Electrotonic transform and EPSCs for WT and Q175+/- spiny projection neurons (Goodliffe et al 2018)

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Accession:236310
This model achieves electrotonic transform and computes mean inward and outward attenuation from 0 to 500 Hz input; and randomly activates synapses along dendrites to simulate AMPAR mediated EPSCs. For electrotonic analysis, in Elec folder, the entry file is MSNelec_transform.hoc. For EPSC simulation, in Syn folder, the entry file is randomepsc.hoc. Run read_EPSCsims_mdb_alone.m next with the simulated parameter values specified to compute the mean EPSC.
Reference:
1 . Goodliffe JW, Song H, Rubakovic A, Chang W, Medalla M, Weaver CM, Luebke JI (2018) Differential changes to D1 and D2 medium spiny neurons in the 12-month-old Q175+/- mouse model of Huntington's Disease. PLoS One 13:e0200626 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism: Striatum;
Cell Type(s): Neostriatum spiny neuron;
Channel(s):
Gap Junctions:
Receptor(s): AMPA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Detailed Neuronal Models; Membrane Properties; Electrotonus; Synaptic-input statistic;
Implementer(s):
Search NeuronDB for information about:  AMPA;
/
GoodliffeEtAl2018
Syn
tau_tables
bkkca.mod
cadyn.mod *
caL.mod
caL13.mod
caldyn.mod
can.mod
caq.mod *
car.mod
cat.mod
kaf.mod
kas.mod
kdr.mod
kir.mod *
krp.mod *
linearIclamp.mod
naf.mod
nap.mod
skkca.mod
stim.mod *
actionPotentialPlayer.hoc *
all_tau_vecs.hoc
analyticFunctions.hoc *
analyze_EPSC.m
aux_procs.hoc
baseline_values.txt
basic_procs.hoc
createFit_WTD1.m
electro_procs.hoc
fixnseg.hoc *
load_scripts.hoc
msp_template.hoc
PFC-V1_AddSynapses.hoc
PFC-V1_AddSynapses_fix.hoc
PFC-V1_AddSynapses_neg.hoc
PFC-V1_AddSynapses_negexp.hoc
plot_seClamp_i.ses
ran_test.hoc
randomepsc.hoc
ranstream.hoc
read_EPSCsims_mdb_alone.m
readcell.hoc
readNRNbin_Vclamp.m
                            
: $Id: netstim.mod,v 1.2 2003/03/31 13:27:47 hines Exp $
: comments at end

NEURON	{ 
  ARTIFICIAL_CELL stim
  RANGE y, frequency, number, start, noise, change, event, flag, hey, on, end
}

PARAMETER {
	frequency	= 1 (1/s) <1e-9,1e9>: mean frequency of spiking = 1000/interval (from netstim.mod)
	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)
	change	(ms)	: the frequency will change at thist time, so recalculate
	hey 
}

PROCEDURE seed(x) {
	set_seed(x)
:	VERBATIM
:		printf("Seed is %g\n", _lx);
:	ENDVERBATIM
}

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*(1/frequency)*(1000)
		event = start + invl((1/frequency)*(1000)) - (1/frequency)*(1000)*(1. - noise)
		: but not earlier than 0
		if (event < 0) {
			event = 0
		}
		if (event > change) { init_sequence(t) net_send(change-t, 4) hey = 4}	: next spike is after frequency change, so it must be recalculated
		else {	net_send(event-t, 3) hey = 3}
	}
}	

PROCEDURE init_sequence(t(ms)) {
	if (number > 0) {
		on = 1
		event = t
		end = t + 1e-6 + invl((1/frequency)*(1000))*(number-1)	: controls how many spikes are generated by defining mean 
	: time to stop spiking - so with noise = 0, it's exact, with noise number becomes a mean number of spikes
	}
}

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((1/frequency)*(1000))
	}
	if (event > end) {
		on = 0			: stop spiking (based on number?)
	}
}

NET_RECEIVE (w) {
	if (flag == 0) { : external event 
		y = 2
		net_event(t)		: sends event at time t to all processes connected to jstim
		net_send(.1, 2)		: spike ends in 0.1 ms
		hey = 2
	}
	if (flag == 3) { : from INITIAL
		if (on == 0) {
			init_sequence(t)
			net_send(0, 1)	: net_send(interval, flag) is self event to arrive at t+interval
			hey = 1
		}
	}
	if (flag == 1 && on == 1) {
		y = 2
		net_event(t)		: sends event at time t to all processes connected to jstim
		event = t
		event_time()

		if (event > change) {
			net_send(change-t, 4)	: at time of frequency change, recalculate next spike w/ new frequency
			hey = 4
		} else { 
			if (on == 1) {
				net_send(event - t, 1)
				hey = 1
			}
		}

		net_send(.1, 2)		: spike ends in 0.1 ms
		hey = 2
	}
	if (flag == 2) {
		y = 0
	}
	if (flag == 4) {
		event = t
		event_time()		: recalculate next event time with new frequency
		if (on == 1) {
			if (event > change) {
				net_send(change - t, 4)	: at time of frequency change, recalculate next spike w/ new frequency
				hey = 4
			} else { 
				net_send(event - t, 1)
				hey = 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: 	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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