def feedback(connect_dict, fb_npas1, fb_lhx6):
print('***** ADDING FEEDBACK CONNECTIONS')
if fb_npas1 > 0:
connect_dict['D2'] = {'gaba2': {}}
connect_dict['D2']['gaba2']['Npas'] = connect(synapse='gaba2',
pre='Npas',
post='D2',
probability=1,
weight=fb_npas1)
connect_dict['D1'] = {'gaba2': {}}
connect_dict['D1']['gaba2']['Npas'] = connect(synapse='gaba2',
pre='Npas',
post='D1',
probability=1,
weight=fb_npas1 * 0.67)
if fb_lhx6 > 0:
connect_dict['FSI'] = {'gaba': {}}
connect_dict['FSI']['gaba']['Lhx6'] = connect(synapse='gaba',
pre='Lhx6',
post='FSI',
probability=1,
weight=fb_lhx6)
return connect_dict
#test effect of FSI input
conn_delete['FSI'] = {'gaba': ['FSI']}
if FSI_in[1] == '0':
conn_delete['D1'] = {'gaba2': ['FSI']}
conn_delete['D2'] = {'gaba2': ['FSI']}
return conn_delete
connect_dict = {}
##### Note that number of inputs = probability * number of presyn neurons. Thus,
## if increase presyn neurons, will increase inputs
# **************** add in proximal_distr or distal_distr when using multicompartmental neurons
connect_dict = {'ep': {'gaba': {}}}
connect_dict['ep']['gaba']['proto'] = connect(synapse='gaba',
pre='proto',
post='ep',
probability=0.3,
weight=1.0)
connect_dict['ep']['gaba']['Lhx6'] = connect(synapse='gaba',
pre='Lhx6',
post='ep',
probability=0.5,
weight=1.0)
connect_dict['ep']['gaba']['D1'] = connect(synapse='gaba',
pre='D1',
post='ep',
probability=D1_to_ep,
weight=1.4)
#Inputs from striatum to GPe
#Input resistance. Npas: 360 MOhm, proto: 280 Mohm, Lhx6: 300 Mohm
'NaF': 0.0152,
'NaS': 0.0654,
'BKCa': 0.025,
'SKCa': 0.1024,
}
chanvar = {'proto': chanSTD_proto, 'Npas': chanSTD_arky, 'Lhx6': chanSTD_arky}
####################### Connections
#tables of extrinsic inputs
#first string is name of the table in moose, and 2nd string is name of external file
tt_STN = TableSet('tt_STN', 'gp_net/STN_lognorm', syn_per_tt=3)
#description of intrinsic inputs
ConnSpaceConst = 300e-6
neur1pre_neur1post = connect(
synapse='gaba', pre='proto', post='proto', space_const=ConnSpaceConst
) #internal post syn fraction in 10% Shink Smith 1995
neur1pre_neur2post = connect(synapse='gaba',
pre='proto',
post='Npas',
space_const=ConnSpaceConst)
neur1pre_neur3post = connect(synapse='gaba',
pre='proto',
post='Lhx6',
space_const=ConnSpaceConst)
neur2pre_neur1post = connect(synapse='gaba',
pre='Npas',
post='proto',
space_const=ConnSpaceConst)
neur2pre_neur2post = connect(synapse='gaba',
pre='Npas',
#postsyn_fraction, when summed over all external time tables to a neuron type should <= 1
#to reduce number of inputs, can reduce postsyn_fraction
distr = dend_location(mindist=0e-6, maxdist=400e-6,
postsyn_fraction=1) #,half_dist=50e-6,steep=1)
FSI_distr = dend_location(mindist=0e-6, maxdist=80e-6, postsyn_fraction=1)
MSNconnSpaceConst = 100e-6 #Czubakyko & Plenz PNAS: 37% connected at 10 um distance
FSIconnSpaceConst = 700e-6
#connectins between network neurons (intrinsic connections)
#number of connections is controled by space constant, or probability
#thus, as network size increases, may run out of post-synaptic neurons for connections
#can either change space constant, grid spacing, or increase NumSyn
D1pre_D1post = connect(synapse='gaba',
pre='D1',
post='D1',
num_conns=1,
space_const=MSNconnSpaceConst)
D1pre_D2post = connect(synapse='gaba',
pre='D1',
post='D2',
num_conns=1,
space_const=MSNconnSpaceConst)
D2pre_D1post = connect(synapse='gaba',
pre='D2',
post='D1',
num_conns=1,
space_const=MSNconnSpaceConst)
D2pre_D2post = connect(synapse='gaba',
pre='D2',
post='D2',
#tt_str2 = TableSet('tt_str2', 'ep_net/str_InhomPoisson_freq4.0_osc5.0',syn_per_tt=2)
tt_str2 = TableSet('tt_str2',
'ep_net/str_InhomPoisson_freq4.0_osc5.0_theta5.0',
syn_per_tt=2)
#tt_GPe = TableSet('tt_GPe', 'ep_net/GPe_InhomPoisson',syn_per_tt=2)
tt_GPe = TableSet('tt_GPe', 'ep_net/GPe_lognorm', syn_per_tt=2)
#description of intrinsic inputs
ConnSpaceConst = 125e-6
ep_distr = dend_location(mindist=30e-6,
maxdist=100e-6,
postsyn_fraction=1,
half_dist=50e-6,
steep=1)
neur1pre_neur1post = connect(
synapse='gaba', pre='ep', post='gaba', probability=0.5,
dend_loc=ep_distr) #need reference for no internal connections
#description of synapse and dendritic location of extrinsic inputs
GPe_distr = dend_location(mindist=0, maxdist=60e-6, half_dist=30e-6, steep=-1)
if TWO_STR_INPUTS:
str_distr = dend_location(mindist=30e-6,
maxdist=1000e-6,
postsyn_fraction=0.5,
half_dist=100e-6,
steep=1)
else:
str_distr = dend_location(mindist=30e-6,
maxdist=1000e-6,
postsyn_fraction=1.0,
half_dist=100e-6,