def ev(self, time):
''' set odors stimulation intensity '''
model = getmodel()
for gid, cell in model.mitrals.items():
if gidfunc.ismitral(gid):
g_e_baseline = params.orn_g_mc_baseline
std_e = params.orn_g_mc_std
g_e_max = params.orn_g_mc_max
else:
g_e_baseline = params.orn_g_mt_baseline
std_e = params.orn_g_mt_std
g_e_max = params.orn_g_mt_max
if h.section_exists("tuftden", 0, cell):
for i in range(int(cell.ornsyn.count())):
cell.ornsyn.o(i).cc_peak = self.w_odor[mgid2glom(gid)]
cell.ornsyn.o(i).g_e_baseline = g_e_baseline
cell.ornsyn.o(i).g_e_max = g_e_max
cell.ornsyn.o(i).std_e = std_e
for nc in self.netcons.values():
nc.event(h.t)
if self.verbose and rank == 0:
print 'activation of %s at %g (ms)\tinterval %g' % (
self.odorname, time, self.next_invl)
# set up for next sniff
self.next_invl = self.rng_act.repick()
if (time + self.next_invl) < (self.start + self.dur):
h.cvode.event(time + self.next_invl,
(self.ev, (time + self.next_invl, )))
def mkmitral(gid):
if ismitral(gid):
nrn = genMitral(gid)
m = h.Mitral(gid)
else:
nrn = genMTufted(gid)
m = h.mTufted(gid)
m.createsec(len(nrn.dend), len(nrn.tuft))
for i, d in enumerate(nrn.dend):
if d.parent == nrn.soma[0]:
m.secden[i].connect(m.soma(.5))
else:
index = nrn.dend.index(d.parent)
m.secden[i].connect(m.secden[index](1))
m.geometry() # again to get the hillock stylized shape
fillall(nrn, m)
m.subsets()
m.topol() # need to connect secondary dendrites explicitly
m.segments(
) # again to get the proper number of segments for tuft and secden
m.memb()
m.setup_orns(gid, params.stream_orn_w)
if ismitral(gid):
orn_g_std = params.orn_g_mc_std
orn_g_baseline = params.orn_g_mc_baseline
orn_g_max = params.orn_g_mc_max
elif ismtufted(gid):
orn_g_std = params.orn_g_mt_std
orn_g_baseline = params.orn_g_mt_baseline
orn_g_max = params.orn_g_mt_max
for i in range(int(m.ntuft)):
m.ornsyn.o(i).g_e_baseline = orn_g_baseline
m.ornsyn.o(i).std_e = orn_g_std
m.ornsyn.o(i).g_e_max = orn_g_max
if mgid2glom(gid) in params.vclamp:
m.vcinit()
return m
def __init__(self, mgid, blanesgid, w=None):
self.mgid = mgid
self.blanes_gid = blanesgid
self.blanes_cell = getmodel().blanes[blanesgid]
rng = params.ranstream(blanesgid,
params.stream_blanes + gidfunc.mgid2glom(mgid))
ibranch = int(rng.discunif(0, self.blanes_cell.nden0 - 1))
idend = int(rng.discunif(0, self.blanes_cell.nden1 - 1))
x = rng.uniform(0, 1)
self.syn = h.Exp2Syn(self.blanes_cell.dend[ibranch][idend](x))
self.syn.e = 0
self.syn.tau1 = 1
self.syn.tau2 = 250
self.nc = pc.gid_connect(mgid, self.syn)
if w:
self.nc.weight[0] = w
else:
self.nc.weight[0] = params.mt2bc_exc_gmax
self.nc.delay = 1
self.gid = gidfunc.mbs_gid(mgid, blanesgid)
def init():
data = {}
for uid in range(nhost):
data.update({ uid:(getmodel().mitrals.keys()) })
data = a2a.all2all(data)
mgids = []
for _mgids in data.values(): mgids += _mgids
mgids = set(mgids)
# initialize source
for mgid in getmodel().mitrals.keys():
mpriden = split.mpriden(mgid)
if not mpriden:
continue
rgj = params.ranstream(mgid, params.stream_gap_junction)
mpriden.push()
secref = h.SectionRef()
h.pop_section()
h.mk_gj_src(pc, mgid, secref)
glomid = mgid2glom(mgid)
sistergids = []
# no longer all to all, only a chain
if not (ismtufted(mgid) and (mgid - nmi) % nmt == (nmt - 1)):
if ismitral(mgid) and mgid % nmxg == (nmxg - 1):
sistergids += [glomid * nmt + nmi]
else:
sistergids += [mgid + 1]
if not (ismitral(mgid) and mgid % nmxg == 0):
if ismtufted(mgid) and (mgid - nmi) % nmt == 0:
sistergids += [(glomid + 1) * nmxg - 1]
else:
sistergids += [mgid - 1]
sistergids = mgids.intersection(range(glomid * nmxg, glomid * nmxg + nmxg) + range(glomid * nmt + nmi, glomid * nmt + nmt + nmi)).difference([ mgid ])
for sistermgid in sistergids:
gap = h.Gap(mpriden(0.99))
if ismitral(mgid) and ismitral(sistermgid):
gap.g = rgj.uniform(gj_min_g1, gj_max_g1)
elif ismtufted(mgid) and ismtufted(sistermgid):
gap.g = rgj.uniform(gj_min_g3, gj_max_g3)
else:
gap.g = rgj.uniform(gj_min_g2, gj_max_g2)
getmodel().gj[(mgid, sistermgid)] = gap
pc.barrier()
# initialize targets
for key, gap in getmodel().gj.items():
mgid, sistermgid = key
pc.target_var(gap, gap._ref_vgap, sistermgid)
util.elapsed('Gap junctions built')
def __init__(self, odorname, start, dur, conc):
''' Specifies the odor for an OdorStim. Note that the OdorStim is
activated with setup which can only be called after the mitrals
dict exists (usually from determine_connections.py).
'''
if rank == 0:
print("OdorStim %s start=%g dur=%g conc=%g" %
(odorname, start, dur, conc))
self.odorname = odorname
self.start = start
self.dur = dur
self.conc = conc
self.verbose = True
self.next_invl = 0
if params.glomerular_layer == 0:
self.w_odor = odors.odors[odorname].getORNs(conc) # odor vector
elif params.glomerular_layer == 1:
self.w_odor = odors.odors[odorname].afterPG_1(conc) # odor vector
elif params.glomerular_layer == 2:
self.w_odor = odors.odors[odorname].afterPG_2(conc) # odor vector
# set up the netcons to stimulate the ORNs
self.netcons = {}
if not use_OdorStimHelper:
self.rng_act = params.ranstream(0, params.stream_orn_act)
self.rng_act.uniform(params.sniff_invl_min, params.sniff_invl_max)
model = getmodel()
self.src = None
if use_OdorStimHelper or allow_prcellstate_debug:
src = h.OdorStimHelper()
src.start = start
src.dur = dur
src.invl_min = params.sniff_invl_min
src.invl_max = params.sniff_invl_max
src.noiseFromRandom123(0, params.stream_orn_act, 0)
self.src = src
for gid, cell in model.mitrals.items():
peak = self.w_odor[mgid2glom(gid)]
if gidfunc.ismitral(gid):
g_e_baseline = params.orn_g_mc_baseline
std_e = params.orn_g_mc_std
g_e_max = params.orn_g_mc_max
else:
g_e_baseline = params.orn_g_mt_baseline
std_e = params.orn_g_mt_std
g_e_max = params.orn_g_mt_max
if h.section_exists("tuftden", 0, cell):
for i in range(int(cell.ornsyn.count())):
nc = h.NetCon(self.src, cell.ornsyn.o(i))
self.netcons[(gid, i)] = nc
# NetCon weights are peak, base, gemax, stde
nc.weight[0] = peak
nc.weight[1] = g_e_baseline
nc.weight[2] = g_e_max
nc.weight[3] = std_e
nc.delay = 0.0
if use_OdorStimHelper:
pass
else:
self.fih = h.FInitializeHandler(0, (self.init_ev, (start, )))
