATIM
#ifndef NRN_VERSION_GTEQ_8_2_0
double nrn_random_pick(void* r);
void* nrn_random_arg(int argpos);
#define RANDCAST
#else
#define RANDCAST (Rand*)
#endif
ENDVERBATIM

FUNCTION erand() {
VERBATIM
	if (_p_donotuse) {
		/*
		:Supports separate independent but reproducible streams for
		: each instance. However, the corresponding hoc Random
		: distribution MUST be set to Random.normal(0, 1) (BPG)
		*/
		_lerand = nrn_random_pick(RANDCAST _p_donotuse);
	}else{
ENDVERBATIM
		: the old standby. Cannot use if reproducible parallel sim
		: independent of nhost or which host this instance is on
		: is desired, since each instance on this cpu draws from
		: the same stream
		erand = normrand(0, 1)
VERBATIM
	}
ENDVERBATIM
}

PROCEDURE noiseFromRandom() {
VERBATIM
 {
	void** pv = (void**)(&_p_donotuse);
	if (ifarg(1)) {
		*pv = nrn_random_arg(1);
	}else{
		*pv = (void*)0;
	}
 }
ENDVERBATIM
}

PROCEDURE next_invl() {
	if (number > 0) {
		event = invl(interval)
	}
	if (ispike >= number) {
		on = 0
	}
}

NET_RECEIVE (w) {
	if (flag == 0) { : external event
		if (w > 0 && on == 0) { : turn on spike sequence
			: but not if a netsend is on the queue
			init_sequence(t)
			: randomize the first spike so on average it occurs at
			: noise*interval (most likely interval is always 0)
			next_invl()
			event = event - interval*(1. - noise)
			net_send(event, 1)
		}else if (w < 0) { : turn off spiking definitively
			on = 0
		}
	}
	if (flag == 3) { : from INITIAL
		if (on == 1) { : but ignore if turned off by external event
			init_sequence(t)
			net_send(0, 1)
		}
	}
	if (flag == 1 && on == 1) {
		ispike = ispike + 1
		net_event(t)
		next_invl()
		if (on == 1) {
			net_send(event, 1)
		}
	}
}

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 spike generator.  The trains
of spikes can be either periodic or noisy (Poisson-distributed)

Parameters;
   noise: 	between 0 (no noise-periodic) and 1 (fully noisy)
   interval: 	mean time between spikes (ms)
   number: 	number of spikes (independent of noise)

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

Modified by Michael Hines for use with CVode
The intrinsic bursting parameters have been removed since
generators can stimulate other generators to create complicated bursting
patterns with independent statistics (see below)

Modified by Michael Hines to use logical event style with NET_RECEIVE
This stimulator can also be triggered by an input event.
If the stimulator is in the on==0 state (no net_send events on queue)
 and receives a positive weight
event, then the stimulator changes to the on=1 state and goes through
its entire spike sequence before changing to the on=0 state. During
that time it ignores any positive weight events. If, in an on!=0 state,
the stimulator receives a negative weight event, the stimulator will
change to the on==0 state. In the on==0 state, it will ignore any ariving
net_send events. A change to the on==1 state immediately fires the first spike of
its sequence.

ENDCOMMENT


Loading data, please wait...