def build_complete_model(connection_file):
# distribute
nmc.build_net_round_robin(getmodel(), connection_file)
import distribute
import multisplit_distrib
multisplit_distrib.multisplit_distrib(distribute.getmodel())
# set initial weights
if len(params.initial_weights) > 0:
weightsave.weight_load(params.initial_weights)
# print sections
nc = h.List("NetCon")
nc = int(pc.allreduce(nc.count(),1))
if rank == 0: print "NetCon count = ", nc
nseg = 0
for sec in h.allsec():
nseg += sec.nseg
nseg = int(pc.allreduce(nseg, 1))
if rank == 0: print "Total # compartments = ", nseg
util.show_progress(200)
#from odorstim import OdorSequence
#odseq = OdorSequence(params.odor_sequence)
if rank == 0: print 'total setup time ', h.startsw()-startsw
def build_complete_model(connection_file):
# distribute
nmc.build_net_round_robin(getmodel(), connection_file)
import distribute
import multisplit_distrib
multisplit_distrib.multisplit_distrib(distribute.getmodel())
# set initial weights
if len(params.initial_weights) > 0:
weightsave.weight_load(params.initial_weights)
# print sections
nc = h.List("NetCon")
nc = int(pc.allreduce(nc.count(), 1))
if rank == 0: print "NetCon count = ", nc
nseg = 0
for sec in h.allsec():
nseg += sec.nseg
nseg = int(pc.allreduce(nseg, 1))
if rank == 0: print "Total # compartments = ", nseg
util.show_progress(200)
import custom_params
if custom_params.enableOdorInput:
from odorstim import OdorSequence
odseq = OdorSequence(params.odor_sequence)
if rank == 0: print 'total setup time ', h.startsw() - startsw
def build_part_model(gloms, mitrals, dicfile=''):
model = getmodel()
model.clear()
# gids
gids = set()
for glomid in gloms:
gids.update(range(glomid * params.Nmitral_per_glom, (glomid+1) * params.Nmitral_per_glom) + \
range(glomid * params.Nmtufted_per_glom + params.gid_mtufted_begin, (glomid+1) * params.Nmtufted_per_glom + params.gid_mtufted_begin))
gids.update(mitrals)
# distribute
nmc.build_net_round_robin(model, gids, dicfile)
import distribute
if False:
# CoreNEURON does not support multisplit
import multisplit_distrib
multisplit_distrib.multisplit_distrib(distribute.getmodel())
if params.gap_junctions_active:
gapjunc.init()
# set initial weights
if len(params.initial_weights) > 0:
weightsave.weight_load(params.initial_weights)
# print sections
nc = h.List("NetCon")
nc = int(pc.allreduce(nc.count(), 1))
if rank == 0: print "NetCon count = ", nc
nseg = 0
for sec in h.allsec():
nseg += sec.nseg
nseg = int(pc.allreduce(nseg, 1))
if rank == 0: print "Total # compartments = ", nseg
pc.spike_record(-1, parrun.spikevec, parrun.idvec)
util.show_progress(200)
odseq = [OdorStim(*od) for od in params.odor_sequence]
model.odseq = odseq
# record
for rec in params.sec2rec:
vr.record(*rec)
if rank == 0: print 'total setup time ', h.startsw() - startsw
# put in spiketime order
srt = tvec.sortindex()
tvec.index(tvec, srt)
gidvec.index(gidvec, srt)
#make the PatternStim
ps = h.PatternStim()
ps.play(tvec, gidvec)
return (tvec, gidvec, ps, spk_standard)
mksubset(model)
# inits the weights
if weights_file:
import weightsave
weightsave.weight_load(weights_file)
import dummysyns
dummysyns.mk_dummy_syns([])
import odorstim
odseq = odorstim.OdorSequence(params.odor_sequence)
ps = patstim(spike_file)
def spkrecord(model):
#record only the output spikes from cells for camparison with ps[3] spk_standard
spkvec = h.Vector()
gidvec = h.Vector()
for gid in model.gids:
pc.spike_record(gid, spkvec, gidvec)