def createNetwork():
# the dimesion is X x Y x maxDisparity+1 because disparity 0 means no shift in pixel location
# however the network should still exist and contain only one disparity map.
assert dimensionRetinaX > maxDisparity >= 0, "Maximum Disparity Constant is illegal!"
print "Creating Cooperative Network..."
print "\t Creating Populations..."
from SimulationAndNetworkSettings import t_synE, t_synI, t_memb, vResetInh, vResetCO
from pyNN.nest import record
network = []
for x in range(0, dimensionRetinaX):
for disp in range(0, maxDisparity+1):
inhLeftPop = Population(dimensionRetinaY, IF_curr_exp, {'tau_syn_E':t_synE, 'tau_syn_I':t_synI, 'tau_m':t_memb, 'v_reset':vResetInh},
label="Blocker Left {0}".format(x*dimensionRetinaX + disp))
inhRightPop = Population(dimensionRetinaY, IF_curr_exp, {'tau_syn_E':t_synE, 'tau_syn_I':t_synI, 'tau_m':t_memb, 'v_reset':vResetInh},
label="Blocker Right {0}".format(x*dimensionRetinaX + disp))
cellOutputPop = Population(dimensionRetinaY, IF_curr_exp, {'tau_syn_E':t_synE, 'tau_syn_I':t_synI, 'tau_m':t_memb, 'v_reset':vResetCO},
label="Cell Output {0}".format(x*dimensionRetinaX + disp))
# reocrd data for plotting purposes
cellOutputPop.record('v')
network.append((inhLeftPop, inhRightPop, cellOutputPop))
interconnectNetworkNeurons(network)
return network
def main(args):
setup(timestep=0.1)
random_image = np.random.rand(2,2)
size = random_image.size
input_population_arr = Population(random_image.size, SpikeSourceArray, {'spike_times': [0 for i in range(0, random_image.size)]})
cell_params = {'tau_refrac': 2.0, 'v_thresh': -50.0, 'tau_syn_E': 2.0, 'tau_syn_I': 2.0}
output_population = Population(1, IF_curr_alpha, cell_params, label="output")
projection = Projection(input_population_arr, output_population, AllToAllConnector())
projection.setWeights(1.0)
input_population_arr.record('spikes')
output_population.record('spikes')
tstop = 1000.0
run(tstop)
output_population.write_data("simpleNetwork_output.pkl",'spikes')
input_population_arr.write_data("simpleNetwork_input.pkl",'spikes')
#output_population.print_v("simpleNetwork.v")
end()
image = cv2.imread('/fzi/ids/mlprak2/Bilder/impuls.tif')
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
frame = np.zeros(image.size) + 255
rates_init = np.zeros(frame.size)
pop_in = Population(frame.shape, SpikeSourcePoisson, {'rate': rates_init})
pop_out = Population(1, IF_curr_alpha, {'tau_refrac': 5 })
projection = Projection(pop_in, pop_out, AllToAllConnector())
projection.setWeights(1.0)
pop_in.set(rate=image.astype(float).flatten())
pop_in.record('spikes')
pop_out.record('spikes')
tstop = 100.0
run(tstop)
spikes_in = pop_in.get_data()
data_out = pop_out.get_data()
for seg in data_out.segments:
print seg
for st in seg.spiketrains:
print st
for seg in spikes_in.segments:
