plt.axvline(t, color='green', linewidth=1.)
max_v = np.max(segment.filter(name='v')[0])
ax0.plot(segment.spiketrains[0],
0.99 * max_v * np.ones_like(segment.spiketrains[0]), '.b')
ax0.plot(segment.filter(name='v')[0], color='blue')
timestep = 1.
runtime = 1000.
sim.setup(timestep)
n_neurons = 1
preExc = sim.Population(n_neurons,
sim.SpikeSourcePoisson(rate=100),
label='input')
preExc.record('spikes')
preInh = sim.Population(n_neurons,
sim.SpikeSourcePoisson(rate=100),
label='input')
preInh.record('spikes')
parameters = {
'v_rest': -65.0, # Resting membrane potential in mV.
'cm': 1.0, # Capacity of the membrane in nF
'tau_m': 20.0, # Membrane time constant in ms.
'tau_refrac': 0.1, # Duration of refractory period in ms.
'tau_syn_E': 3.0, # Decay time of excitatory synaptic current in ms.
'tau_syn_I': 5.0, # Decay time of inhibitory synaptic current in ms.
else:
# create separate populations for excitatory and inhibitory neurons
exc_cells = sim.Population(n_exc,
celltype(**cell_params),
label="Excitatory_Cells")
inh_cells = sim.Population(n_inh,
celltype(**cell_params),
label="Inhibitory_Cells")
if use_assembly:
# group the populations into an assembly
all_cells = exc_cells + inh_cells
if benchmark == "COBA":
ext_stim = sim.Population(20,
sim.SpikeSourcePoisson(rate=rate,
duration=stim_dur),
label="expoisson")
rconn = 0.01
ext_conn = sim.FixedProbabilityConnector(rconn)
ext_syn = sim.StaticSynapse(weight=0.1)
print("%s Initialising membrane potential to random values..." % node_id)
uniformDistr = RandomDistribution('uniform',
low=v_reset,
high=v_thresh,
rng=rng)
if use_views:
all_cells.initialize(v=uniformDistr)
else:
exc_cells.initialize(v=uniformDistr)
'.',
color=COLORS[nid])
volts = segment.filter(name='v')[0]
for nid, vs in enumerate(volts.T):
# print(volts.shape)
ax0.plot(vs, color=COLORS[nid])
timestep = 0.1
runtime = 200.
sim.setup(timestep)
n_neurons = 3
preExc = sim.Population(n_neurons,
sim.SpikeSourcePoisson(rate=200),
label='input')
preExc.record('spikes')
parameters = {
'v_rest': -65.0, # Resting membrane potential in mV.
'cm': 1.0, # Capacity of the membrane in nF
'tau_m': 20.0, # Membrane time constant in ms.
'tau_refrac': 0.1, # Duration of refractory period in ms.
'tau_syn_E': 3.0, # Decay time of excitatory synaptic current in ms.
'tau_syn_I': 5.0, # Decay time of inhibitory synaptic current in ms.
'tau_syn_S': 5.0,
'i_offset': 0.0, # Offset current in nA
'v_reset': -65.0, # Reset potential after a spike in mV.
'v_threshold': -50.0, # Spike threshold in mV. STATIC, MIN
'i': 0.0, # nA total input current
# Take the neuron layers that need to be updated online or that we want to take records of
LGNBright = network.get_population("LGNBright")
LGNDark = network.get_population("LGNDark")
V1 = network.get_population("V1LayerToPlot")
V2 = network.get_population("V2LayerToPlot")
V4Bright = network.get_population("V4Brightness")
V4Dark = network.get_population("V4Darkness")
LGNBright.record("spikes")
LGNDark.record("spikes")
V1.record("spikes")
V2.record("spikes")
V4Bright.record("spikes")
V4Dark.record("spikes")
if inputPoisson:
LGNBrightInput = sim.Population(ImageNumPixelRows * ImageNumPixelColumns,
sim.SpikeSourcePoisson())
LGNDarkInput = sim.Population(ImageNumPixelRows * ImageNumPixelColumns,
sim.SpikeSourcePoisson())
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(
'tau_syn_E': 1.0, # ms
'tau_syn_I': 5.0, # ms
}
timestep = 0.1
max_w = 0.01
start_w = max_w / 2.0
min_delay = 0.1
sim.setup(timestep,
min_delay=min_delay,
backend='CUDA'
# backend='SingleThreadedCPU'
)
pre = sim.Population(10, sim.SpikeSourcePoisson(**{'rate': 10}))
post = sim.Population(100, neuron_class(**base_params))
# w = 1.0
w = sim.RandomDistribution('normal', [5.0, 1.0])
synapse = sim.StaticSynapse(weight=w)
prj = sim.Projection(
pre,
post,
sim.FixedProbabilityConnector(0.25),
# prj = sim.Projection(pre, post, sim.AllToAllConnector(),
# prj = sim.Projection(pre, post, sim.OneToOneConnector(),
synapse_type=synapse,
receptor_type='excitatory')
sim.run(100)