def getCTree(cfit, locs, f_name, recompute_ctree=False, recompute_biophys=False):
"""
Uses `neat.CompartmentFitter` to derive a `neat.CompartmentTree` for the
given `locs`. The simplified tree is stored under `f_name`. If the
simplified tree exists, it is loaded by default in memory (unless
`recompute_ctree` is ``True``). The impedances for efficient impedance
matrix evaluation are also stored, and are by default reloaded if they exist
(unless `recompute_biophys` is ``True``).
"""
try:
if recompute_ctree:
raise IOError
print("\n>>>> loading file %s" % f_name)
file = open(f_name + ".p", "rb")
ctree = pickle.load(file)
clocs = pickle.load(file)
except (IOError, EOFError) as err:
print("\n>>>> (re-)deriving model %s" % f_name)
ctree = cfit.fit_model(
locs,
alpha_inds=[0],
parallel=True,
use_all_channels_for_passive=False,
recompute=recompute_biophys,
)
clocs = ctree.get_equivalent_locs()
print(">>>> writing file %s" % f_name)
file = open(f_name + ".p", "wb")
pickle.dump(ctree, file)
pickle.dump(clocs, file)
file.close()
return ctree, clocs
def runCaCoinc(
sim_tree,
locs,
ca_loc_idx,
soma_ind,
stim_type="psp",
dt=0.1,
t_max=300.0,
t_calibrate=100.0,
psp_params=dict(t_rise=0.5, t_decay=5.0, i_amp=0.5, t_stim=50.0),
i_in_params=dict(i_amp=1.9, t_onset=45.0, t_dur=5.0),
rec_kwargs=dict(
record_from_syns=False,
record_from_iclamps=False,
record_from_vclamps=False,
record_from_channels=False,
record_v_deriv=False,
),
pprint=True,
):
"""
Runs the BAC-firing protocol to elicit an AP burst in response to coincident
somatic and dendritic input
"""
# initialize the NEURON model
sim_tree.init_model(dt=dt, t_calibrate=t_calibrate, factor_lambda=10.0)
sim_tree.store_locs(locs, "rec locs")
if stim_type == "psp" or stim_type == "coinc":
sim_tree.add_double_exp_current(
locs[ca_loc_idx], psp_params["t_rise"], psp_params["t_decay"]
)
sim_tree.set_spiketrain(0, psp_params["i_amp"], [psp_params["t_stim"]])
if stim_type == "psp":
sim_tree.add_i_clamp(
locs[soma_ind], 0.0, i_in_params["t_onset"], i_in_params["t_dur"]
)
if stim_type == "current":
sim_tree.add_double_exp_current(
locs[ca_loc_idx], psp_params["t_rise"], psp_params["t_decay"]
)
sim_tree.set_spiketrain(0, 0.0, [psp_params["t_stim"]])
if stim_type == "current" or stim_type == "coinc":
sim_tree.add_i_clamp(
locs[soma_ind],
i_in_params["i_amp"],
i_in_params["t_onset"],
i_in_params["t_dur"],
)
# simulate the NEURON model
res = sim_tree.run(t_max, pprint=pprint, **rec_kwargs)
sim_tree.delete_model()
return res
def runCalciumCoinc(
recompute_ctree=False, recompute_biophys=False, axdict=None, pshow=True
):
global D2S_CASPIKE, D2S_APIC
global CA_LOC
lss_ = ["-", "-.", "--"]
css_ = [colours[3], colours[0], colours[1]]
lws_ = [0.8, 1.2, 1.6]
# create the full model
phys_tree = getL5Pyramid()
sim_tree = NeuronSimTree(phys_tree)
# compartmentfitter object
cfit = CompartmentFitter(phys_tree, name="bac_firing", path="data/")
# single branch initiation zone
branch = sim_tree.path_to_root(sim_tree[236])[::-1]
locs_sb = sim_tree.distribute_locs_at_d2s(
D2S_CASPIKE, node_arg=branch, name="single branch"
)
# abpical trunk locations
apic = sim_tree.path_to_root(sim_tree[221])[::-1]
locs_apic = sim_tree.distribute_locs_at_d2s(
D2S_APIC, node_arg=apic, name="apic connection"
)
# store set of locations
fit_locs = [(1, 0.5)] + locs_apic + locs_sb
sim_tree.store_locs(fit_locs, name="ca coinc")
# PSP input location index
ca_ind = sim_tree.get_nearest_loc_idxs([CA_LOC], name="ca coinc")[0]
# obtain the simplified tree
ctree, clocs = getCTree(
cfit,
fit_locs,
"data/ctree_bac_firing",
recompute_biophys=recompute_biophys,
recompute_ctree=recompute_ctree,
)
# print(ctree)
print("--- ctree nodes currents")
print("\n".join([str(n.currents) for n in ctree]))
reslist, creslist_sb, creslist_sb_ = [], [], []
locindslist_sb, locindslist_apic_sb = [], []
if axdict is None:
pl.figure("inp")
axes_input = [pl.subplot(131), pl.subplot(132), pl.subplot(133)]
pl.figure("V trace")
axes_trace = [pl.subplot(131), pl.subplot(132), pl.subplot(133)]
pl.figure("morph")
axes_morph = [pl.subplot(121), pl.subplot(122)]
else:
axes_input = axdict["inp"]
axes_trace = axdict["trace"]
axes_morph = axdict["morph"]
pshow = False
for jj, stim in enumerate(["current", "psp", "coinc"]):
print("--- sim full ---")
rec_locs = sim_tree.get_locs("ca coinc")
# runn the simulation
res = runCaCoinc(
sim_tree,
rec_locs,
ca_ind,
0,
stim_type=stim,
rec_kwargs=dict(record_from_syns=True, record_from_iclamps=True),
)
print("---- sim reduced ----")
rec_locs = clocs
# run the simulation of the reduced tree
csim_tree = NeuronCompartmentTree(ctree)
cres = runCaCoinc(
csim_tree,
rec_locs,
ca_ind,
0,
stim_type=stim,
rec_kwargs=dict(record_from_syns=True, record_from_iclamps=True),
)
id_offset = 1.0
vd_offset = 7.2
vlim = (-80.0, 20.0)
ilim = (-0.1, 2.2)
# input current
ax = axes_input[jj]
ax.plot(res["t"], -res["i_clamp"][0], c="r", lw=lwidth)
ax.plot(res["t"], res["i_syn"][0] + id_offset, c="b", lw=lwidth)
ax.set_yticks([0.0, id_offset])
if jj == 1 or jj == 2:
drawScaleBars(ax, ylabel=" nA", b_offset=0)
else:
drawScaleBars(ax)
if jj == 2:
ax.set_yticklabels([r"Soma", r"Dend"])
ax.set_ylim(ilim)
# somatic trace
ax = axes_trace[jj]
ax.set_xticks([0.0, 50.0])
ax.plot(res["t"], res["v_m"][0], c="DarkGrey", lw=lwidth)
ax.plot(cres["t"], cres["v_m"][0], c=cll[0], lw=1.6 * lwidth, ls="--")
# dendritic trace
# ax.plot(res['t'], res['v_m'][ca_ind]+vd_offset, c='DarkGrey', lw=lwidth)
# ax.plot(cres['t'], cres['v_m'][ca_ind]+vd_offset, c=cll[1], lw=1.6*lwidth, ls='--')
# ax.set_yticks([cres['v_m'][0][0], cres['v_m'][ca_ind][0]+vd_offset])
ax.plot(res["t"], res["v_m"][ca_ind], c="DarkGrey", lw=lwidth)
ax.plot(cres["t"], cres["v_m"][ca_ind], c=cll[1], lw=1.6 * lwidth, ls="--")
# ax.set_yticks([cres['v_m'][0][0], cres['v_m'][ca_ind][0]+vd_offset])
# if jj == 1 or jj == 2:
# drawScaleBars(ax, xlabel=' ms', ylabel=' mV', b_offset=15)
# # drawScaleBars(ax, xlabel=' ms', b_offset=25)
# else:
# drawScaleBars(ax)
# if jj == 2:
# ax.set_yticklabels([r'Soma', r'Dend'])
# ax.set_ylim(vlim)
print("iv")
plocs = sim_tree.get_locs("ca coinc")
markers = [{"marker": "s", "mfc": cfl[0], "mec": "k", "ms": markersize / 1.1}] + [
{"marker": "s", "mfc": cfl[1], "mec": "k", "ms": markersize / 1.1}
for _ in locs_apic + locs_sb
]
markers[ca_ind]["marker"] = "v"
plotargs = {"lw": lwidth / 1.3, "c": "DarkGrey"}
sim_tree.plot_2d_morphology(
axes_morph[0],
use_radius=False,
plotargs=plotargs,
marklocs=plocs,
loc_args=markers,
lims_margin=0.01,
)
# compartment tree dendrogram
labelargs = {0: {"marker": "s", "mfc": cfl[0], "mec": "k", "ms": markersize * 1.2}}
labelargs.update(
{
ii: {"marker": "s", "mfc": cfl[1], "mec": "k", "ms": markersize * 1.2}
for ii in range(1, len(plocs))
}
)
ctree.plot_dendrogram(
axes_morph[1], plotargs={"c": "k", "lw": lwidth}, labelargs=labelargs
)
pl.show()
if __name__ == "__main__":
if SIM_FLAG:
runCalciumCoinc()
else:
plotStoredImg("../docs/figures/bac_firing.png")
