def run_simulation(sim, params):
print "Running Network ..."
timer = Timer()
timer.reset()
sim.run(params['run_time'])
simCPUtime = timer.elapsedTime()
print "... The simulation took %s ms to run." % str(simCPUtime)
def main_pyNN(parameters):
timer = Timer()
sim = import_module(parameters.simulator)
timer.mark("import")
sim.setup(threads=parameters.threads)
timer.mark("setup")
populations = {}
for name, P in parameters.populations.parameters():
populations[name] = sim.Population(P.n,
getattr(sim,
P.celltype)(**P.params),
label=name)
timer.mark("build")
if parameters.projections:
projections = {}
for name, P in parameters.projections.parameters():
connector = getattr(sim, P.connector.type)(**P.connector.params)
synapse_type = getattr(
sim, P.synapse_type.type)(**P.synapse_type.params)
projections[name] = sim.Projection(populations[P.pre],
populations[P.post],
connector,
synapse_type,
receptor_type=P.receptor_type,
label=name)
timer.mark("connect")
if parameters.recording:
for pop_name, to_record in parameters.recording.parameters():
for var_name, n_record in to_record.items():
populations[pop_name].sample(n_record).record(var_name)
timer.mark("record")
sim.run(parameters.sim_time)
timer.mark("run")
spike_counts = {}
if parameters.recording:
for pop_name in parameters.recording.names():
block = populations[pop_name].get_data(
) # perhaps include some summary statistics in the data returned?
spike_counts["spikes_%s" %
pop_name] = populations[pop_name].mean_spike_count()
timer.mark("get_data")
mpi_rank = sim.rank()
num_processes = sim.num_processes()
sim.end()
data = dict(timer.marks)
data.update(num_processes=num_processes)
data.update(spike_counts)
return mpi_rank, data
def test_callback(data_input):
global message
message = data_input.actual.positions
msg_list = list(message)
#msg_list[0] = int(message[0].encode('hex'),16)
#for i in
#msg_list = int(message.encode('hex'),16)
#print('============= Received image data.',message)
rospy.loginfo('=====received data %r', msg_list[0])
timer = Timer()
dt = 0.1
p.setup(timestep=dt) # 0.1ms
pub = rospy.Publisher('/arm_controller/follow_joint_trajectory/goal',
FollowJointTrajectoryActionGoal,
queue_size=10)
command = FollowJointTrajectoryActionGoal()
command.header.stamp = rospy.Time.now()
command.goal.trajectory.joint_names = ['elbow']
point = JointTrajectoryPoint()
point.positions = [rate_command / 10]
point.time_from_start = rospy.Duration(1)
command.goal.trajectory.points.append(point)
pub.publish(command)
rospy.loginfo('=====send command %r', command.goal.trajectory.points[0])
print("now plotting the network---------------")
rospy.loginfo('--------now plotting---------------')
n_panels = sum(a.shape[1]
for a in pop_1_data.segments[0].analogsignalarrays) + 2
plt.subplot(n_panels, 1, 1)
plot_spiketrains(pop_1_data.segments[0])
panel = 3
for array in pop_1_data.segments[0].analogsignalarrays:
for i in range(array.shape[1]):
plt.subplot(n_panels, 1, panel)
plot_signal(array, i, colour='bg'[panel % 2])
panel += 1
plt.xlabel("time (%s)" % array.times.units._dimensionality.string)
plt.setp(plt.gca().get_xticklabels(), visible=True) #
def main_pynest(parameters):
P = parameters
assert P.sim_name == "pynest"
timer = Timer()
import nest
timer.mark("import")
nest.SetKernelStatus({"resolution": 0.1})
timer.mark("setup")
p = nest.Create("iaf_psc_alpha", n=P.n, params={"I_e": 1000.0})
timer.mark("build")
# todo: add recording and data retrieval
nest.Simulate(P.sim_time)
timer.mark("run")
mpi_rank = nest.Rank()
num_processes = nest.NumProcesses()
data = P.as_dict()
data.update(num_processes=num_processes, timings=timer.marks)
return mpi_rank, data
def run_model(sim, **options):
"""
Run a simulation using the parameters read from the file "I_f_curve.json"
:param sim: the PyNN backend module to be used.
:param options: should contain a keyword "simulator" which is the name of the PyNN backend module used.
:return: a tuple (`data`, `times`) where `data` is a Neo Block containing the recorded spikes
and `times` is a dict containing the time taken for different phases of the simulation.
"""
import json
from pyNN.utility import Timer
timer = Timer()
g = open("I_f_curve.json", 'r')
d = json.load(g)
N = d['param']['N']
max_current = d['param']['max_current']
tstop = d['param']['tstop']
if options['simulator'] == "hardware.brainscales":
hardware_preset = d['setup'].pop('hardware_preset', None)
if hardware_preset:
d['setup']['hardware'] = sim.hardwareSetup[hardware_preset]
timer.start()
sim.setup(**d['setup'])
popcell = sim.Population(N, sim.IF_cond_exp, d['IF_cond_exp'])
#current_source = []
#for i in xrange(N):
# current_source.append(sim.DCSource(amplitude=(max_current*(i+1)/N)))
# popcell[i:(i+1)].inject(current_source[i])
i_offset = max_current * (1 + np.arange(N))/N
popcell.tset("i_offset", i_offset)
if PYNN07:
popcell.record()
else:
popcell.record('spikes')
#popcell[0, 1, N-2, N-1].record('v') # debug
setup_time = timer.diff()
sim.run(tstop)
run_time = timer.diff()
if PYNN07:
spike_array = popcell.getSpikes()
data = spike_array_to_neo(spike_array, popcell, tstop)
else:
data = popcell.get_data()
sim.end()
closing_time = timer.diff()
times = {'setup_time': setup_time, 'run_time': run_time, 'closing_time': closing_time}
return data, times
def do_run(seed=None):
simulator_name = 'spiNNaker'
timer = Timer()
# === Define parameters =========================================
parallel_safe = True
n = 1500 # number of cells
# number of excitatory cells:number of inhibitory cells
r_ei = 4.0
pconn = 0.02 # connection probability
dt = 1 # (ms) simulation timestep
tstop = 200 # (ms) simulaton duration
delay = 1
# Cell parameters
area = 20000. # (µm²)
tau_m = 20. # (ms)
cm = 1. # (µF/cm²)
g_leak = 5e-5 # (S/cm²)
e_leak = -49. # (mV)
v_thresh = -50. # (mV)
v_reset = -60. # (mV)
t_refrac = 5. # (ms) (clamped at v_reset)
# (mV) 'mean' membrane potential, for calculating CUBA weights
v_mean = -60.
tau_exc = 5. # (ms)
tau_inh = 10. # (ms)
# (nS) #Those weights should be similar to the COBA weights
g_exc = 0.27
# (nS) # but the delpolarising drift should be taken into account
g_inh = 4.5
e_rev_exc = 0. # (mV)
e_rev_inh = -80. # (mV)
# === Calculate derived parameters ===============================
area *= 1e-8 # convert to cm²
cm *= area * 1000 # convert to nF
r_m = 1e-6 / (g_leak * area) # membrane resistance in MΩ
assert tau_m == cm * r_m # just to check
# number of excitatory cells
n_exc = int(round((n * r_ei / (1 + r_ei))))
n_inh = n - n_exc # number of inhibitory cells
celltype = p.IF_curr_exp
# (nA) weight of excitatory synapses
w_exc = 1e-3 * g_exc * (e_rev_exc - v_mean)
w_inh = 1e-3 * g_inh * (e_rev_inh - v_mean) # (nA)
assert w_exc > 0
assert w_inh < 0
# === Build the network ==========================================
p.setup(timestep=dt, min_delay=delay, max_delay=delay)
if simulator_name == 'spiNNaker':
# this will set 100 neurons per core
p.set_number_of_neurons_per_core(p.IF_curr_exp, 100)
# this will set 50 neurons per core
p.set_number_of_neurons_per_core(p.IF_cond_exp, 50)
# node_id = 1
# np = 1
# host_name = socket.gethostname()
cell_params = {'tau_m': tau_m, 'tau_syn_E': tau_exc, 'tau_syn_I': tau_inh,
'v_rest': e_leak, 'v_reset': v_reset, 'v_thresh': v_thresh,
'cm': cm, 'tau_refrac': t_refrac, 'i_offset': 0}
timer.start()
exc_cells = p.Population(n_exc, celltype, cell_params,
label="Excitatory_Cells")
inh_cells = p.Population(n_inh, celltype, cell_params,
label="Inhibitory_Cells")
rng = NumpyRNG(seed=seed, parallel_safe=parallel_safe)
uniform_distr = RandomDistribution('uniform', [v_reset, v_thresh], rng=rng)
exc_cells.initialize(v=uniform_distr)
inh_cells.initialize(v=uniform_distr)
exc_conn = p.FixedProbabilityConnector(pconn, rng=rng)
synapse_exc = p.StaticSynapse(weight=w_exc, delay=delay)
inh_conn = p.FixedProbabilityConnector(pconn, rng=rng)
synapse_inh = p.StaticSynapse(weight=w_inh, delay=delay)
connections = dict()
connections['e2e'] = p.Projection(exc_cells, exc_cells, exc_conn,
synapse_type=synapse_exc,
receptor_type='excitatory')
connections['e2i'] = p.Projection(exc_cells, inh_cells, exc_conn,
synapse_type=synapse_exc,
receptor_type='excitatory')
connections['i2e'] = p.Projection(inh_cells, exc_cells, inh_conn,
synapse_type=synapse_inh,
receptor_type='inhibitory')
connections['i2i'] = p.Projection(inh_cells, inh_cells, inh_conn,
synapse_type=synapse_inh,
receptor_type='inhibitory')
# === Setup recording ==============================
exc_cells.record("spikes")
# === Run simulation ================================
p.run(tstop)
exc_spikes = exc_cells.get_data("spikes")
exc_cells.write_data(neo_path, "spikes")
p.end()
return exc_spikes
def test_va_benchmark(self):
try:
simulator_name = 'spiNNaker'
timer = Timer()
# === Define parameters =========================================
rngseed = 98766987
parallel_safe = True
n = 1500 # number of cells
# number of excitatory cells:number of inhibitory cells
r_ei = 4.0
pconn = 0.02 # connection probability
dt = 0.1 # (ms) simulation timestep
tstop = 200 # (ms) simulaton duration
delay = 1
# Cell parameters
area = 20000. # (µm²)
tau_m = 20. # (ms)
cm = 1. # (µF/cm²)
g_leak = 5e-5 # (S/cm²)
e_leak = -49. # (mV)
v_thresh = -50. # (mV)
v_reset = -60. # (mV)
t_refrac = 5. # (ms) (clamped at v_reset)
# (mV) 'mean' membrane potential, for calculating CUBA weights
v_mean = -60.
tau_exc = 5. # (ms)
tau_inh = 10. # (ms)
# (nS) #Those weights should be similar to the COBA weights
g_exc = 0.27
# (nS) # but the delpolarising drift should be taken into account
g_inh = 4.5
e_rev_exc = 0. # (mV)
e_rev_inh = -80. # (mV)
# === Calculate derived parameters ===============================
area *= 1e-8 # convert to cm²
cm *= area * 1000 # convert to nF
r_m = 1e-6 / (g_leak * area) # membrane resistance in MΩ
assert tau_m == cm * r_m # just to check
# number of excitatory cells
n_exc = int(round((n * r_ei / (1 + r_ei))))
n_inh = n - n_exc # number of inhibitory cells
print n_exc, n_inh
celltype = p.IF_curr_exp
# (nA) weight of excitatory synapses
w_exc = 1e-3 * g_exc * (e_rev_exc - v_mean)
w_inh = 1e-3 * g_inh * (e_rev_inh - v_mean) # (nA)
assert w_exc > 0
assert w_inh < 0
# === Build the network ==========================================
p.setup(timestep=dt, min_delay=delay, max_delay=delay)
if simulator_name == 'spiNNaker':
# this will set 100 neurons per core
p.set_number_of_neurons_per_core('IF_curr_exp', 100)
# this will set 50 neurons per core
p.set_number_of_neurons_per_core('IF_cond_exp', 50)
node_id = 1
np = 1
host_name = socket.gethostname()
print "Host #%d is on %s" % (np, host_name)
cell_params = {
'tau_m': tau_m,
'tau_syn_E': tau_exc,
'tau_syn_I': tau_inh,
'v_rest': e_leak,
'v_reset': v_reset,
'v_thresh': v_thresh,
'cm': cm,
'tau_refrac': t_refrac,
'i_offset': 0
}
print cell_params
timer.start()
print "%s Creating cell populations..." % node_id
exc_cells = p.Population(n_exc,
celltype,
cell_params,
label="Excitatory_Cells")
inh_cells = p.Population(n_inh,
celltype,
cell_params,
label="Inhibitory_Cells")
p.NativeRNG(12345)
print "%s Initialising membrane potential to random values..." \
% node_id
rng = NumpyRNG(seed=rngseed, parallel_safe=parallel_safe)
uniform_distr = RandomDistribution('uniform', [v_reset, v_thresh],
rng=rng)
exc_cells.initialize('v', uniform_distr)
inh_cells.initialize('v', uniform_distr)
print "%s Connecting populations..." % node_id
exc_conn = p.FixedProbabilityConnector(pconn,
weights=w_exc,
delays=delay)
inh_conn = p.FixedProbabilityConnector(pconn,
weights=w_inh,
delays=delay)
connections = dict()
connections['e2e'] = p.Projection(exc_cells,
exc_cells,
exc_conn,
target='excitatory',
rng=rng)
connections['e2i'] = p.Projection(exc_cells,
inh_cells,
exc_conn,
target='excitatory',
rng=rng)
connections['i2e'] = p.Projection(inh_cells,
exc_cells,
inh_conn,
target='inhibitory',
rng=rng)
connections['i2i'] = p.Projection(inh_cells,
inh_cells,
inh_conn,
target='inhibitory',
rng=rng)
# === Setup recording ==============================
print "%s Setting up recording..." % node_id
exc_cells.record()
# === Run simulation ================================
print "%d Running simulation..." % node_id
print "timings: number of neurons:", n
print "timings: number of synapses:", n * n * pconn
p.run(tstop)
exc_spikes = exc_cells.getSpikes()
print len(exc_spikes)
current_file_path = os.path.dirname(os.path.abspath(__file__))
current_file_path = os.path.join(current_file_path, "spikes.data")
exc_cells.printSpikes(current_file_path)
pre_recorded_spikes = p.utility_calls.read_spikes_from_file(
current_file_path, 0, n_exc, 0, tstop)
for spike_element, read_element in zip(exc_spikes,
pre_recorded_spikes):
self.assertEqual(round(spike_element[0], 1),
round(read_element[0], 1))
self.assertEqual(round(spike_element[1], 1),
round(read_element[1], 1))
p.end()
# System intentional overload so may error
except SpinnmanTimeoutException as ex:
raise SkipTest(ex)
def test(cases=[1]):
sp = Space(periodic_boundaries=((0, 1), (0, 1), None))
safe = False
verbose = True
autapse = False
parallel_safe = True
render = True
for case in cases:
#w = RandomDistribution('uniform', (0,1))
w = "0.2 + d/0.2"
#w = 0.1
#w = lambda dist : 0.1 + numpy.random.rand(len(dist[0]))*sqrt(dist[0]**2 + dist[1]**2)
#delay = RandomDistribution('uniform', (0.1,5.))
delay = "0.1 + d/0.2"
#delay = 0.1
#delay = lambda distances : 0.1 + numpy.random.rand(len(distances))*distances
d_expression = "d < 0.1"
#d_expression = "(d[0] < 0.05) & (d[1] < 0.05)"
#d_expression = "(d[0]/(0.05**2) + d[1]/(0.1**2)) < 100*numpy.random.rand()"
timer = Timer()
np = num_processes()
timer.start()
if case is 1:
conn = DistanceDependentProbabilityConnector(
d_expression,
delays=delay,
weights=w,
space=sp,
safe=safe,
verbose=verbose,
allow_self_connections=autapse)
fig_name = "DistanceDependent_%s_np_%d.png" % (simulator_name, np)
elif case is 2:
conn = FixedProbabilityConnector(0.05,
weights=w,
delays=delay,
space=sp,
safe=safe,
verbose=verbose,
allow_self_connections=autapse)
fig_name = "FixedProbability_%s_np_%d.png" % (simulator_name, np)
elif case is 3:
conn = AllToAllConnector(delays=delay,
weights=w,
space=sp,
safe=safe,
verbose=verbose,
allow_self_connections=autapse)
fig_name = "AllToAll_%s_np_%d.png" % (simulator_name, np)
elif case is 4:
conn = FixedNumberPostConnector(50,
weights=w,
delays=delay,
space=sp,
safe=safe,
verbose=verbose,
allow_self_connections=autapse)
fig_name = "FixedNumberPost_%s_np_%d.png" % (simulator_name, np)
elif case is 5:
conn = FixedNumberPreConnector(50,
weights=w,
delays=delay,
space=sp,
safe=safe,
verbose=verbose,
allow_self_connections=autapse)
fig_name = "FixedNumberPre_%s_np_%d.png" % (simulator_name, np)
elif case is 6:
conn = OneToOneConnector(safe=safe,
weights=w,
delays=delay,
verbose=verbose)
fig_name = "OneToOne_%s_np_%d.png" % (simulator_name, np)
elif case is 7:
conn = FromFileConnector('connections.dat',
safe=safe,
verbose=verbose)
fig_name = "FromFile_%s_np_%d.png" % (simulator_name, np)
elif case is 8:
conn = SmallWorldConnector(degree=0.1,
rewiring=0.,
weights=w,
delays=delay,
safe=safe,
verbose=verbose,
allow_self_connections=autapse,
space=sp)
fig_name = "SmallWorld_%s_np_%d.png" % (simulator_name, np)
print "Generating data for %s" % fig_name
rng = NumpyRNG(23434, num_processes=np, parallel_safe=parallel_safe)
prj = Projection(x, x, conn, rng=rng)
simulation_time = timer.elapsedTime()
print "Building time", simulation_time
print "Nb synapses built", len(prj)
if render:
if not (os.path.isdir('Results')):
os.mkdir('Results')
print "Saving Positions...."
x.savePositions('Results/positions.dat')
print "Saving Connections...."
prj.saveConnections('Results/connections.dat',
compatible_output=False)
if node_id == 0 and render:
figure()
print "Generating and saving %s" % fig_name
positions = numpy.loadtxt('Results/positions.dat')
connections = numpy.loadtxt('Results/connections.dat')
positions = positions[numpy.argsort(positions[:, 0])]
idx_pre = (connections[:, 0] - x.first_id).astype(int)
idx_post = (connections[:, 1] - x.first_id).astype(int)
d = distances(positions[idx_pre, 1:3], positions[idx_post, 1:3], 1)
subplot(231)
title('Cells positions')
plot(positions[:, 1], positions[:, 2], '.')
subplot(232)
title('Weights distribution')
hist(connections[:, 2], 50)
subplot(233)
title('Delay distribution')
hist(connections[:, 3], 50)
subplot(234)
ids = numpy.random.permutation(numpy.unique(positions[:, 0]))[0:6]
colors = ['k', 'r', 'b', 'g', 'c', 'y']
for count, cell in enumerate(ids):
draw_rf(cell, positions, connections, colors[count])
subplot(235)
plot(d, connections[:, 2], '.')
subplot(236)
plot(d, connections[:, 3], '.')
savefig("Results/" + fig_name)
os.remove('Results/connections.dat')
os.remove('Results/positions.dat')
def test(cases=[1]):
sp = Space(periodic_boundaries=((0, 1), (0, 1), None), axes='xy')
safe = False
callback = progress_bar.set_level
autapse = False
parallel_safe = True
render = True
to_file = True
for case in cases:
#w = RandomDistribution('uniform', (0,1))
w = "0.2 + d/0.2"
#w = 0.1
#w = lambda dist : 0.1 + numpy.random.rand(len(dist[0]))*sqrt(dist[0]**2 + dist[1]**2)
#delay = RandomDistribution('uniform', (0.1,5.))
#delay = "0.1 + d/0.2"
delay = 0.1
#delay = lambda distances : 0.1 + numpy.random.rand(len(distances))*distances
d_expression = "exp(-d**2/(2*0.1**2))"
#d_expression = "(d[0] < 0.05) & (d[1] < 0.05)"
#d_expression = "(d[0]/(0.05**2) + d[1]/(0.1**2)) < 100*numpy.random.rand()"
timer = Timer()
np = num_processes()
timer.start()
synapse = StaticSynapse(weight=w, delay=delay)
rng = NumpyRNG(23434, parallel_safe=parallel_safe)
if case is 1:
conn = DistanceDependentProbabilityConnector(
d_expression,
safe=safe,
callback=callback,
allow_self_connections=autapse,
rng=rng)
fig_name = "DistanceDependent_%s_np_%d.png" % (simulator_name, np)
elif case is 2:
conn = FixedProbabilityConnector(0.02,
safe=safe,
callback=callback,
allow_self_connections=autapse,
rng=rng)
fig_name = "FixedProbability_%s_np_%d.png" % (simulator_name, np)
elif case is 3:
conn = AllToAllConnector(delays=delay,
safe=safe,
callback=callback,
allow_self_connections=autapse)
fig_name = "AllToAll_%s_np_%d.png" % (simulator_name, np)
elif case is 4:
conn = FixedNumberPostConnector(50,
safe=safe,
callback=callback,
allow_self_connections=autapse,
rng=rng)
fig_name = "FixedNumberPost_%s_np_%d.png" % (simulator_name, np)
elif case is 5:
conn = FixedNumberPreConnector(50,
safe=safe,
callback=callback,
allow_self_connections=autapse,
rng=rng)
fig_name = "FixedNumberPre_%s_np_%d.png" % (simulator_name, np)
elif case is 6:
conn = OneToOneConnector(safe=safe, callback=callback)
fig_name = "OneToOne_%s_np_%d.png" % (simulator_name, np)
elif case is 7:
conn = FromFileConnector(files.NumpyBinaryFile(
'Results/connections.dat', mode='r'),
safe=safe,
callback=callback,
distributed=True)
fig_name = "FromFile_%s_np_%d.png" % (simulator_name, np)
elif case is 8:
conn = SmallWorldConnector(degree=0.1,
rewiring=0.,
safe=safe,
callback=callback,
allow_self_connections=autapse)
fig_name = "SmallWorld_%s_np_%d.png" % (simulator_name, np)
print "Generating data for %s" % fig_name
prj = Projection(x, x, conn, synapse, space=sp)
mytime = timer.diff()
print "Time to connect the cell population:", mytime, 's'
print "Nb synapses built", prj.size()
if to_file:
if not (os.path.isdir('Results')):
os.mkdir('Results')
print "Saving Connections...."
prj.save('all',
files.NumpyBinaryFile('Results/connections.dat',
mode='w'),
gather=True)
mytime = timer.diff()
print "Time to save the projection:", mytime, 's'
if render and to_file:
print "Saving Positions...."
x.save_positions('Results/positions.dat')
end()
if node_id == 0 and render and to_file:
figure()
print "Generating and saving %s" % fig_name
positions = numpy.loadtxt('Results/positions.dat')
positions[:, 0] -= positions[:, 0].min()
connections = files.NumpyBinaryFile('Results/connections.dat',
mode='r').read()
print positions.shape, connections.shape
connections[:, 0] -= connections[:, 0].min()
connections[:, 1] -= connections[:, 1].min()
idx_pre = connections[:, 0].astype(int)
idx_post = connections[:, 1].astype(int)
d = distances(positions[idx_pre, 1:3], positions[idx_post, 1:3], 1)
subplot(231)
title('Cells positions')
plot(positions[:, 1], positions[:, 2], '.')
subplot(232)
title('Weights distribution')
hist(connections[:, 2], 50)
subplot(233)
title('Delay distribution')
hist(connections[:, 3], 50)
subplot(234)
numpy.random.seed(74562)
ids = numpy.random.permutation(positions[:, 0])[0:6]
colors = ['k', 'r', 'b', 'g', 'c', 'y']
for count, cell in enumerate(ids):
draw_rf(cell, positions, connections, colors[count])
subplot(235)
plot(d, connections[:, 2], '.')
subplot(236)
plot(d, connections[:, 3], '.')
savefig("Results/" + fig_name)
#os.remove('Results/connections.dat')
#os.remove('Results/positions.dat')
show()
def run_retina(params):
"""Run the retina using the specified parameters."""
print "Setting up simulation"
timer = Timer()
timer.start() # start timer on construction
pyNN.setup(timestep=params['dt'],
max_delay=params['syn_delay'],
threads=params['threads'],
rng_seeds=params['kernelseeds'])
N = params['N']
phr_ON = pyNN.Population((N, N), pyNN.native_cell_type('dc_generator')())
phr_OFF = pyNN.Population((N, N), pyNN.native_cell_type('dc_generator')())
noise_ON = pyNN.Population(
(N, N),
pyNN.native_cell_type('noise_generator')(mean=0.0,
std=params['noise_std']))
noise_OFF = pyNN.Population(
(N, N),
pyNN.native_cell_type('noise_generator')(mean=0.0,
std=params['noise_std']))
phr_ON.set(start=params['simtime'] / 4,
stop=params['simtime'] / 4 * 3,
amplitude=params['amplitude'] * params['snr'])
phr_OFF.set(start=params['simtime'] / 4,
stop=params['simtime'] / 4 * 3,
amplitude=-params['amplitude'] * params['snr'])
# target ON and OFF populations
v_init = params['parameters_gc'].pop('Vinit')
out_ON = pyNN.Population((N, N),
pyNN.native_cell_type('iaf_cond_exp_sfa_rr')(
**params['parameters_gc']))
out_OFF = pyNN.Population((N, N),
pyNN.native_cell_type('iaf_cond_exp_sfa_rr')(
**params['parameters_gc']))
out_ON.initialize(v=v_init)
out_OFF.initialize(v=v_init)
#print "Connecting the network"
retina_proj_ON = pyNN.Projection(phr_ON, out_ON, pyNN.OneToOneConnector())
retina_proj_ON.set(weight=params['weight'])
retina_proj_OFF = pyNN.Projection(phr_OFF, out_OFF,
pyNN.OneToOneConnector())
retina_proj_OFF.set(weight=params['weight'])
noise_proj_ON = pyNN.Projection(noise_ON, out_ON, pyNN.OneToOneConnector())
noise_proj_ON.set(weight=params['weight'])
noise_proj_OFF = pyNN.Projection(noise_OFF, out_OFF,
pyNN.OneToOneConnector())
noise_proj_OFF.set(weight=params['weight'])
out_ON.record('spikes')
out_OFF.record('spikes')
# reads out time used for building
buildCPUTime = timer.elapsedTime()
print "Running simulation"
timer.start() # start timer on construction
pyNN.run(params['simtime'])
simCPUTime = timer.elapsedTime()
out_ON_DATA = out_ON.get_data().segments[0]
out_OFF_DATA = out_OFF.get_data().segments[0]
print "\nRetina Network Simulation:"
print(params['description'])
print "Number of Neurons : ", N**2
print "Output rate (ON) : ", out_ON.mean_spike_count(), \
"spikes/neuron in ", params['simtime'], "ms"
print "Output rate (OFF) : ", out_OFF.mean_spike_count(), \
"spikes/neuron in ", params['simtime'], "ms"
print "Build time : ", buildCPUTime, "s"
print "Simulation time : ", simCPUTime, "s"
return out_ON_DATA, out_OFF_DATA
def callback(data_input):
#====================================================================
# Unpacking the Joint Angle Message
#====================================================================
global message
message = data_input.degree
rospy.loginfo('=====> received joint angle in degree %r', message)
print message
if type(message) != int:
input_rates = list(message)
n_input_neurons = len(input_rates)
else:
input_rates = message
n_input_neurons = 1
#msg_list= [int(msg.encode('hex'),16) for msg in message]
timer = Timer()
dt = 0.1
p.setup(timestep=dt) # 0.1ms
#====================================================================
# Defining the LSM
#====================================================================
n_res=2000
w_exc_b=0.2
w_inh_b=-0.8
rout_w_exc=20
rout_w_inh=-80
n_readout_neurons = 2
n_reservoir_neurons = n_res
n_res = n_reservoir_neurons
exc_rate = 0.8 # percentage of excitatory neurons in reservoir
n_exc = int(round(n_reservoir_neurons*exc_rate))
n_inh = n_reservoir_neurons-n_exc
izh_celltype = p.native_cell_type('izhikevich')
if_celltype = p.IF_curr_exp
celltype = if_celltype
spike_source = p.native_cell_type('poisson_generator')
inp_pop=p.Population(n_input_neurons*10,spike_source,{'rate':input_rates})
exc_cells = p.Population(n_exc, celltype, label="Excitatory_Cells")
inh_cells = p.Population(n_inh, celltype, label="Inhibitory_Cells")
# initialize with a uniform random distributin
# use seeding for reproducability
rngseed = 98766987
parallel_safe = True
rng = NumpyRNG(seed=rngseed, parallel_safe=parallel_safe)
unifDistr = RandomDistribution('uniform', (-70,-65), rng=rng)
inh_cells.initialize('V_m',unifDistr)
exc_cells.initialize('V_m',unifDistr)
readout_neurons = p.Population(2, celltype, label="readout_neuron")
inp_weight=3.
inp_delay =1
inp_weight_distr = RandomDistribution('normal', [inp_weight, 1e-3], rng=rng)
# connect each input neuron to 30% of the reservoir neurons
inp_conn = p.FixedProbabilityConnector(p_connect=0.3,weights =inp_weight_distr, delays=inp_delay)
connections = {}
connections['inp2e'] = p.Projection(inp_pop, exc_cells, inp_conn)
connections['inp2i'] = p.Projection(inp_pop, inh_cells, inp_conn)
pconn = 0.01 # sparse connection probability
# scale the weights w.r.t. the network to keep it stable
w_exc = w_exc_b/np.sqrt(n_res) # nA
w_inh = w_inh_b/np.sqrt(n_res) # nA
delay_exc = 1 # defines how long (ms) the synapse takes for transmission
delay_inh = 1
weight_distr_exc = RandomDistribution('normal', [w_exc, 1/n_res], rng=rng)
weight_distr_inh = RandomDistribution('normal', [w_inh, 1/n_res], rng=rng)
exc_conn = p.FixedProbabilityConnector(pconn, weights=weight_distr_exc, delays=delay_exc)
inh_conn = p.FixedProbabilityConnector(pconn, weights=weight_distr_inh, delays=delay_inh)
connections['e2e'] = p.Projection(exc_cells, exc_cells, exc_conn, target='excitatory')
connections['e2i'] = p.Projection(exc_cells, inh_cells, exc_conn, target='excitatory')
connections['i2e'] = p.Projection(inh_cells, exc_cells, inh_conn, target='inhibitory')
connections['i2i'] = p.Projection(inh_cells, inh_cells, inh_conn, target='inhibitory')
rout_conn_exc = p.AllToAllConnector(weights=rout_w_exc, delays=delay_exc)
rout_conn_inh = p.AllToAllConnector(weights=rout_w_inh, delays=delay_exc)
connections['e2rout'] = p.Projection(exc_cells, readout_neurons, rout_conn_exc, target='excitatory')
connections['i2rout'] = p.Projection(inh_cells, readout_neurons, rout_conn_inh, target='inhibitory')
readout_neurons.record()
exc_cells.record()
inh_cells.record()
inp_pop.record()
p.run(20)
r_spikes = readout_neurons.getSpikes()
exc_spikes = exc_cells.getSpikes()
inh_spikes = inh_cells.getSpikes()
inp_spikes = inp_pop.getSpikes()
rospy.loginfo('=====> shape of r_spikes %r', np.shape(r_spikes))
#====================================================================
# Compute Readout Spike Rates
#====================================================================
alpha_rates = alpha_decoding(r_spikes,dt)
mean_rates = mean_decoding(r_spikes,dt)
#====================================================================
# Publish Readout Rates
#====================================================================
# TODO: error handling if r_spikes is empty
pub = rospy.Publisher('/alpha_readout_rates', Pop_List, queue_size=10)
alpha_readout_rates = Pop_List
alpha_readout_rates = alpha_rates
pub.publish(alpha_readout_rates)
pub = rospy.Publisher('/mean_readout_rates', Pop_List, queue_size=10)
mean_readout_rates = Pop_List
mean_readout_rates = mean_rates
pub.publish(mean_readout_rates)
def run_model(sim, **options):
"""
Run a simulation using the parameters read from the file "spike_train_statistics.json"
:param sim: the PyNN backend module to be used.
:param options: should contain a keyword "simulator" which is the name of the PyNN backend module used.
:return: a tuple (`data`, `times`) where `data` is a Neo Block containing the recorded spikes
and `times` is a dict containing the time taken for different phases of the simulation.
"""
import json
from pyNN.utility import Timer
print("Running")
timer = Timer()
g = open("spike_train_statistics.json", 'r')
d = json.load(g)
N = d['param']['N']
max_rate = d['param']['max_rate']
tstop = d['param']['tstop']
d['SpikeSourcePoisson'] = {
"duration": tstop
}
if options['simulator'] == "hardware.brainscales":
hardware_preset = d['setup'].pop('hardware_preset', None)
if hardware_preset:
d['setup']['hardware'] = sim.hardwareSetup[hardware_preset]
d['SpikeSourcePoisson']['random'] = True
place = mapper.place()
timer.start()
sim.setup(**d['setup'])
spike_sources = sim.Population(N, sim.SpikeSourcePoisson, d['SpikeSourcePoisson'])
delta_rate = max_rate/N
rates = numpy.linspace(delta_rate, max_rate, N)
print("Firing rates: %s" % rates)
if PYNN07:
spike_sources.tset("rate", rates)
else:
spike_sources.set(rate=rates)
if options['simulator'] == "hardware.brainscales":
for i, spike_source in enumerate(spike_sources):
place.to(spike_source, hicann=i//8, neuron=i%64)
place.commit()
if PYNN07:
spike_sources.record()
else:
spike_sources.record('spikes')
setup_time = timer.diff()
sim.run(tstop)
run_time = timer.diff()
if PYNN07:
spike_array = spike_sources.getSpikes()
data = spike_array_to_neo(spike_array, spike_sources, tstop)
else:
data = spike_sources.get_data()
sim.end()
closing_time = timer.diff()
times = {'setup_time': setup_time, 'run_time': run_time, 'closing_time': closing_time}
return data, times
def run(self, params, verbose=True):
"""
params are the parameters to use
"""
tmpdir = tempfile.mkdtemp()
myTimer = Timer()
# === Build the network ========================================================
if verbose: print "Setting up simulation"
myTimer.start() # start timer on construction
sim.setup(timestep=params['dt'], max_delay=params['syn_delay'])
N = params['N']
#dc_generator
phr_ON = sim.Population((N, ), 'dc_generator')
phr_OFF = sim.Population((N, ), 'dc_generator')
for factor, phr in [(-params['snr'], phr_OFF),
(params['snr'], phr_ON)]:
phr.tset('amplitude', params['amplitude'] * factor)
phr.set({
'start': params['simtime'] / 4,
'stop': params['simtime'] / 4 * 3
})
# internal noise model (see benchmark_noise)
noise_ON = sim.Population((N, ), 'noise_generator', {
'mean': 0.,
'std': params['noise_std']
})
noise_OFF = sim.Population((N, ), 'noise_generator', {
'mean': 0.,
'std': params['noise_std']
})
# target ON and OFF populations (what about a tridimensional Population?)
out_ON = sim.Population(
(N, ), sim.IF_curr_alpha
) #'IF_cond_alpha) #iaf_sfa_neuron')# EIF_cond_alpha_isfa_ista, IF_cond_exp_gsfa_grr,sim.IF_cond_alpha)#'iaf_sfa_neuron',params['parameters_gc'])#'iaf_cond_neuron')# IF_cond_alpha) #
out_OFF = sim.Population(
(N, ), sim.IF_curr_alpha
) #'IF_cond_alpha) #IF_curr_alpha)#'iaf_sfa_neuron')#sim.IF_curr_alpha)#,params['parameters_gc'])
# initialize membrane potential TODO: and conductances?
from pyNN.random import RandomDistribution, NumpyRNG
rng = NumpyRNG(seed=params['kernelseed'])
vinit_distr = RandomDistribution(distribution='uniform',
parameters=[-70, -55],
rng=rng)
for out_ in [out_ON, out_OFF]:
out_.randomInit(vinit_distr)
retina_proj_ON = sim.Projection(phr_ON, out_ON,
sim.OneToOneConnector())
retina_proj_ON.setWeights(params['weight'])
# TODO fix setWeight, add setDelays to 10 ms (relative to stimulus onset)
retina_proj_OFF = sim.Projection(phr_OFF, out_OFF,
sim.OneToOneConnector())
retina_proj_OFF.setWeights(params['weight'])
noise_proj_ON = sim.Projection(noise_ON, out_ON,
sim.OneToOneConnector())
noise_proj_ON.setWeights(params['weight'])
noise_proj_OFF = sim.Projection(
noise_OFF, out_OFF, sim.OneToOneConnector(
)) # implication if ON and OFF have the same noise input?
noise_proj_OFF.setWeights(params['weight'])
out_ON.record()
out_OFF.record()
# reads out time used for building
buildCPUTime = myTimer.elapsedTime()
# === Run simulation ===========================================================
if verbose: print "Running simulation"
myTimer.reset() # start timer on construction
sim.run(params['simtime'])
simCPUTime = myTimer.elapsedTime()
myTimer.reset() # start timer on construction
# TODO LUP use something like "for pop in [phr, out]" ?
out_ON_filename = os.path.join(tmpdir, 'out_on.gdf')
out_OFF_filename = os.path.join(tmpdir, 'out_off.gdf')
out_ON.printSpikes(out_ON_filename) #
out_OFF.printSpikes(out_OFF_filename) #
# TODO LUP get out_ON_DATA on a 2D grid independantly of out_ON.cell.astype(int)
out_ON_DATA = load_spikelist(out_ON_filename,
range(N),
t_start=0.0,
t_stop=params['simtime'])
out_OFF_DATA = load_spikelist(out_OFF_filename,
range(N),
t_start=0.0,
t_stop=params['simtime'])
out = {
'out_ON_DATA': out_ON_DATA,
'out_OFF_DATA': out_OFF_DATA
} #,'out_ON_pos':out_ON}
# cleans up
os.remove(out_ON_filename)
os.remove(out_OFF_filename)
os.rmdir(tmpdir)
writeCPUTime = myTimer.elapsedTime()
if verbose:
print "\nRetina Network Simulation:"
print(params['description'])
print "Number of Neurons : ", N
print "Output rate (ON) : ", out_ON_DATA.mean_rate(
), "Hz/neuron in ", params['simtime'], "ms"
print "Output rate (OFF) : ", out_OFF_DATA.mean_rate(
), "Hz/neuron in ", params['simtime'], "ms"
print("Build time : %g s" % buildCPUTime)
print("Simulation time : %g s" % simCPUTime)
print("Writing time : %g s" % writeCPUTime)
return out
def runNetwork(Be,
Bi,
nn_stim,
show_gui=True,
dt = defaultParams.dt,
N_rec_v = 5,
save=False,
simtime = defaultParams.Tpost+defaultParams.Tstim+defaultParams.Tblank+defaultParams.Ttrans,
extra = {},
kernelseed = 123):
exec("from pyNN.%s import *" % simulator_name) in globals()
timer = Timer()
rec_conn={'EtoE':1, 'EtoI':1, 'ItoE':1, 'ItoI':1}
print('####################')
print('### (Be, Bi, nn_stim): ', Be, Bi, nn_stim)
print('####################')
Bee, Bei = Be, Be
Bie, Bii = Bi, Bi
N = defaultParams.N
NE = defaultParams.NE
NI = defaultParams.NI
print('\n # -----> Num cells: %s, size of pert. inh: %s; base rate %s; pert rate %s'% (N, nn_stim, defaultParams.r_bkg, defaultParams.r_stim))
r_extra = np.zeros(N)
r_extra[NE:NE+nn_stim] = defaultParams.r_stim
rr1 = defaultParams.r_bkg*np.random.uniform(.75,1.25, N)
rr2 = rr1 + r_extra
rank = setup(timestep=dt, max_delay=defaultParams.delay_default, reference='ISN', save_format='hdf5', **extra)
print("rank =", rank)
nump = num_processes()
print("num_processes =", nump)
import socket
host_name = socket.gethostname()
print("Host #%d is on %s" % (rank+1, host_name))
if 'threads' in extra:
print("%d Initialising the simulator with %d threads..." % (rank, extra['threads']))
else:
print("%d Initialising the simulator with single thread..." % rank)
timer.start() # start timer on construction
print("%d Setting up random number generator using seed %s" % (rank, kernelseed))
ks = open('kernelseed','w')
ks.write('%i'%kernelseed)
ks.close()
rng = NumpyRNG(kernelseed, parallel_safe=True)
nesp = defaultParams.neuron_params_default
cell_parameters = {
'cm': nesp['C_m']/1000, # Capacitance of the membrane in nF
'tau_refrac': nesp['t_ref'], # Duration of refractory period in ms.
'v_spike': 0.0 , # Spike detection threshold in mV. https://github.com/nest/nest-simulator/blob/master/models/aeif_cond_alpha.cpp
'v_reset': nesp['V_reset'], # Reset value for V_m after a spike. In mV.
'v_rest': nesp['E_L'], # Resting membrane potential (Leak reversal potential) in mV.
'tau_m': nesp['C_m']/nesp['g_L'], # Membrane time constant in ms = cm/tau_m*1000.0, C_m/g_L
'i_offset': nesp['I_e']/1000, # Offset current in nA
'a': 0, # Subthreshold adaptation conductance in nS.
'b': 0, # Spike-triggered adaptation in nA
'delta_T': 2 , # Slope factor in mV. See https://github.com/nest/nest-simulator/blob/master/models/aeif_cond_alpha.cpp
'tau_w': 144.0, # Adaptation time constant in ms. See https://github.com/nest/nest-simulator/blob/master/models/aeif_cond_alpha.cpp
'v_thresh': nesp['V_th'], # Spike initiation threshold in mV
'e_rev_E': nesp['E_ex'], # Excitatory reversal potential in mV.
'tau_syn_E': nesp['tau_syn_ex'], # Rise time of excitatory synaptic conductance in ms (alpha function).
'e_rev_I': nesp['E_in'], # Inhibitory reversal potential in mV.
'tau_syn_I': nesp['tau_syn_in'], # Rise time of the inhibitory synaptic conductance in ms (alpha function).
}
print("%d Creating population with %d neurons." % (rank, N))
celltype = EIF_cond_alpha_isfa_ista(**cell_parameters)
celltype.default_initial_values['v'] = cell_parameters['v_rest'] # Setting default init v, useful for NML2 export
layer_volume = Cuboid(1000,100,1000)
layer_structure = RandomStructure(layer_volume, origin=(0,0,0))
layer_structure_input = RandomStructure(layer_volume, origin=(0,-150,0))
default_cell_radius = 15
stim_cell_radius = 10
#EI_pop = Population(N, celltype, structure=layer_structure, label="EI")
E_pop = Population(NE, celltype, structure=layer_structure, label='E_pop')
E_pop.annotate(color='1 0 0')
E_pop.annotate(radius=default_cell_radius)
E_pop.annotate(type='E') # temp indicator to use for connection arrowhead
#print("%d Creating pop %s." % (rank, E_pop))
I_pop = Population(NI, celltype, structure=layer_structure, label='I_pop')
I_pop.annotate(color='0 0 .9')
I_pop.annotate(radius=default_cell_radius)
I_pop.annotate(type='I') # temp indicator to use for connection arrowhead
#print("%d Creating pop %s." % (rank, I_pop))
I_pert_pop = PopulationView(I_pop, np.array(range(0,nn_stim)),label='I_pert_pop')
I_nonpert_pop = PopulationView(I_pop, np.array(range(nn_stim,NI)),label='I_nonpert_pop')
p_rate = defaultParams.r_bkg
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeA_E = SpikeSourcePoisson(rate=p_rate, start=0,duration=defaultParams.Ttrans+defaultParams.Tblank+defaultParams.Tstim+defaultParams.Tpost)
expoissonA_E = Population(NE, source_typeA_E, structure=layer_structure_input, label="stim_E")
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeA_I = SpikeSourcePoisson(rate=p_rate, start=0,duration=defaultParams.Ttrans+defaultParams.Tblank)
expoissonA_I = Population(NI, source_typeA_I, structure=layer_structure_input, label="pre_pert_stim_I")
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeB = SpikeSourcePoisson(rate=p_rate, start=defaultParams.Ttrans+defaultParams.Tblank,duration=defaultParams.Tstim+defaultParams.Tpost)
#expoissonB_E = Population(NE, source_typeB, label="non_pert_stim_E")
expoissonB_I = Population(len(I_nonpert_pop), source_typeB, structure=layer_structure_input, label="non_pert_stim_I")
p_rate = defaultParams.r_bkg+defaultParams.r_stim
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeC = SpikeSourcePoisson(rate=p_rate, start=defaultParams.Ttrans+defaultParams.Tblank, duration=defaultParams.Tstim)
expoissonC = Population(nn_stim, source_typeC, structure=layer_structure_input, label="pert_stim")
p_rate = defaultParams.r_bkg
print("%d Creating excitatory Poisson generator with rate %g spikes/s." % (rank, p_rate))
source_typeD = SpikeSourcePoisson(rate=p_rate, start=defaultParams.Ttrans+defaultParams.Tblank+defaultParams.Tstim, duration=defaultParams.Tpost)
expoissonD = Population(nn_stim, source_typeD, structure=layer_structure_input, label="pert_poststim")
for p in [expoissonA_E,expoissonA_I,expoissonB_I,expoissonC,expoissonD]:
p.annotate(color='0.8 0.8 0.8')
p.annotate(radius=stim_cell_radius)
progress_bar = ProgressBar(width=20)
connector_E = FixedProbabilityConnector(0.15, rng=rng, callback=progress_bar)
connector_I = FixedProbabilityConnector(1, rng=rng, callback=progress_bar)
EE_syn = StaticSynapse(weight=0.001*Bee, delay=defaultParams.delay_default)
EI_syn = StaticSynapse(weight=0.001*Bei, delay=defaultParams.delay_default)
II_syn = StaticSynapse(weight=0.001*Bii, delay=defaultParams.delay_default)
IE_syn = StaticSynapse(weight=0.001*Bie, delay=defaultParams.delay_default)
#I_syn = StaticSynapse(weight=JI, delay=delay)
ext_Connector = OneToOneConnector(callback=progress_bar)
ext_syn_bkg = StaticSynapse(weight=0.001*defaultParams.Be_bkg, delay=defaultParams.delay_default)
ext_syn_stim = StaticSynapse(weight=0.001*defaultParams.Be_stim, delay=defaultParams.delay_default)
E_to_E = Projection(E_pop, E_pop, connector_E, EE_syn, receptor_type="excitatory")
print("E --> E\t\t", len(E_to_E), "connections")
E_to_I = Projection(E_pop, I_pop, connector_E, EI_syn, receptor_type="excitatory")
print("E --> I\t\t", len(E_to_I), "connections")
I_to_I = Projection(I_pop, I_pop, connector_I, II_syn, receptor_type="inhibitory")
print("I --> I\t\t", len(I_to_I), "connections")
I_to_E = Projection(I_pop, E_pop, connector_I, IE_syn, receptor_type="inhibitory")
print("I --> E\t\t", len(I_to_E), "connections")
input_A_E = Projection(expoissonA_E, E_pop, ext_Connector, ext_syn_bkg, receptor_type="excitatory")
print("input --> %s cells pre pert\t"%len(E_pop), len(input_A_E), "connections")
input_A_I = Projection(expoissonA_I, I_pop, ext_Connector, ext_syn_bkg, receptor_type="excitatory")
print("input --> %s cells pre pert\t"%len(I_pop), len(input_A_I), "connections")
##input_B_E = Projection(expoissonB_E, E_pop, ext_Connector, ext_syn_bkg, receptor_type="excitatory")
##print("input --> %s cells post pert\t"%len(E_pop), len(input_B_E), "connections")
input_B_I = Projection(expoissonB_I, I_nonpert_pop, ext_Connector, ext_syn_bkg, receptor_type="excitatory")
print("input --> %s cells post pert\t"%len(I_nonpert_pop), len(input_B_I), "connections")
input_C = Projection(expoissonC, I_pert_pop, ext_Connector, ext_syn_stim, receptor_type="excitatory")
print("input --> %s cells pre pert\t"%len(I_pert_pop), len(input_C), "connections")
input_D = Projection(expoissonD, I_pert_pop, ext_Connector, ext_syn_stim, receptor_type="excitatory")
print("input --> %s cells pre pert\t"%len(I_pert_pop), len(input_D), "connections")
# Can't be used for connections etc. as NeuroML export not (yet) supported
EI_pop = Assembly(E_pop, I_pop, label='EI')
# Record spikes
print("%d Setting up recording in excitatory population." % rank)
EI_pop.record('spikes')
if N_rec_v>0:
EI_pop[0:min(N,N_rec_v)].record('v')
# read out time used for building
buildCPUTime = timer.elapsedTime()
# === Run simulation ===========================================================
# run, measure computer time
timer.start() # start timer on construction
print("%d Running simulation in %s for %g ms (dt=%sms)." % (rank, simulator_name, simtime, dt))
run(simtime)
print("Done")
simCPUTime = timer.elapsedTime()
# write data to file
if save and not simulator_name=='neuroml':
for pop in [EI_pop]:
filename="ISN-%s-%s-%i.gdf"%(simulator_name, pop.label, rank)
ff = open(filename, 'w')
spikes = pop.get_data('spikes', gather=False)
spiketrains = spikes.segments[0].spiketrains
print('Saving data recorded for %i spiketrains in pop %s, indices: %s, ids: %s to %s'% \
(len(spiketrains),
pop.label,
[s.annotations['source_index'] for s in spiketrains],
[s.annotations['source_id'] for s in spiketrains],
filename))
for spiketrain_i in range(len(spiketrains)):
spiketrain = spiketrains[spiketrain_i]
source_id = spiketrain.annotations['source_id']
source_index = spiketrain.annotations['source_index']
#print("Writing spike data for cell %s[%s] (gid: %i): %i spikes: [%s,...,%s] "%(pop.label,source_index, source_id, len(spiketrain),spiketrain[0],spiketrain[-1]))
for t in spiketrain:
ff.write('%s\t%i\n'%(t.magnitude,spiketrain_i))
ff.close()
vs = pop.get_data('v', gather=False)
for segment in vs.segments:
for i in range(len(segment.analogsignals[0].transpose())):
filename="ISN-%s-%s-cell%i.dat"%(simulator_name, pop.label, i)
print('Saving cell %i in %s to %s'%(i,pop.label,filename))
vm = segment.analogsignals[0].transpose()[i]
tt = np.array([t*dt/1000. for t in range(len(vm))])
times_vm = np.array([tt, vm/1000.]).transpose()
np.savetxt(filename, times_vm , delimiter = '\t', fmt='%s')
spike_data = {}
spike_data['senders'] = []
spike_data['times'] = []
index_offset = 1
for pop in [EI_pop]:
if rank == 0:
spikes = pop.get_data('spikes', gather=False)
#print(spikes.segments[0].all_data)
num_rec = len(spikes.segments[0].spiketrains)
print("Extracting spike info (%i) for %i cells in %s"%(num_rec,pop.size,pop.label))
#assert(num_rec==len(spikes.segments[0].spiketrains))
for i in range(num_rec):
ss = spikes.segments[0].spiketrains[i]
for s in ss:
index = i+index_offset
#print("Adding spike at %s in %s[%i] (cell %i)"%(s,pop.label,i,index))
spike_data['senders'].append(index)
spike_data['times'].append(s)
index_offset+=pop.size
print("Build time : %g s" % buildCPUTime)
print("Simulation time : %g s" % simCPUTime)
# === Clean up and quit ========================================================
end()
def run(self, params, verbose=True):
tmpdir = tempfile.mkdtemp()
timer = Timer()
timer.start() # start timer on construction
# === Build the network ========================================================
if verbose: print "Setting up simulation"
sim.setup(timestep=params.simulation.dt,
max_delay=params.simulation.syn_delay,
debug=False)
N = params.N
#dc_generator
current_source = sim.DCSource(amplitude=params.snr,
start=params.simulation.simtime / 4,
stop=params.simulation.simtime / 4 * 3)
# internal noise model (NEST specific)
noise = sim.Population(N, 'noise_generator', {
'mean': 0.,
'std': params.noise_std
})
# target population
output = sim.Population(N, sim.IF_cond_exp)
# initialize membrane potential
numpy.random.seed(params.simulation.kernelseed)
V_rest, V_spike = -70., -53.
output.tset('v_init',
V_rest + numpy.random.rand(N, ) * (V_spike - V_rest))
# Connecting the network
conn = sim.OneToOneConnector(weights=params.weight)
sim.Projection(noise, output, conn)
for cell in output:
cell.inject(current_source)
output.record()
# reads out time used for building
buildCPUTime = timer.elapsedTime()
# === Run simulation ===========================================================
if verbose: print "Running simulation"
timer.reset() # start timer on construction
sim.run(params.simulation.simtime)
simCPUTime = timer.elapsedTime()
timer.reset() # start timer on construction
output_filename = os.path.join(tmpdir, 'output.gdf')
#print output_filename
output.printSpikes(output_filename) #
output_DATA = load_spikelist(output_filename,
N,
t_start=0.0,
t_stop=params.simulation.simtime)
writeCPUTime = timer.elapsedTime()
if verbose:
print "\nFiber Network Simulation:"
print "Number of Neurons : ", N
print "Mean Output rate : ", output_DATA.mean_rate(
), "Hz during ", params.simulation.simtime, "ms"
print("Build time : %g s" % buildCPUTime)
print("Simulation time : %g s" % simCPUTime)
print("Writing time : %g s" % writeCPUTime)
os.remove(output_filename)
os.rmdir(tmpdir)
return output_DATA
def test_callback(data_input):
global message
message = data_input.actual.positions
msg_list = list(message)
#msg_list[0] = int(message[0].encode('hex'),16)
#for i in
#msg_list = int(message.encode('hex'),16)
#print('============= Received image data.',message)
rospy.loginfo('=====received data %r', msg_list[0])
timer = Timer()
dt = 0.1
p.setup(timestep=dt) # 0.1ms
pop_1 = p.Population(1, p.IF_curr_exp, {}, label="pop_1")
#input = p.Population(1, p.SpikeSourceArray, {'spike_times': [[0,3,6]]}, label='input')
input = p.Population(1, p.SpikeSourcePoisson,
{'rate': (msg_list[0] + 1.6) * 100})
stat_syn = p.StaticSynapse(weight=50.0, delay=1)
input_proj = p.Projection(input,
pop_1,
p.OneToOneConnector(),
synapse_type=stat_syn,
receptor_type='excitatory')
pop_1.record(['v', 'spikes'])
p.run(10)
pop_1_data = pop_1.get_data()
spikes = pop_1_data.segments[0].spiketrains[0]
mean_rate = int(gaussian_convolution(spikes, dt))
rospy.loginfo('=====mean_rate %r', mean_rate) # mean_rate = 64
rate_command = mean_rate
# rate coding of the spike train
'''
pub = rospy.Publisher('/cmd_vel_mux/input/teleop', Twist, queue_size=10)
# construct the output command
command = Twist()
command.linear.x = rate_command*0.02
command.angular.z = rate_command/50000.
pub.publish(command)
'''
pub = rospy.Publisher('/arm_controller/follow_joint_trajectory/goal',
FollowJointTrajectoryActionGoal,
queue_size=10)
command = FollowJointTrajectoryActionGoal()
command.header.stamp = rospy.Time.now()
command.goal.trajectory.joint_names = ['elbow']
point = JointTrajectoryPoint()
point.positions = [rate_command / 10]
point.time_from_start = rospy.Duration(1)
command.goal.trajectory.points.append(point)
pub.publish(command)
rospy.loginfo('=====send command %r', command.goal.trajectory.points[0])
fig_settings = {
'lines.linewidth': 0.5,
'axes.linewidth': 0.5,
'axes.labelsize': 'small',
'legend.fontsize': 'small',
'font.size': 8
}
plt.rcParams.update(fig_settings)
fig1 = plt.figure(1, figsize=(6, 8))
def plot_spiketrains(segment):
for spiketrain in segment.spiketrains:
y = np.ones_like(spiketrain) * spiketrain.annotations['source_id']
plt.plot(spiketrain, y, '.')
plt.ylabel(segment.name)
plt.setp(plt.gca().get_xticklabels(), visible=False)
def plot_signal(signal, index, colour='b'):
label = "Neuron %d" % signal.annotations['source_ids'][index]
plt.plot(signal.times, signal[:, index], colour, label=label)
plt.ylabel("%s (%s)" %
(signal.name, signal.units._dimensionality.string))
plt.setp(plt.gca().get_xticklabels(), visible=False)
plt.legend()
print("now plotting the network---------------")
rospy.loginfo('--------now plotting---------------')
n_panels = sum(a.shape[1]
for a in pop_1_data.segments[0].analogsignalarrays) + 2
plt.subplot(n_panels, 1, 1)
plot_spiketrains(pop_1_data.segments[0])
panel = 3
for array in pop_1_data.segments[0].analogsignalarrays:
for i in range(array.shape[1]):
plt.subplot(n_panels, 1, panel)
plot_signal(array, i, colour='bg'[panel % 2])
panel += 1
plt.xlabel("time (%s)" % array.times.units._dimensionality.string)
plt.setp(plt.gca().get_xticklabels(), visible=True) #
Brunel N (2000) Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. J Comput Neurosci 8:183-208
Andrew Davison, UNIC, CNRS
May 2006
"""
from pyNN.utility import get_script_args, Timer, ProgressBar
simulator_name = get_script_args(1)[0]
exec("from pyNN.%s import *" % simulator_name)
from pyNN.random import NumpyRNG, RandomDistribution
timer = Timer()
# === Define parameters ========================================================
downscale = 50 # scale number of neurons down by this factor
# scale synaptic weights up by this factor to
# obtain similar dynamics independent of size
order = 50000 # determines size of network:
# 4*order excitatory neurons
# 1*order inhibitory neurons
Nrec = 50 # number of neurons to record from, per population
epsilon = 0.1 # connectivity: proportion of neurons each neuron projects to
# Parameters determining model dynamics, cf Brunel (2000), Figs 7, 8 and Table 1
# here: Case C, asynchronous irregular firing, ~35 Hz
eta = 2.0 # rel rate of external input
def runBrunelNetwork(g=5.,
eta=2.,
dt=0.1,
simtime=1000.0,
delay=1.5,
epsilon=0.1,
order=2500,
N_rec=50,
N_rec_v=2,
save=False,
simulator_name='nest',
jnml_simulator=None,
extra={}):
exec("from pyNN.%s import *" % simulator_name) in globals()
timer = Timer()
# === Define parameters ========================================================
downscale = 1 # scale number of neurons down by this factor
# scale synaptic weights up by this factor to
# obtain similar dynamics independent of size
order = order # determines size of network:
# 4*order excitatory neurons
# 1*order inhibitory neurons
Nrec = N_rec # number of neurons to record from, per population
epsilon = epsilon # connectivity: proportion of neurons each neuron projects to
# Parameters determining model dynamics, cf Brunel (2000), Figs 7, 8 and Table 1
# here: Case C, asynchronous irregular firing, ~35 Hz
eta = eta # rel rate of external input
g = g # rel strength of inhibitory synapses
J = 0.1 # synaptic weight [mV]
delay = delay # synaptic delay, all connections [ms]
# single neuron parameters
tauMem = 20.0 # neuron membrane time constant [ms]
tauSyn = 0.1 # synaptic time constant [ms]
tauRef = 2.0 # refractory time [ms]
U0 = 0.0 # resting potential [mV]
theta = 20.0 # threshold
# simulation-related parameters
simtime = simtime # simulation time [ms]
dt = dt # simulation step length [ms]
# seed for random generator used when building connections
connectseed = 12345789
use_RandomArray = True # use Python rng rather than NEST rng
# seed for random generator(s) used during simulation
kernelseed = 43210987
# === Calculate derived parameters =============================================
# scaling: compute effective order and synaptic strength
order_eff = int(float(order) / downscale)
J_eff = J * downscale
# compute neuron numbers
NE = int(4 * order_eff) # number of excitatory neurons
NI = int(1 * order_eff) # number of inhibitory neurons
N = NI + NE # total number of neurons
# compute synapse numbers
CE = int(epsilon * NE) # number of excitatory synapses on neuron
CI = int(epsilon * NI) # number of inhibitory synapses on neuron
C = CE + CI # total number of internal synapses per n.
Cext = CE # number of external synapses on neuron
# synaptic weights, scaled for alpha functions, such that
# for constant membrane potential, charge J would be deposited
fudge = 0.00041363506632638 # ensures dV = J at V=0
# excitatory weight: JE = J_eff / tauSyn * fudge
JE = (J_eff / tauSyn) * fudge
# inhibitory weight: JI = - g * JE
JI = -g * JE
# threshold, external, and Poisson generator rates:
nu_thresh = theta / (J_eff * CE * tauMem)
nu_ext = eta * nu_thresh # external rate per synapse
p_rate = 1000 * nu_ext * Cext # external input rate per neuron (Hz)
# number of synapses---just so we know
Nsyn = (
C + 1
) * N + 2 * Nrec # number of neurons * (internal synapses + 1 synapse from PoissonGenerator) + 2synapses" to spike detectors
# put cell parameters into a dict
cell_params = {
'tau_m': tauMem,
'tau_syn_E': tauSyn,
'tau_syn_I': tauSyn,
'tau_refrac': tauRef,
'v_rest': U0,
'v_reset': U0,
'v_thresh': theta,
'cm': 0.001
} # (nF)
# === Build the network ========================================================
# clear all existing network elements and set resolution and limits on delays.
# For NEST, limits must be set BEFORE connecting any elements
#extra = {'threads' : 2}
rank = setup(timestep=dt, max_delay=delay, **extra)
print("rank =", rank)
np = num_processes()
print("np =", np)
import socket
host_name = socket.gethostname()
print("Host #%d is on %s" % (rank + 1, host_name))
if 'threads' in extra:
print("%d Initialising the simulator with %d threads..." %
(rank, extra['threads']))
else:
print("%d Initialising the simulator with single thread..." % rank)
# Small function to display information only on node 1
def nprint(s):
if rank == 0:
print(s)
timer.start() # start timer on construction
print("%d Setting up random number generator" % rank)
rng = NumpyRNG(kernelseed, parallel_safe=True)
print("%d Creating excitatory population with %d neurons." % (rank, NE))
celltype = IF_curr_alpha(**cell_params)
celltype.default_initial_values[
'v'] = U0 # Setting default init v, useful for NML2 export
E_net = Population(NE, celltype, label="E_net")
print("%d Creating inhibitory population with %d neurons." % (rank, NI))
I_net = Population(NI, celltype, label="I_net")
print(
"%d Initialising membrane potential to random values between %g mV and %g mV."
% (rank, U0, theta))
uniformDistr = RandomDistribution('uniform', low=U0, high=theta, rng=rng)
E_net.initialize(v=uniformDistr)
I_net.initialize(v=uniformDistr)
print("%d Creating excitatory Poisson generator with rate %g spikes/s." %
(rank, p_rate))
source_type = SpikeSourcePoisson(rate=p_rate)
expoisson = Population(NE, source_type, label="expoisson")
print("%d Creating inhibitory Poisson generator with the same rate." %
rank)
inpoisson = Population(NI, source_type, label="inpoisson")
# Record spikes
print("%d Setting up recording in excitatory population." % rank)
E_net.record('spikes')
if N_rec_v > 0:
E_net[0:min(NE, N_rec_v)].record('v')
print("%d Setting up recording in inhibitory population." % rank)
I_net.record('spikes')
if N_rec_v > 0:
I_net[0:min(NI, N_rec_v)].record('v')
progress_bar = ProgressBar(width=20)
connector = FixedProbabilityConnector(epsilon,
rng=rng,
callback=progress_bar)
E_syn = StaticSynapse(weight=JE, delay=delay)
I_syn = StaticSynapse(weight=JI, delay=delay)
ext_Connector = OneToOneConnector(callback=progress_bar)
ext_syn = StaticSynapse(weight=JE, delay=dt)
print(
"%d Connecting excitatory population with connection probability %g, weight %g nA and delay %g ms."
% (rank, epsilon, JE, delay))
E_to_E = Projection(E_net,
E_net,
connector,
E_syn,
receptor_type="excitatory")
print("E --> E\t\t", len(E_to_E), "connections")
I_to_E = Projection(I_net,
E_net,
connector,
I_syn,
receptor_type="inhibitory")
print("I --> E\t\t", len(I_to_E), "connections")
input_to_E = Projection(expoisson,
E_net,
ext_Connector,
ext_syn,
receptor_type="excitatory")
print("input --> E\t", len(input_to_E), "connections")
print(
"%d Connecting inhibitory population with connection probability %g, weight %g nA and delay %g ms."
% (rank, epsilon, JI, delay))
E_to_I = Projection(E_net,
I_net,
connector,
E_syn,
receptor_type="excitatory")
print("E --> I\t\t", len(E_to_I), "connections")
I_to_I = Projection(I_net,
I_net,
connector,
I_syn,
receptor_type="inhibitory")
print("I --> I\t\t", len(I_to_I), "connections")
input_to_I = Projection(inpoisson,
I_net,
ext_Connector,
ext_syn,
receptor_type="excitatory")
print("input --> I\t", len(input_to_I), "connections")
# read out time used for building
buildCPUTime = timer.elapsedTime()
# === Run simulation ===========================================================
# run, measure computer time
timer.start() # start timer on construction
print("%d Running simulation for %g ms (dt=%sms)." % (rank, simtime, dt))
run(simtime)
print("Done")
simCPUTime = timer.elapsedTime()
# write data to file
#print("%d Writing data to file." % rank)
#(E_net + I_net).write_data("Results/brunel_np%d_%s.pkl" % (np, simulator_name))
if save and not simulator_name == 'neuroml':
for pop in [E_net, I_net]:
io = PyNNTextIO(filename="brunel-PyNN-%s-%s-%i.gdf" %
(simulator_name, pop.label, rank))
spikes = pop.get_data('spikes', gather=False)
for segment in spikes.segments:
io.write_segment(segment)
io = PyNNTextIO(filename="brunel-PyNN-%s-%s-%i.dat" %
(simulator_name, pop.label, rank))
vs = pop.get_data('v', gather=False)
for segment in vs.segments:
io.write_segment(segment)
spike_data = {}
spike_data['senders'] = []
spike_data['times'] = []
index_offset = 1
for pop in [E_net, I_net]:
if rank == 0:
spikes = pop.get_data('spikes', gather=False)
#print(spikes.segments[0].all_data)
num_rec = len(spikes.segments[0].spiketrains)
print("Extracting spike info (%i) for %i cells in %s" %
(num_rec, pop.size, pop.label))
#assert(num_rec==len(spikes.segments[0].spiketrains))
for i in range(num_rec):
ss = spikes.segments[0].spiketrains[i]
for s in ss:
index = i + index_offset
#print("Adding spike at %s in %s[%i] (cell %i)"%(s,pop.label,i,index))
spike_data['senders'].append(index)
spike_data['times'].append(s)
index_offset += pop.size
#from IPython.core.debugger import Tracer
#Tracer()()
E_rate = E_net.mean_spike_count() * 1000.0 / simtime
I_rate = I_net.mean_spike_count() * 1000.0 / simtime
# write a short report
nprint("\n--- Brunel Network Simulation ---")
nprint("Nodes : %d" % np)
nprint("Number of Neurons : %d" % N)
nprint("Number of Synapses : %d" % Nsyn)
nprint("Input firing rate : %g" % p_rate)
nprint("Excitatory weight : %g" % JE)
nprint("Inhibitory weight : %g" % JI)
nprint("Excitatory rate : %g Hz" % E_rate)
nprint("Inhibitory rate : %g Hz" % I_rate)
nprint("Build time : %g s" % buildCPUTime)
nprint("Simulation time : %g s" % simCPUTime)
# === Clean up and quit ========================================================
end()
if simulator_name == 'neuroml' and jnml_simulator:
from pyneuroml import pynml
lems_file = 'LEMS_Sim_PyNN_NeuroML2_Export.xml'
print('Going to run generated LEMS file: %s on simulator: %s' %
(lems_file, jnml_simulator))
if jnml_simulator == 'jNeuroML':
results, events = pynml.run_lems_with_jneuroml(
lems_file,
nogui=True,
load_saved_data=True,
reload_events=True)
elif jnml_simulator == 'jNeuroML_NEURON':
results, events = pynml.run_lems_with_jneuroml_neuron(
lems_file,
nogui=True,
load_saved_data=True,
reload_events=True)
spike_data['senders'] = []
spike_data['times'] = []
for k in events.keys():
values = k.split('/')
index = int(
values[1]) if values[0] == 'E_net' else NE + int(values[1])
n = len(events[k])
print(
"Loading spikes for %s (index %i): [%s, ..., %s (n=%s)] sec" %
(k, index, events[k][0] if n > 0 else '-',
events[k][-1] if n > 0 else '-', n))
for t in events[k]:
spike_data['senders'].append(index)
spike_data['times'].append(t * 1000)
#print spike_data
return spike_data