Model of CA1 activity during working memory task (Spera et al. 2016)

 Download zip file   Auto-launch 
Help downloading and running models
Accession:223962
"The cellular processes underlying individual differences in the Woring Memory Capacity (WMC) of humans are essentially unknown. Psychological experiments suggest that subjects with lower working memory capacity (LWMC), with respect to subjects with higher capacity (HWMC), take more time to recall items from a list because they search through a larger set of items and are much more susceptible to interference during retrieval. ... In this paper, we investigate the possible underlying mechanisms at the single neuron level by using a computational model of hippocampal CA1 pyramidal neurons, which have been suggested to be deeply involved in the recognition of specific items. ..."
Reference:
1 . Spera E, Migliore M, Unsworth N, Tegolo D (2016) On the cellular mechanisms underlying working memory capacity in humans Neural Network World 4:335-359
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Synapse;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Working memory;
Implementer(s):
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell;
/
SperaEtAl2016
readme.txt
distr.mod *
Gfluct.mod
h.mod *
kadist.mod *
kaprox.mod *
kdrca1.mod *
na3n.mod *
naxn.mod *
netstims.mod *
fixnseg.hoc *
geoc91662.hoc *
mosinit.hoc
obliqui91662.txt
simulation91662.hoc
                            
: $Id: netstim.mod,v 1.1.1.1 2001/01/01 20:30:37 hines Exp $
: modified in such a way that the first event will never be before start
: M.Migliore Dec.2001
: modified in such a way to have the first event at start
: M.Migliore Sep. 2003

NEURON	{ 
  POINT_PROCESS NetStims
  RANGE y
  RANGE interval, number, start
  RANGE noise
}

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)
}

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) {
	: first spike occurs at start
		event = start
		net_send(event, 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


Loading data, please wait...