/************************************************************
'ca1' model code repository
Written by Marianne Bezaire, marianne.bezaire@gmail.com, www.mariannebezaire.com
In the lab of Ivan Soltesz, www.ivansolteszlab.org
Published and latest versions of this code are available online at:
ModelDB:
Open Source Brain: http://www.opensourcebrain.org/projects/nc_ca1
Main code file: ../main.hoc
This file: Last updated on April 10, 2015
This file defines two procedures related to spontaneous stimulation:
1. connectStims: this is used when each cell needs to have its own,
unique excitatory spike train input. This procedure connects
a specific artificial cell to each real cell of the model, so that
each real cell has its own independent input. When this procedure
is used, then the artificial cells should NOT be connected to the
real cells within the connectivity algorithm. If the connectivity
algorithm is used instead, the difference is that any particular
artificial cell could be connected to 0, 1, or multiple real cells
in lines with whatever positioning and axonal distribution properties
have been specified. This is a good alternative for when the
artificial cells are meant to mimic real inputs to a network.
2. setNoise: this procedure sets the properties of the random number
generators that are then associated to the noise functionality of
the artificial cells. Each artificial cell has its own, unique
random stream that is associated to its noise algorithm. The noise
algorithm then draws from that random number stream to determine
the interspike intervals that define the spike train pattern of the cell.
************************************************************/
// The connectStims procedure connects a specific artificial cell to each real cell of the model, and it
// does this for each artificial cell type. It expects there to be enough artificial cells of each type
// that a unique artificial cell can be connected with each real cell of the model. The specific
// artificial cell that is connected to each real cell has a gid that is related to the gid of the real
// cell.
proc connectStims() { local wgt, reli, typei, jgid, postcelltype, precelltype localobj cell
for precelltype=0, numCellTypes-1 { // For each potential presynaptic cell type
if (cellType[precelltype].is_art==1) { // If the cell is an artificial cell type
for postcelltype=0, numCellTypes-1 { // For each potential postsynaptic cell type
if (cellType[postcelltype].is_art==0) { // If the cell is a real cell type
wgt = cellType[precelltype].wgtConns.x[postcelltype] // Check the weight specified for this connection
if (wgt > 0) { // and if stronger than 0, make the connections
// For each cell of type postsynaptic cell type owned by this processor
for pcitr(&reli, &typei, &jgid, cellType[postcelltype].cellStartGid, cellType[postcelltype].cellEndGid) {
if (pc.gid_exists(jgid)) {
cell = pc.gid2cell(jgid) // Create a reference to the postsynaptic cell
if (cell.pre_list.o(precelltype).count()>0) { // If potential synapses have been defined for this connection type
// Make a single synapse connection from a specific artificial cell to a specific real cell,
// where the cells that are connected are related by their gids.
nc_appendo(jgid-cellType[postcelltype].cellEndGid-1+cellType[precelltype].cellStartGid, cell, precelltype, 0, wgt, 3)
cellType[precelltype].numCons.x[postcelltype] +=1 // Increment the connection counter
}
}
}
}
}
}
}
}
}
if (MakeArtConns==0) { // Only make connections from artificial cells if they
connectStims() // have not been connected at another time (ie, in the
} // connectivity algorithm)
proc setNoise() {local reli, typei, jgid, idx localobj cell
for idx=0, numCellTypes-1 {
if (cellType[idx].is_art==1) { // If the cell type is artificial, set the random stream
// associated with the noise in its spike times
// For all the artificial cells of this type, owned by this processor
for pcitr(&reli, &typei, &jgid, cellType[idx].cellStartGid, cellType[idx].cellEndGid) {
cell = pc.gid2cell(jgid) // Create a reference to the artificial cell
ranstimlist.object(reli).r.negexp(1) // Using a negative exponential distribution to determine
// the interspike interval (ISI) gives Poisson-distributed
// spike times. Always use 1 as the argument; the interval
// for the netstim will set the 'rate' (or average ISI).
//
// For another example (which defines the randomstream class
// differently, and therefore calls different commands at
// different times) see the following file:
// <http://senselab.med.yale.edu/modeldb/showmodel.asp?model=83319&file=\destexhe_benchmarks\NEURON\common\netstim.hoc>
cell.noiseFromRandom(ranstimlist.object(reli).r) // Set this random stream as the random number
} // generator for the noise functionality of
} // the spike generator.
}
}
setNoise()
