inhibitory = []
noise = []
downstream = []
source_a = Current('source', 0, 'current', 20.7)
source_b = Current('source', 1, 'current', 21.5)
for i in range(99):
neuron_producing = Neuron('excitatory', i, ex_settings, 'off')
noise_pos = Noise('noise', i, 100, noise_intensy, 3.0)
noise_neg = Noise('noise', i, 100, -noise_intensy, 3.0)
noise.append(noise_pos)
noise.append(noise_neg)
noise_pos.connect(neuron_producing)
noise_neg.connect(neuron_producing)
if random.random() < 0.5:
excitatory_a.append(neuron_producing)
source_a.connect(neuron_producing)
else:
excitatory_b.append(neuron_producing)
source_b.connect(neuron_producing)
if random.random() < 0.5:
observer_a.append(neuron_producing)
else:
observer_b.append(neuron_producing)
#for i in range(99):
#neuron_producing = Neuron('downstream', i, ds_settings, 'off')
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)
for i in range(4000):
for neuron in set:
event = Event(name = 'update')
simpy.activate(event, event.update(neuron), delay = i)
simpy.simulate(until = 4000.0)
print(len(neuron_a.spikes_record), len(neuron_b.spikes_record))
if inh=='on':
outfile_a = open('inhib_a.txt', 'w')
else:
