Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011)

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Accession:139883
The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a "ReConv" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells.
Reference:
1 . Diwakar S, Lombardo P, Solinas S, Naldi G, D'Angelo E (2011) Local field potential modeling predicts dense activation in cerebellar granule cells clusters under LTP and LTD control. PLoS One 6:e21928 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Extracellular;
Brain Region(s)/Organism:
Cell Type(s): Cerebellum interneuron granule cell;
Channel(s): I K; I M; I K,Ca; I Sodium; I Calcium; I Cl, leak;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; MATLAB; Octave;
Model Concept(s): Extracellular Fields; Evoked LFP;
Implementer(s): Diwakar, Shyam [shyam at amrita.edu];
Search NeuronDB for information about:  Cerebellum interneuron granule cell; GabaA; AMPA; NMDA; I K; I M; I K,Ca; I Sodium; I Calcium; I Cl, leak;
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ReConv
data
readme.html
AmpaCOD.mod *
GRC_CA.mod *
GRC_CALC.mod *
GRC_GABA.mod *
GRC_KA.mod *
GRC_KCA.mod *
GRC_KIR.mod *
GRC_KM.mod *
GRC_KV.mod *
GRC_LKG1.mod *
GRC_LKG2.mod *
GRC_NA.mod *
NmdaS.mod *
Pregen.mod *
ComPanel.hoc
Grc_Cell.hoc
mosinit.hoc
Parametri.hoc
ReConv_GrC.jpg
ReConv_invitro.jpg
ReConv_invivo.jpg
Record_vext.hoc
Start.hoc
                            
: $Id: pregen.mod,v 1.3 2000/05/16 11:16:56 hines Exp $
: comments at end

NEURON	{ 
  POINT_PROCESS SpikeGenerator
  RANGE y
  RANGE fast_invl, slow_invl, burst_len, start, end,delay
  RANGE noise
}

PARAMETER {
	fast_invl	= 10 (ms)	: time between spikes in a burst (msec)
	slow_invl	= 0 (ms)	: burst period (msec)
: actually, above is interburst period in conformity with original version
: see
	burst_len	= 10		: burst length (# spikes)
	start		= 50 (ms)	: start of first interburst interval
	end		= 1e10 (ms)	: time to stop bursting
	noise		= 0		: amount of randomeaness (0.0 - 1.0)
	delay		= 4
}

ASSIGNED {
	y
	burst
	event (ms)
	burst_off (ms)
	burst_on (ms)
	toff (ms)
	on
}

PROCEDURE seed(x) {
	set_seed(x)
}

INITIAL {
	on = 1
	toff = 1e9
	y = -90
	burst = 0
	event = start - slow_invl
	:
	event_time()
	while (on == 1 && event < 0) {
		event_time()
	}
	if (on == 1) {
		net_send(event, 1)
	}
}	

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
	}else{
		interval = (1. - noise)*mean + noise*(mean*exprand(1)+delay) : (delay+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.
		}
	}else{
		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) {
		on = 0
	}
}

NET_RECEIVE (w) {
:printf("Pregen receive t=%g flag=%g\n", t, flag) 
	if (flag == 1 && on == 1) {
		y = 20
		net_event(t)
		event_time()
		net_send(event - t, 1)
		net_send(.1, 2)
	}
	if (flag == 2) {
		y = -90
	}
}

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 fast spike generator.  The trains
of spikes can be either periodic or noisy (Poisson-distributed), and 
either tonic or bursting.

Parameters;
   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

Modified by Michael Hines to use logical event style with NET_RECEIVE
ENDCOMMENT


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