def __init__(self,
seed=None,
temporal_patterns=np.array([]),
spatial_patterns_gcs=np.array([]),
spatial_patterns_bcs=np.array([]),
BC_decay=20,
HC_decay=20,
BC_delay=0.85,
HC_delay=3.8,
GC_weight=2.5 * 10**(-2),
BC_weight=1.2 * 10**(-3),
HC_weight=0.6 * 10**(-2)):
self.init_params = locals()
self.init_params['self'] = str(self.init_params['self'])
# Setup cells
self.mk_population(GranuleCell, 2000)
self.mk_population(MossyCell, 60)
self.mk_population(BasketCell, 24)
self.mk_population(HippCell, 24)
# Set seed for reproducibility
if seed:
self.set_numpy_seed(seed)
# Setup recordings
self.populations[0].record_aps()
self.populations[1].record_aps()
self.populations[2].record_aps()
self.populations[3].record_aps()
temporal_patterns = np.array(temporal_patterns)
if (type(spatial_patterns_gcs) == np.ndarray
and type(temporal_patterns) == np.ndarray):
for pa in range(len(spatial_patterns_gcs)):
# PP -> GC
gennetwork.PerforantPathPoissonTmgsyn(self.populations[0],
temporal_patterns[pa],
spatial_patterns_gcs[pa],
'midd', 10, 0, 1, 0, 0,
1 * 10**(-3))
if (type(spatial_patterns_bcs) == np.ndarray
and type(temporal_patterns) == np.ndarray):
for pa in range(len(spatial_patterns_bcs)):
# PP -> BC
gennetwork.PerforantPathPoissonTmgsyn(self.populations[2],
temporal_patterns[pa],
spatial_patterns_bcs[pa],
'ddend', 6.3, 0, 1, 0, 0,
1 * 10**(-3))
# GC -> MC
gennetwork.tmgsynConnection(self.populations[0], self.populations[1],
12, 'proxd', 1, 7.6, 500, 0.1, 0, 0, 10,
1.5, 0.2 * 10**(-2) * 10)
# GC -> BC
# Weight x4, target_pool = 2
gennetwork.tmgsynConnection(self.populations[0], self.populations[2],
8, 'proxd', 1, 8.7, 500, 0.1, 0, 0, 10,
0.8, GC_weight)
# GC -> HC
# Divergence x4; Weight doubled; Connected randomly.
gennetwork.tmgsynConnection(self.populations[0], self.populations[3],
24, 'proxd', 1, 8.7, 500, 0.1, 0, 0, 10,
1.5, GC_weight)
# MC -> MC
gennetwork.tmgsynConnection(self.populations[1], self.populations[1],
24, 'proxd', 3, 2.2, 0, 1, 0, 0, 10, 2,
0.5 * 10**(-3))
# MC -> BC
gennetwork.tmgsynConnection(self.populations[1], self.populations[2],
12, 'proxd', 1, 2, 0, 1, 0, 0, 10, 3,
0.3 * 10**(-3))
# MC -> HC
gennetwork.tmgsynConnection(self.populations[1], self.populations[3],
20, 'midd', 2, 6.2, 0, 1, 0, 0, 10, 3,
0.2 * 10**(-3))
# BC -> GC
# # synapses x3; Weight *1/4; tau from 5.5 to 20 (Hefft & Jonas, 2005)
gennetwork.tmgsynConnection(self.populations[2], self.populations[0],
560, 'soma', 400, 20 * BC_decay, 0, 1, 0,
-70, 10, BC_delay, BC_weight / BC_decay)
# We reseed here to make sure that those connections are consistent
# between this and net_global which has a global target pool for
# BC->GC.
if seed:
self.set_numpy_seed(seed + 1)
# BC -> MC
gennetwork.tmgsynConnection(self.populations[2], self.populations[1],
28, 'proxd', 3, 3.3, 0, 1, 0, -70, 10, 1.5,
1.5 * 10**(-3))
# BC -> BC
gennetwork.tmgsynConnection(self.populations[2], self.populations[2],
12, 'proxd', 2, 1.8, 0, 1, 0, -70, 10, 0.8,
7.6 * 10**(-3))
# HC -> GC
# Weight x10; Nr synapses x4; tau from 6 to 20 (Hefft & Jonas, 2005)
gennetwork.tmgsynConnection(self.populations[3], self.populations[0],
2000, 'dd', 640, 20 * HC_decay, 0, 1, 0,
-70, 10, HC_delay, HC_weight / HC_decay)
# HC -> MC
gennetwork.tmgsynConnection(self.populations[3], self.populations[1],
60, ['mid1d', 'mid2d'], 4, 6, 0, 1, 0, -70,
10, 1, 1.5 * 10**(-3))
# HC -> BC
gennetwork.tmgsynConnection(self.populations[3], self.populations[2],
24, 'ddend', 4, 5.8, 0, 1, 0, -70, 10, 1.6,
0.5 * 10**(-3))
def __init__(self,
seed=None,
temporal_patterns=np.array([]),
spatial_patterns_gcs=np.array([]),
spatial_patterns_bcs=np.array([]),
network_type='full',
pp_weight=1e-3):
self.init_params = locals()
self.init_params['self'] = str(self.init_params['self'])
# Setup cells
self.mk_population(GranuleCell, 2000)
self.mk_population(MossyCell, 60)
self.mk_population(BasketCell, 24)
self.mk_population(HippCell, 24)
# Set seed for reproducibility
if seed:
self.set_numpy_seed(seed)
# Setup recordings
self.populations[0].record_aps()
self.populations[1].record_aps()
self.populations[2].record_aps()
self.populations[3].record_aps()
temporal_patterns = np.array(temporal_patterns, dtype=object)
# weights
# feedforward inhibition
pp_bc = pp_weight
# feedback inhibition
gc_bc = 2.5e-2
gc_hc = 2.5e-2
gc_mc = 2e-2
# complete inhibition
bc_gc = 1.2e-3
hc_gc = 6e-3
if network_type == "no-feedback":
# Set GC to BC, HC and MC weights to 0
gc_bc, gc_hc, gc_mc = 0, 0, 0
elif network_type == "no-feedforward":
# Set PP to BC weight to 0
pp_bc = 0
elif network_type == "disinhibited":
bc_gc, hc_gc = 0, 0
elif network_type != "full":
raise ValueError("""network_type must be 'full',
'no-feedback', 'no-feedforward' or 'disinhibited'""")
if (type(spatial_patterns_gcs) == np.ndarray
and type(temporal_patterns) == np.ndarray):
for pa in range(len(spatial_patterns_gcs)):
# PP -> GC
gennetwork.PerforantPathPoissonTmgsyn(self.populations[0],
temporal_patterns[pa],
spatial_patterns_gcs[pa],
'midd', 10, 0, 1, 0, 0,
pp_weight)
if (type(spatial_patterns_bcs) == np.ndarray
and type(temporal_patterns) == np.ndarray):
for pa in range(len(spatial_patterns_bcs)):
# PP -> BC
gennetwork.PerforantPathPoissonTmgsyn(self.populations[2],
temporal_patterns[pa],
spatial_patterns_bcs[pa],
'ddend', 6.3, 0, 1, 0, 0,
pp_bc)
# GC -> MC
gennetwork.tmgsynConnection(self.populations[0], self.populations[1],
12, 'proxd', 1, 7.6, 500, 0.1, 0, 0, 10,
1.5, gc_mc)
# GC -> BC
# Weight x4, target_pool = 2
gennetwork.tmgsynConnection(self.populations[0], self.populations[2],
8, 'proxd', 1, 8.7, 500, 0.1, 0, 0, 10,
0.8, gc_bc)
# GC -> HC
# Divergence x4; Weight doubled; Connected randomly.
gennetwork.tmgsynConnection(self.populations[0], self.populations[3],
24, 'proxd', 1, 8.7, 500, 0.1, 0, 0, 10,
1.5, gc_hc)
# MC -> MC
gennetwork.tmgsynConnection(self.populations[1], self.populations[1],
24, 'proxd', 3, 2.2, 0, 1, 0, 0, 10, 2,
5e-4)
# MC -> BC
gennetwork.tmgsynConnection(self.populations[1], self.populations[2],
12, 'proxd', 1, 2, 0, 1, 0, 0, 10, 3, 3e-4)
# MC -> HC
gennetwork.tmgsynConnection(self.populations[1], self.populations[3],
20, 'midd', 2, 6.2, 0, 1, 0, 0, 10, 3,
2e-4)
# BC -> GC
# # synapses x3; Weight *1/4; tau from 5.5 to 20 (Hefft & Jonas, 2005)
gennetwork.tmgsynConnection(self.populations[2], self.populations[0],
560, 'soma', 400, 20, 0, 1, 0, -70, 10,
0.85, bc_gc)
# We reseed here to make sure that those connections are consistent
# between this and net_global which has a global target pool for
# BC->GC.
if seed:
self.set_numpy_seed(seed + 1)
# BC -> MC
gennetwork.tmgsynConnection(self.populations[2], self.populations[1],
28, 'proxd', 3, 3.3, 0, 1, 0, -70, 10, 1.5,
1.5e-3)
# BC -> BC
gennetwork.tmgsynConnection(self.populations[2], self.populations[2],
12, 'proxd', 2, 1.8, 0, 1, 0, -70, 10, 0.8,
7.6e-3)
# HC -> GC
# Weight x10; Nr synapses x4; tau from 6 to 20 (Hefft & Jonas, 2005)
gennetwork.tmgsynConnection(self.populations[3], self.populations[0],
2000, 'dd', 640, 20, 0, 1, 0, -70, 10, 3.8,
hc_gc)
# HC -> MC
gennetwork.tmgsynConnection(self.populations[3], self.populations[1],
60, ['mid1d', 'mid2d'], 4, 6, 0, 1, 0, -70,
10, 1, 1.5e-3)
# HC -> BC
gennetwork.tmgsynConnection(self.populations[3], self.populations[2],
24, 'ddend', 4, 5.8, 0, 1, 0, -70, 10, 1.6,
5e-4)