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,