#Layer 1: Antennal Lobe (AL) [first stage in separation]
glo_para = dict(num_pn=6, num_ln=2, # flies: 3 and 30
PNClass=nm.PN_2, LNClass=nm.LN,
PNSynapseClass=nm.Synapse_nAch_PN_2, LNSynapseClass=nm.Synapse_gaba_LN_with_slow)
al_prob_para = dict(prob_l2p = 0.4, prob_l2l=0.4)
al_cond_para = dict(gLN = 110, gPN = -1.0, gLNPN=400, gPNLN=400)
al_para = dict(num_glo=45, glo_para=glo_para,al_prob_para=al_prob_para, al_cond_para=al_cond_para) # flies: 54
AL = net.get_antennal_lobe(**al_para)
folder_prefix = 'results/'
# net.draw_colored_layered_digraph(AL)
num_layers = len(AL.layers)
Ibase = 1000 # nA
p = 0.33 # probability of injecting
#run for specified time with dt
time_len = 300.0
dt = 0.02
time_sampled_range = np.arange(0., time_len, dt)
num_odors = int(input('Enter number of odours: '))
# networks.draw_colored_layered_digraph(rn)
#Layer 1: Antennal Lobe (AL) [first stage in separation]
glo_para = dict(
num_pn=3,
num_ln=30, # flies: 3 and 30
PNClass=ExcitatoryNeuron,
LNClass=InhibitoryNeuron,
PNSynapseClass=ExcitatorySynapse,
LNSynapseClass=RandomSynapse)
# glo = networks.get_glomeruli(**glo_para)
# networks.draw_colored_layered_digraph(glo)
al_para = dict(num_glo=54, glo_para=glo_para) # flies: 54
al = networks.get_antennal_lobe(**al_para)
#networks.draw_colored_layered_digraph(al)
#Layer 2: Mushroom Body (MB) [second stage in separation]
mb_para = dict(
num_kc=2500, # flies: 2500
KCClass=ExcitatoryNeuron,
GGNClass=InhibitoryNeuron,
KCSynapseClass=ExcitatorySynapse,
GGNSynapseClass=ExcitatorySynapse)
# mb = networks.get_mushroom_body(**mb_para)
# networks.draw_colored_layered_digraph(mb)
#Layer 3: Beta-lobe (BL) [read-out]
bl_para = dict(
num_bl=34, #flies: 34