else:
observer_b.append(neuron_producing)
#for i in range(99):
#neuron_producing = Neuron('downstream', i, ds_settings, 'off')
#downstream.append(neuron_producing)
#for observee in random.sample(excitatory_a+excitatory_b, 20):
#observee.connect(neuron_producing)
for i in range(801):
neuron_producing = Neuron('inhibitory', i, in_settings, 'off')
if inhi == 'on':
inhibitory.append(neuron_producing)
for inhibitee in random.sample(excitatory_a+excitatory_b, 20):
inhibitee.connect(neuron_producing)
neuron_producing.connect(inhibitee)
all_neuron = excitatory_a + excitatory_b + inhibitory + downstream + noise
duration = 2400
for i in range(duration):
for neuron in all_neuron:
event = Event(name = 'update')
simpy.activate(event, event.update(neuron), delay = i)
print("simulation scheduled.")
simpy.simulate(until = duration+0.0)
print("simulation done.")
current_a = Current('current', 0, 'current', 20.7)
current_b = Current('current', 1, 'current', 21.5)
neuron_a = Neuron('neuron', 0, settings, 'off')
neuron_b = Neuron('neuron', 1, settings, 'off')
noise_a_pos = Noise('noise', 0, 100, noise_intensy, 3.0)
noise_a_neg = Noise('noise', 1, 100, -noise_intensy, 3.0)
noise_b_pos = Noise('noise', 2, 100, noise_intensy, 3.0)
noise_b_neg = Noise('noise', 3, 100, -noise_intensy, 3.0)
set = [pool_a, pool_b, shared_pool, current_a, current_b, neuron_a, neuron_b, noise_a_pos, noise_a_neg, noise_b_pos, noise_b_neg]
inh = 'off'
#inh = 'on'
if inh=='on':
pool_a.connect(neuron_a)
neuron_a.connect(pool_a)
pool_b.connect(neuron_b)
neuron_b.connect(pool_b)
shared_pool.connect(neuron_a)
shared_pool.connect(neuron_b)
neuron_a.connect(shared_pool)
neuron_b.connect(shared_pool)
current_a.connect(neuron_a)
current_b.connect(neuron_b)
noise_a_pos.connect(neuron_a)
noise_a_neg.connect(neuron_a)
noise_b_pos.connect(neuron_b)
noise_b_neg.connect(neuron_b)
