def csa_example():
cs = csa.cset(csa.random(0.1), 10000.0, 1.0)
pop1 = nest.LayoutNetwork("iaf_neuron", [16])
pop2 = nest.LayoutNetwork("iaf_neuron", [16])
nest.PrintNetwork(10)
nest.CGConnect(pop1, pop2, cs, {"weight": 0, "delay": 1})
pg = nest.Create("poisson_generator", params={"rate": 80000.0})
nest.DivergentConnect(pg, nest.GetLeaves(pop1)[0], 1.2, 1.0)
vm_params = {
"record_to": ["memory"],
"withgid": True,
"withtime": True,
"interval": 0.1
}
vm = nest.Create("voltmeter", params=vm_params)
nest.DivergentConnect(vm, nest.GetLeaves(pop2)[0])
nest.Simulate(50.0)
from nest import visualization
allnodes = pg + nest.GetLeaves(pop1)[0] + nest.GetLeaves(pop2)[0] + vm
visualization.plot_network(allnodes, "test_csa.png")
if havePIL:
im = Image.open("test_csa.png")
im.show()
from nest import voltage_trace
voltage_trace.from_device(vm)
def simulate():
global startsimulate, endsimulate, SAVE_PATH
SAVE_PATH = "output-{0}/".format(NEURONS)
print os.getcwd()
if not os.path.exists("../results/"):
os.mkdir("../results/")
if not os.path.exists(SAVE_PATH):
os.mkdir(SAVE_PATH)
begin = 0
logger.debug('* * * Simulating')
nest.PrintNetwork()
startsimulate = datetime.datetime.now()
for t in np.arange(0, T, dt):
print "SIMULATING [{0}, {1}]".format(t, t + dt)
nest.Simulate(dt)
#print nest.GetConnections( (Cortex[L5][Glu][10][0], ) , Cortex[L6][GABA][10], synapse_model="stdp_synapse" )
for data in sorted(
nest.GetStatus(nest.GetConnections(
(Cortex[L5][Glu][10][0], )))):
print "from {0} to {1} weight {2} | delay {3} | tau_plus {4}".format(
data['source'], data['target'], data['weight'], data['delay'],
data['tau_plus'])
#
end = clock()
times.append("{0:10.1f} {1:8.1f} {2:10.1f} {3:4.1f} {4}\n".format(
begin, end - begin, end, t,
datetime.datetime.now().time()))
begin = end
print "COMPLETED {0}%\n".format(t / dt)
endsimulate = datetime.datetime.now()
logger.debug('* * * Simulation completed successfully')
def simulate():
global startsimulate, endsimulate, SAVE_PATH
nest.PrintNetwork()
logger.debug('* * * Simulating')
startsimulate = datetime.datetime.now()
# For example set 10ms. It's mean that we will start read data from 10ms of simulation
last_spike_time = 10
for t in np.arange(0, T, dt):
print "SIMULATING [{0}, {1}]".format(t, t + dt)
# if interval ended (by the last spike time)
if last_spike_time <= t:
# try to find data file
if os.path.isfile(filename):
# open file an read all intervals
with open(filename, 'r') as f:
# write with some coefficient
intervals = [
float(time) / 100 for time in f.readline().split(" ")
]
# transfer to spike times
intervals[:1] = t, intervals[0] + t
for i in xrange(2, len(intervals)):
intervals[i] += intervals[i - 1]
# set new last spike time
print intervals
last_spike_time = intervals[-1]
# create generator with new intervals
generator = nest.Create('spike_generator', 1)
nest.SetStatus(
generator, {
'spike_times': intervals,
'spike_weights': [100. for i in intervals]
})
# connect to the neuron
nest.Connect(generator, (Cortex[L4][L4_Glu0][column][0], ))
# delete unwanted data
del intervals
# mark file as opened
os.rename(filename, filename + "_done")
nest.Simulate(dt)
print "COMPLETED {0:.2f}%\n".format(t / T * 100)
endsimulate = datetime.datetime.now()
logger.debug('* * * Simulation completed successfully')
def simulate():
global startsimulate, endsimulate
begin = 0
nest.PrintNetwork()
logger.debug('* * * Simulating')
startsimulate = datetime.datetime.now()
for t in np.arange(0, T, dt):
print "SIMULATING [{0}, {1}]".format(t, t + dt)
nest.Simulate(dt)
end = clock()
times.append("{0:10.1f} {1:8.1f} {2:10.1f} {3:4.1f} {4}\n".format(begin, end - begin, end,
t, datetime.datetime.now().time()))
begin = end
print "COMPLETED {0}%\n".format(t/dt)
endsimulate = datetime.datetime.now()
logger.debug('* * * Simulation completed successfully')
def csa_example():
cs = csa.cset(csa.random(0.1), 10000.0, 1.0)
# This does not work yet, as the implementation does not yet
# support nested subnets.
#
#pop1 = nest.LayoutNetwork("iaf_neuron", [4,4])
#pop2 = nest.LayoutNetwork("iaf_neuron", [4,4])
pop1 = nest.LayoutNetwork("iaf_neuron", [16])
pop2 = nest.LayoutNetwork("iaf_neuron", [16])
nest.PrintNetwork(10)
nest.CGConnect(pop1, pop2, cs, {"weight": 0, "delay": 1})
pg = nest.Create("poisson_generator", params={"rate": 80000.0})
nest.DivergentConnect(pg, nest.GetLeaves(pop1)[0], 1.2, 1.0)
vm = nest.Create("voltmeter",
params={
"record_to": ["memory"],
"withgid": True,
"withtime": True,
"interval": 0.1
})
nest.DivergentConnect(vm, nest.GetLeaves(pop2)[0])
nest.Simulate(50.0)
# Not yet supported in NEST
#from nest import visualization
#allnodes = [pg, nest.GetLeaves(pop1)[0], nest.GetLeaves(pop2)[0], vm]
#visualization.plot_network(allnodes, "test_csa.png", "png", plot_modelnames=True
#if havePIL:
# im = Image.open("test_csa.png")
# im.show()
from nest import voltage_trace
voltage_trace.from_device(vm)
def simulate():
global startsimulate, endsimulate, SAVE_PATH
SAVE_PATH = "results/output-{0}/".format(NEURONS)
if not os.path.exists(SAVE_PATH):
os.mkdir(SAVE_PATH)
begin = 0
logger.debug('* * * Simulating')
nest.PrintNetwork()
startsimulate = datetime.datetime.now()
for t in np.arange(0, T, dt):
print "SIMULATING [{0}, {1}]".format(t, t + dt)
nest.Simulate(dt)
end = clock()
times.append("{0:10.1f} {1:8.1f} {2:10.1f} {3:4.1f} {4}\n".format(begin, end - begin, end,
t, datetime.datetime.now().time()))
begin = end
print "COMPLETED {0}%\n".format(t/dt)
endsimulate = datetime.datetime.now()
logger.debug('* * * Simulation completed successfully')
})
Vp_h = topo.CreateLayer(layerProps)
Vp_v = topo.CreateLayer(layerProps)
#! Collect all populations
#! -----------------------
#! For reference purposes, e.g., printing, we collect all populations
#! in a tuple:
populations = (retina, Tp, Rp, Vp_h, Vp_v)
#! Inspection
#! ----------
#! We can now look at the network using `PrintNetwork`:
nest.PrintNetwork()
#! We can also try to plot a single layer in a network. For
#! simplicity, we use Rp, which has only a single neuron per position.
topo.PlotLayer(Rp)
pylab.title('Layer Rp')
pylab.show()
#! Synapse models
#! ==============
#! Actual synapse dynamics, e.g., properties such as the synaptic time
#! course, time constants, reversal potentials, are properties of
#! neuron models in NEST and we set them in section `Neuron models`_
#! above. When we refer to *synapse models* in NEST, we actually mean
#! connectors which store information about connection weights and
# For bbox_extra_artists, see
# https://github.com/matplotlib/matplotlib/issues/351
plt.savefig('../user_manual_figures/layer1.png',
bbox_inches='tight',
bbox_extra_artists=tx)
print("#{ layer1s.log #}")
# { layer1s #}
print(nest.GetStatus(l)[0]['topology'])
# { end #}
print("#{ end.log #}")
print("#{ layer1p.log #}")
# { layer1p #}
nest.PrintNetwork(depth=2)
# { end #}
print("#{ end.log #}")
# --------------------------------------------------
nest.ResetKernel()
# { layer2 #}
l = tp.CreateLayer({
'rows': 5,
'columns': 5,
'extent': [2.0, 0.5],
'elements': 'iaf_neuron'
})
# { end #}
import nest
import pylab
import nest.topology as topo
pylab.ion()
nest.ResetKernel()
l1 = topo.CreateLayer({
'columns': 4,
'rows': 3,
'extent': [2.0, 1.5],
'elements': 'iaf_neuron'
})
nest.PrintNetwork()
nest.PrintNetwork(2)
nest.PrintNetwork(2, l1)
topo.PlotLayer(l1, nodesize=50)
# beautify
pylab.axis([-1.0, 1.0, -0.75, 0.75])
pylab.axes().set_aspect('equal', 'box')
pylab.axes().set_xticks((-0.75, -0.25, 0.25, 0.75))
pylab.axes().set_yticks((-0.5, 0, 0.5))
pylab.grid(True)
pylab.xlabel('4 Columns, Extent: 1.5')
pylab.ylabel('2 Rows, Extent: 1.0')
# pylab.savefig('grid_iaf.png')
"positions": poss,
"elements": "iaf_psc_alpha",
"extent": [1.1, 1.1]
}
my_layer_dict_on_grid = {
"rows": 11,
"columns": 11,
"extent": [11.0, 11.0],
"elements": 'iaf_psc_alpha'
}
# connectivity specifications with a mask
conndict = {
'connection_type': 'divergent',
'mask': {
'rectangular': {
'lower_left': [-2.0, -1.0],
'upper_right': [2.0, 1.0]
}
}
}
my_layer = topp.CreateLayer(my_layer_dict_on_grid)
topp.PlotLayer(my_layer)
topp.ConnectLayers(my_layer, my_layer, conndict)
nest.PrintNetwork(depth=1)
topp.PlotTargets([5], my_layer)
plt.show()
import random
pylab.ion()
nest.ResetKernel()
nest.CopyModel('iaf_psc_alpha', 'pyr')
nest.CopyModel('iaf_psc_alpha', 'in')
ctx = topo.CreateLayer({
'columns': 4,
'rows': 3,
'extent': [2.0, 1.5],
'elements': ['pyr', 'in']
})
nest.PrintNetwork()
nest.PrintNetwork(2)
nest.PrintNetwork(2, ctx)
# ctx_leaves is a work-around until NEST 3.0 is released
ctx_leaves = nest.GetLeaves(ctx)[0]
# extract position information
ppyr = pylab.array(
tuple(
zip(*[
topo.GetPosition([n])[0] for n in ctx_leaves
if nest.GetStatus([n], 'model')[0] == 'pyr'
])))
