Amyloid-beta effects on release probability and integration at CA3-CA1 synapses (Romani et al. 2013)

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Accession:147757
The role of amyloid beta (Aß) in brain function and in the pathogenesis of Alzheimer’s disease remains elusive. Recent publications reported that an increase in Aß concentration perturbs presynaptic release in hippocampal neurons, in particular by increasing release probability of CA3-CA1 synapses. The model predics how this alteration can affect synaptic plasticity and signal integration. The results suggest that the perturbation of release probability induced by increased Aß can significantly alter the spike probability of CA1 pyramidal neurons and thus contribute to abnormal hippocampal function during Alzheimer’s disease.
Reference:
1 . Romani A, Marchetti C, Bianchi D, Leinekugel X, Poirazi P, Migliore M, Marie H (2013) Computational modeling of the effects of amyloid-beta on release probability at hippocampal synapses. Front Comput Neurosci 7:1 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,t; I A; I K; I M; I h; I Calcium; I_AHP;
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Synaptic Plasticity; Short-term Synaptic Plasticity; Facilitation; Depression; Synaptic Integration; Aging/Alzheimer`s;
Implementer(s): Bianchi, Daniela [danielabianchi12 -at- gmail.com]; Romani, Armando [romani.armando -at- gmail.com];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; I Na,t; I A; I K; I M; I h; I Calcium; I_AHP; Glutamate;
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RomaniEtAl2013
experiment
cad.mod *
cagk.mod *
cal.mod *
calH.mod *
car.mod *
cat.mod *
d3.mod *
h.mod *
kadist.mod *
kaprox.mod *
kca.mod *
kdr.mod *
km.mod *
na3.mod *
na3dend.mod *
na3notrunk.mod *
nap.mod *
nax.mod *
netstimmm.mod *
somacar.mod *
tmgsyn.mod
vecevent.mod
cell-setup.hoc
createNewSyn4.hoc
loadBasicModel.hoc
mosinit.hoc
session.ses
simulation.hoc
                            


NEURON	{ 
  ARTIFICIAL_CELL NetStimm        : NetStim
  RANGE y
  RANGE interval, number, start
  RANGE noise, burstP

}

PARAMETER {
	interval	= 10 (ms) <1e-9,1e9>: time between spikes (msec)
	number	        = 10 <0,1e9>	: number of spikes
	start		= 50 (ms)	: start of first spike
	noise		= 0 <0,1>	: amount of randomeaness (0.0 - 1.0)
	burstP		= 100 (ms)
	}

ASSIGNED {
	y
	event (ms)
	on
	end (ms)
}

PROCEDURE seed(x) {
	set_seed(x)
}

INITIAL {
	on = 0
	y = 0
	if (noise < 0) {
		noise = 0
	}
	if (noise > 1) {
		noise = 1
	}
	if (start >= 0 && number > 0) {
		: randomize the first spike so on average it occurs at
		: start + noise*interval
		event = start + invl(interval) - interval*(1. - noise)
		: but not earlier than 0
		if (event < 0) {
			event = 0
		}

		
  	 	net_send(event, 3)
		net_send(event + burstP, 3)
		net_send(event + 2*burstP, 3)
		net_send(event + 3*burstP, 3)
		net_send(event + 4*burstP, 3)
		net_send(event + 5*burstP, 3)
		net_send(event + 6*burstP, 3)
		net_send(event + 7*burstP, 3)
		net_send(event + 8*burstP, 3)
		net_send(event + 9*burstP, 3)	
:		net_send(event + 10*burstP, 3)	
		
		
	}
	
}
PROCEDURE init_sequence(t(ms)) { 
	if (number > 0) {
		on = 1
		event = t
		end = t + 1e-6 + invl(interval)*(number-1)
	}
}

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*exprand(1)
	}
}

PROCEDURE event_time() {
	if (number > 0) {
		event = event + invl(interval)
	}
	if (event > end) {
		on = 0
	}
}

NET_RECEIVE (w) {
	if (flag == 0) { : external event
		if (w > 0 && on == 0) { : turn on spike sequence
			init_sequence(t)
			net_send(0, 1)
		}else if (w < 0 && on == 1) { : turn off spiking
			on = 0
		}
	}
	if (flag == 3) { : from INITIAL
		if (on == 0) {
			init_sequence(t)
			net_send(0, 1)
		}
	}
	if (flag == 1 && on == 1) {
		y = 2
		net_event(t)
		event_time()
		if (on == 1) {
			net_send(event - t, 1)
		}
		net_send(.1, 2)
	}
	if (flag == 2) {
		y = 0
	}
}

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: 	mean number of spikes

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 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 the on=1 state,
the stimulator receives a negative weight event, the stimulator will
change to the off state. In the off state, it will ignore negative weight
events. A change to the on state immediately fires the first spike of
its sequence.

ENDCOMMENT


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