def __init__(self):
self.cells = [Cell(i) for i in range(5)]
cvode.use_fast_imem(1)
# a few intrinsically firing ARTIFICIAL_CELLS with and without gids
self.acells = [h.IntervalFire() for _ in range(8)]
r = h.Random()
r.Random123(6, 0, 0)
for a in self.acells:
a.tau = r.uniform(2, 5)
a.invl = r.uniform(2, 4)
for i, a in enumerate(self.acells[5:]):
pc.set_gid2node(i + 5, pc.id())
pc.cell(i + 5, h.NetCon(a, None))
def __init__(self):
self.cells = [Cell(i) for i in range(5)]
# This is not supported on GPU, see:
# https://github.com/BlueBrain/CoreNeuron/issues/197
cvode.use_fast_imem(not enable_gpu)
# a few intrinsically firing ARTIFICIAL_CELLS with and without gids
self.acells = [h.IntervalFire() for _ in range(8)]
r = h.Random()
r.Random123(6, 0, 0)
for a in self.acells:
a.tau = r.uniform(2, 5)
a.invl = r.uniform(2, 4)
for i, a in enumerate(self.acells[5:]):
pc.set_gid2node(i + 5, pc.id())
pc.cell(i + 5, h.NetCon(a, None))
def test_inputpresyn():
# NetStim with gid = 1 connected to IntFire1 with gid = 2
# sadly IntFire1 does not exist in coreneuron so use IntervalFire
# make cells
nstim = 3
ncell = 15
cells = {gid: None for gid in range(pc.id(), ncell, pc.nhost())}
for gid in cells:
pc.set_gid2node(gid, pc.id())
cells[gid] = h.NetStim() if gid < nstim else h.IntervalFire()
pc.cell(gid, h.NetCon(cells[gid], None))
# connect
netcons = {}
for gid in cells:
if gid >= nstim:
for srcgid in range(nstim):
netcons[(srcgid,
gid)] = nc = pc.gid_connect(srcgid, cells[gid])
nc.delay = 1
nc.weight[0] = 2
else: # The NetStim
ns = cells[gid]
ns.start = 0
ns.number = 1
ns.interval = 10
# does it look like what we want?
if False:
for gid in cells:
print(pc.id(), gid, cells[gid])
for con in netcons:
print(pc.id(), con)
spiketime = h.Vector()
gidvec = h.Vector()
pc.spike_record(-1, spiketime, gidvec)
def run(tstop):
spiketime.resize(0)
gidvec.resize(0)
pc.set_maxstep(10)
h.finitialize()
pc.psolve(tstop)
run(2)
spikes_std = sortspikes(spiketime, gidvec)
def same():
spikes = sortspikes(spiketime, gidvec)
assert spikes_std == spikes
h.CVode().cache_efficient(1)
from neuron import coreneuron
coreneuron.enable = 1
coreneuron.verbose = 0
run(2)
same()
pc.barrier()