PP_to_GCs = np.array(PP_to_GCs)
BC_indices = np.arange(24)
start_idc = np.array(((start_idc / 2000.0) * 24), dtype=int)
PP_to_BCs = []
for x in start_idc:
curr_idc = np.concatenate((BC_indices[x:24], BC_indices[0:x]))
PP_to_BCs.append(
np.random.choice(curr_idc, size=1, replace=False, p=pdf_bc))
PP_to_BCs = np.array(PP_to_BCs)
# Generate temporal patterns for the 100 PP inputs
np.random.seed(seed)
temporal_patterns = inhom_poiss(rate=args.poiss_rate)
# Start the runs of the model
for run in runs:
nw = net_tunedrev.TunedNetwork(seed,
temporal_patterns[0 + run:24 + run],
PP_to_GCs[0 + run:24 + run],
PP_to_BCs[0 + run:24 + run],
pp_weight=args.pp_weight)
# Attach voltage recordings to all cells
nw.populations[0].voltage_recording(range(2000))
nw.populations[1].voltage_recording(range(60))
nw.populations[2].voltage_recording(range(24))
nw.populations[3].voltage_recording(range(24))
# Run the model
PP_to_GCs = np.array(PP_to_GCs)
# Generate the PP -> BC mapping as above
BC_indices = np.arange(24)
start_idc = np.array(((start_idc / 2000.0) * 24), dtype=int)
PP_to_BCs = []
for x in start_idc:
curr_idc = np.concatenate((BC_indices[x:24], BC_indices[0:x]))
PP_to_BCs.append(
np.random.choice(curr_idc, size=1, replace=False, p=pdf_bc))
PP_to_BCs = np.array(PP_to_BCs)
# Generate temporal patterns for the 100 PP inputs
np.random.seed(seed)
temporal_patterns = inhom_poiss(rate=30)
# Start the runs of the model
for run in runs:
nw = net_tunedrev.TunedNetwork(seed, temporal_patterns[0 + run:24 + run],
PP_to_GCs[0 + run:24 + run],
PP_to_BCs[0 + run:24 + run])
# Attach voltage recordings to all cells
nw.populations[0].voltage_recording(range(2000))
nw.populations[1].voltage_recording(range(60))
nw.populations[2].voltage_recording(range(24))
nw.populations[3].voltage_recording(range(24))
# Run the model
"""Initialization for -2000 to -100"""
h.cvode.active(0)
"C:\\Users\\daniel\\Repos\\nrnmech.dll",
("C:\\Users\\Holger\\danielm\\models_dentate\\"
"dentate_gyrus_Santhakumar2005_and_Yim_patterns\\"
"dentategyrusnet2005\\nrnmech.dll"),
("C:\\Users\\Daniel\\repos\\"
"dentate_gyrus_Santhakumar2005_and_Yim_patterns\\"
"dentategyrusnet2005\\nrnmech.dll")]
for x in dll_files:
if os.path.isfile(x):
dll_dir = x
print("DLL loaded from: " + str(dll_dir))
h.nrn_load_dll(dll_dir)
# Generate temporal patterns for the 100 PP inputs
np.random.seed(10000)
temporal_patterns = inhom_poiss()
# Generate the PP -> GC mapping so that each GC receives inputs from 20/400
# randomly chosen PP inputs
innervation_pattern_gc = np.array(
[np.random.choice(400, 20, replace=False) for x in range(2000)])
innervation_pattern_gc = innervation_pattern_gc.swapaxes(0, 1)
PP_to_GCs = []
for x in range(0, 400):
PP_to_GCs.append(np.argwhere(innervation_pattern_gc == x)[:, 1])
PP_to_GCs = np.array(PP_to_GCs)
# Generate the PP -> BC mapping as above
innervation_pattern_bc = np.array(
PP_to_GCs = np.array(PP_to_GCs)
BC_indices = np.arange(24)
start_idc = np.array(((start_idc / 2000.0) * 24), dtype=int)
PP_to_BCs = []
for x in start_idc:
curr_idc = np.concatenate((BC_indices[x:24], BC_indices[0:x]))
PP_to_BCs.append(
np.random.choice(curr_idc, size=1, replace=False, p=pdf_bc))
PP_to_BCs = np.array(PP_to_BCs)
# Generate temporal patterns for the 100 PP inputs
np.random.seed(input_seed)
temporal_patterns = inhom_poiss(rate=input_freq)
seed_input_vectors = []
seed_output_vectors = []
seed_input_time_stamps = []
seed_output_time_stamps = []
# Start the runs of the model
for run in runs:
print("Run: " + str(run))
nw = net_tunedrevdecaysdelayspeaknorm_linux.TunedNetwork(
seed, temporal_patterns[0 + run:24 + run],
PP_to_GCs[0 + run:24 + run], PP_to_BCs[0 + run:24 + run], bc_decay,
hc_decay, bc_delay, hc_delay, gc_weight, bc_weight, hc_weight)
# Run the model
"""Initialization for -2000 to -100"""
PP_to_GCs = np.array(PP_to_GCs)
# Generate the PP -> BC mapping as above
BC_indices = np.arange(24)
start_idc = np.array(((start_idc / 2000.0) * 24), dtype=int)
PP_to_BCs = []
for x in start_idc:
curr_idc = np.concatenate((BC_indices[x:24], BC_indices[0:x]))
PP_to_BCs.append(
np.random.choice(curr_idc, size=1, replace=False, p=pdf_bc))
PP_to_BCs = np.array(PP_to_BCs)
# Generate temporal patterns for the 100 PP inputs
np.random.seed(10000)
patterns_temp = inhom_poiss()
temporal_patterns = []
for x in range(patterns_temp.shape[0]):
curr_patterns = np.array([])
for y in range(5):
curr_patterns = np.concatenate(
(curr_patterns,
patterns_temp[x][patterns_temp[x] < 100] + (100.0 * y)))
temporal_patterns.append(curr_patterns)
temporal_patterns = np.array(temporal_patterns)
# Start the runs of the model
for run in runs:
nw = net_tunedrev.TunedNetwork(10000, temporal_patterns[0 + run:24 + run],
PP_to_GCs[0 + run:24 + run],