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
"""Initialization for -2000 to -100"""
h.cvode.active(0)
dt = 0.1
h.steps_per_ms = 1.0 / dt
h.finitialize(-60)
h.t = -2000
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)
dt = 0.1
h.steps_per_ms = 1.0 / dt
h.finitialize(-60)
h.t = -2000
h.secondorder = 0
PP_to_BCs = np.array(PP_to_BCs)
# generate temporal patterns out of grid cell act profiles as an input for pyDentate
input_grid_out = inhom_poiss(par_traj, n_traj, dt_s=0.0001, seed=seed_2)
np.random.seed(seed_3) #seed_3 again for network generation & simulation
granule_output = np.empty((n_granule, n_traj), dtype = np.ndarray)
mossy_output = np.empty((n_mossy, n_traj), dtype = np.ndarray)
basket_output = np.empty((n_basket, n_traj), dtype = np.ndarray)
hipp_output = np.empty((n_hipp, n_traj), dtype = np.ndarray)
for trj in range(n_traj):
nw = net_tunedrev.TunedNetwork(seed_3, input_grid_out[:,trj], PP_to_GCs, PP_to_BCs)
# Attach voltage recordings to all cells
nw.populations[0].voltage_recording(range(n_granule))
nw.populations[1].voltage_recording(range(n_mossy))
nw.populations[2].voltage_recording(range(n_basket))
nw.populations[3].voltage_recording(range(n_hipp))
# Run the model
"""Initialization for -2000 to -100"""
h.cvode.active(0)
dt = 0.1
h.steps_per_ms = 1.0/dt
h.finitialize(-60)
h.t = -2000
h.secondorder = 0
h.dt = 10
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)
# Setup specs for stimulation
stim_pool = 500 # Size of the pool from which stimulated cells are chosen
stim_loc = int(2000 / 2.0 - stim_pool / 2.0)
stim_amp = 1.5
stim_dur = 10
stim_delay = 50
for run in runs:
# Create a standard networks and add the stimulation
nw = net_tunedrev.TunedNetwork(seed=10000 + run)
np.random.seed(10000 + run)
# Make sure we are not stimulating a cell we measure
stim_cells = np.random.choice(range(stim_loc, stim_loc + stim_pool),
n_cells,
replace=False)
while np.intersect1d(stim_cells, cells_to_measure).any():
stim_cells = np.random.choice(range(stim_loc, stim_loc + stim_pool),
n_cells,
replace=False)
print("Done intersecting")
nw.populations[0].current_clamp_range(stim_cells,
amp=stim_amp,
dur=stim_dur,
def pyDentate(input_grid_out, seed_2, n_traj, dur_ms):
savedir = os.getcwd()
input_scale = 1000
seed_3 = seed_2 + 150 #seed_3 for network generation & simulation
# Where to search for nrnmech.dll file. Must be adjusted for your machine.
# dll_files = [("C:\\Users\\DanielM\\Repos\\models_dentate\\"
# "dentate_gyrus_Santhakumar2005_and_Yim_patterns\\"
# "dentategyrusnet2005\\nrnmech.dll"),
# "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"),
# ("/home/baris/Python/mechs_7-6_linux/"
# "x86_64/.libs/libnrnmech.so")]
# for x in dll_files:
# if os.path.isfile(x):
# dll_dir = x
# print("DLL loaded from: " + dll_dir)
# h.nrn_load_dll(dll_dir)
#number of cells
n_grid = 200
n_granule = 2000
n_mossy = 60
n_basket = 24
n_hipp = 24
np.random.seed(seed_3) # seed_3 for connections in the network
# Randomly choose target cells for the GridCell lines
gauss_gc = stats.norm(loc=1000, scale=input_scale)
gauss_bc = stats.norm(loc=12,
scale=(input_scale / float(n_granule)) * n_basket)
pdf_gc = gauss_gc.pdf(np.arange(n_granule))
pdf_gc = pdf_gc / pdf_gc.sum()
pdf_bc = gauss_bc.pdf(np.arange(n_basket))
pdf_bc = pdf_bc / pdf_bc.sum()
GC_indices = np.arange(n_granule)
start_idc = np.random.randint(0, n_granule - 1, size=n_grid)
PP_to_GCs = []
for x in start_idc:
curr_idc = np.concatenate((GC_indices[x:n_granule], GC_indices[0:x]))
PP_to_GCs.append(
np.random.choice(curr_idc, size=100, replace=False, p=pdf_gc))
PP_to_GCs = np.array(PP_to_GCs)
BC_indices = np.arange(n_basket)
start_idc = np.array(((start_idc / float(n_granule)) * 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 out of grid cell act profiles as an input for pyDentate
# input_grid_out = inhom_poiss(par_traj, n_traj, dt_s=0.0001, seed=seed_2)
np.random.seed(seed_3) #seed_3 again for network generation & simulation
granule_output = np.empty((n_granule, n_traj), dtype=np.ndarray)
mossy_output = np.empty((n_mossy, n_traj), dtype=np.ndarray)
basket_output = np.empty((n_basket, n_traj), dtype=np.ndarray)
hipp_output = np.empty((n_hipp, n_traj), dtype=np.ndarray)
for trj in range(n_traj):
nw = net_tunedrev.TunedNetwork(seed_3, input_grid_out[:, trj],
PP_to_GCs, PP_to_BCs)
# Attach voltage recordings to all cells
nw.populations[0].voltage_recording(range(n_granule))
nw.populations[1].voltage_recording(range(n_mossy))
nw.populations[2].voltage_recording(range(n_basket))
nw.populations[3].voltage_recording(range(n_hipp))
# Run the model
"""Initialization for -2000 to -100"""
h.cvode.active(0)
dt = 0.1
h.steps_per_ms = 1.0 / dt
h.finitialize(-60)
h.t = -2000
h.secondorder = 0
h.dt = 10
while h.t < -100:
h.fadvance()
h.secondorder = 2
h.t = 0
h.dt = 0.1
"""Setup run control for -100 to 1500"""
h.frecord_init() # Necessary after changing t to restart the vectors
while h.t < dur_ms:
h.fadvance()
print("Done Running")
granule_output[:, trj] = np.array(
[cell[0].as_numpy() for cell in nw.populations[0].ap_counters])
# mossy_output[:,trj] = np.array([cell[0].as_numpy() for cell in nw.populations[1].ap_counters])
# basket_output[:,trj] = np.array([cell[0].as_numpy() for cell in nw.populations[2].ap_counters])
# hipp_output[:,trj] = np.array([cell[0].as_numpy() for cell in nw.populations[3].ap_counters])
return granule_output, mossy_output, basket_output, hipp_output
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)
PP_to_BCs = PP_to_BCs[0:24]
# Generate temporal patterns for the 100 PP inputs
temporal_patterns = inhom_poiss(rate=input_frequency)
temporal_patterns[0:24]
nw = net_tunedrev.TunedNetwork(nw_seed[0], temporal_patterns, PP_to_GCs,
PP_to_BCs)
# 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)
dt = 0.1
h.steps_per_ms = 1.0 / dt
h.finitialize(-60)
h.t = -2000
h.secondorder = 0
h.dt = 10