def run_sim(ncell):
print "Cells: ", ncell
setup0 = time.time()
sim.setup(timestep=0.1)
hh_cell_type = sim.HH_cond_exp()
hh = sim.Population(ncell, hh_cell_type)
pulse = sim.DCSource(amplitude=0.5, start=20.0, stop=80.0)
pulse.inject_into(hh)
hh.record('v')
setup1 = time.time()
t0 = time.time()
sim.run(100.0)
v = hh.get_data()
sim.end()
t1 = time.time()
setup_total = setup1 - setup0
run_total = t1 - t0
print "Setup: ", setup_total
print "Run: ", run_total
print "Total sim time: ", setup_total + run_total
return run_total
import pyNN.nest as simulator
import pyNN.nest.standardmodels.electrodes as elect
N = 1 # Number of neurons
t = 100.0 #Simulation time
# Has to be called at the beginning of the simulation
simulator.setup(timestep=0.1, min_delay=0.1, max_delay=10)
model = simulator.IF_curr_exp()
neurons = simulator.Population(N, model)
# DC source
current = simulator.DCSource(amplitude=0.5, start=20.0, stop=80.0)
#current = elect.DCSource(amplitude=0.5, start=20.0, stop=80.0)
current.inject_into(neurons)
#neurons.inject(current)
# Record the voltage
neurons.record('v')
simulator.run(t) # Run the simulations for t ms
simulator.end()
# Extracts the data
data = neurons.get_data() # Crates a Neo Block
segment = data.segments[0] # Takes the first
vm = segment.analogsignalarrays[0]
cellparams=neuron_parameters_FS_cell)
#V value initialization
sim.initialize(Neurons_RS, v=-65.0) # v:mV
sim.initialize(Neurons_FS, v=-65.0) # v:mV
#parameters recorded : membrane potential & spikes
Neurons_RS.record('v')
Neurons_RS.record('spikes')
Neurons_FS.record('v')
Neurons_FS.record('spikes')
#The external input
pulse = sim.DCSource(
amplitude=0.2, start=100,
stop=400) #amplitude=200pA, start=100ms and stop=400ms
#representaton of the pulse injected
x = [0, 50, 99, 100, 200, 300, 399, 400, 600]
y = [0, 0, 0, 200, 200, 200, 200, 0, 0]
fig = plt.figure()
plt.plot(x, y)
plt.xlabel("time(ms)")
plt.ylabel("I(pA)")
plt.ylim((0, 210))
plt.title('DC current source')
plt.setp(plt.gca().get_xticklabels(), visible=True)
fig.savefig('input.png')
#Adding the external input to the cells before running the simulation
}) for i in range(1)
]
for ifcell in ifcellscond:
ifcell.initialize(v=vrest)
####### simulate #######
spkte = array(
[50., 100., 103., 106., 109., 112., 115., 118., 121., 124., 127.]) + 150.
spkti = spkte + 150.
# Subtract dt to guarantee exact onset of PSPs
spkte -= dt
spkti -= dt
pulse = sim.DCSource(amplitude=0.2, start=50., stop=150.)
spikesource1E = sim.create(sim.SpikeSourceArray, {'spike_times': spkte})
spikesource1I = sim.create(sim.SpikeSourceArray, {'spike_times': spkti})
pulse.inject_into(ifcellscond[0])
sim.connect(spikesource1E,
ifcellscond[0],
weight=wconde,
receptor_type='excitatory',
delay=0.1)
sim.connect(spikesource1I,
ifcellscond[0],
weight=wcondi,
receptor_type='inhibitory',
delay=0.1)
sim.Projection(LGNBrightInput, LGNBright, sim.OneToOneConnector(),
sim.StaticSynapse(weight=connections['brightInputToLGN']))
sim.Projection(LGNDarkInput, LGNDark, sim.OneToOneConnector(),
sim.StaticSynapse(weight=connections['darkInputToLGN']))
if numSegmentationLayers > 1:
if useSurfaceSegmentation:
SurfaceSegmentationOff = network.get_population(
"SurfaceSegmentationOff")
SurfaceSegmentationOn = network.get_population("SurfaceSegmentationOn")
if useBoundarySegmentation:
BoundarySegmentationOff = network.get_population(
"BoundarySegmentationOff")
BoundarySegmentationOn = network.get_population(
"BoundarySegmentationOn")
network.get_population("BoundarySegmentationOnInter3").inject(
sim.DCSource(amplitude=constantInput, start=0.0, stop=simTime))
########################################################################################
### Network is defined, now set up stimulus, segmentation signal and run everything! ###
########################################################################################
# Stuff to plot activity of LGN, V1, V2, etc. layers
cumplotDensityLGNBright = [[0 for j in range(ImageNumPixelColumns)]
for i in range(ImageNumPixelRows)]
cumplotDensityLGNDark = [[0 for j in range(ImageNumPixelColumns)]
for i in range(ImageNumPixelRows)]
cumplotDensityOrientationV2 = [[[[0 for j in range(numPixelColumns)]
for i in range(numPixelRows)]
for h in range(numSegmentationLayers)]
for k in range(numOrientations)]
cumplotDensityOrientationV1 = [[[0 for j in range(numPixelColumns)]