def test_lif_current(self):
"""
Simulates one LIF neuron and checks the membrane-potential trace matches
reference data.
TODO add assertion on comparison, rather than just graphing the result.
TODO take parameter for machine hostname.
"""
print "test_lif_current..."
pynn.setup(**{'machine':'bluu', 'debug':False}) #TODO take machine parameter
lif = {"v_rest":-70.0, "v_reset":-70.0, "v_thresh":-50.0, # mV
"tau_m":40.0, "tau_syn_E":20.0, "tau_syn_I":5.0, # mS
"tau_refrac":1.0, "cm":40.0/50.0, "i_offset":0.0} # ms, nF, nA
pop = pynn.Population(1, pynn.IF_curr_exp, lif)
pop.set("i_offset", 0.5)
pop.record_v()
pynn.run(1000)
with open('data/test_lif_current.pickle', 'r') as f:
data = f.read()
trace = pickle.loads(data)
fig = pylab.figure(figsize=(8.0, 5.7))
ax = fig.add_subplot(1,1,1)
ax.plot(pop.get_v()[:,2])
ax.plot(trace)
pylab.show()
pynn.end()
def test_lif_current(self):
"""
Simulates one LIF neuron and checks the membrane-potential trace matches
reference data.
TODO add assertion on comparison, rather than just graphing the result.
TODO take parameter for machine hostname.
"""
print "test_lif_current..."
pynn.setup(**{
'machine': 'bluu',
'debug': False
}) #TODO take machine parameter
lif = {
"v_rest": -70.0,
"v_reset": -70.0,
"v_thresh": -50.0, # mV
"tau_m": 40.0,
"tau_syn_E": 20.0,
"tau_syn_I": 5.0, # mS
"tau_refrac": 1.0,
"cm": 40.0 / 50.0,
"i_offset": 0.0
} # ms, nF, nA
pop = pynn.Population(1, pynn.IF_curr_exp, lif)
pop.set("i_offset", 0.5)
pop.record_v()
pynn.run(1000)
with open('data/test_lif_current.pickle', 'r') as f:
data = f.read()
trace = pickle.loads(data)
fig = pylab.figure(figsize=(8.0, 5.7))
ax = fig.add_subplot(1, 1, 1)
ax.plot(pop.get_v()[:, 2])
ax.plot(trace)
pylab.show()
pynn.end()
def test_pynn_basic(self):
pass
"""
TODO figure out how to take machine name as a parameter
"""
print "test_pynn_basic setup....."
pynn.setup(**{'machine': 'spinn-7'})
n_atoms = 10
proj1_params = {
'source': None,
'delays': 1,
'weights': 1,
'target': 'excitatory'
}
proj2_params = {
'allow_self_connections': True,
'delays': 2,
'p_connect': 0.1,
'weights': 2,
'source': None,
'target': None
}
cell_params = {
'tau_m': 64,
'v_init': -75,
'i_offset': 0,
'v_rest': -75,
'v_reset': -95,
'v_thresh': -40,
'tau_syn_E': 15,
'tau_syn_I': 15,
'tau_refrac': 10
}
pop1 = pynn.Population(n_atoms,
pynn.IF_curr_exp,
cell_params,
label='pop1')
pop2 = pynn.Population(n_atoms * 2,
pynn.IF_curr_exp,
cell_params,
label='pop2')
proj1 = pynn.Projection(pop1,
pop2,
pynn.OneToOneConnector(weights=1, delays=1),
target='excitatory')
proj2 = pynn.Projection(
pop1, pop2,
pynn.FixedProbabilityConnector(weights=2, delays=2, p_connect=.1))
pynn.controller.map_model() # at this stage the model is mapped
print "checking vertices..."
self.assertEqual(pop1.vertex.parameters, cell_params)
self.assertEqual(pynn.controller.dao.vertices[1].parameters,
cell_params)
self.assertEqual(pop1.vertex.model,
pacman103.front.pynn.models.IF_curr_exp)
self.assertEqual(pynn.controller.dao.vertices[0].label, "pop1")
self.assertEqual(pynn.controller.dao.vertices[1].label, "pop2")
self.assertEqual(pop1.vertex.atoms, n_atoms)
self.assertEqual(pynn.controller.dao.vertices[1].atoms, n_atoms * 2)
print "checking edges..."
self.assertEqual(pynn.controller.dao.edges[0].model,
pacman103.front.pynn.connectors.OneToOneConnector)
self.assertEqual(
proj2.edge.model,
pacman103.front.pynn.connectors.FixedProbabilityConnector)
self.assertEqual(proj1.edge.parameters, proj1_params)
self.assertEqual(pynn.controller.dao.edges[1].parameters, proj2_params)
# testing the mapping phase
self.assertEqual(len(pynn.controller.dao.subedges), 2)
self.assertEqual(len(pynn.controller.dao.subvertices), 2)
self.assertEqual(len(pynn.controller.dao.routings), 2)
# pynn.run(100)
pynn.end()
# Dump data
#pre_pop.printSpikes("results/stdp_pre.spikes")
#post_pop.printSpikes("results/stdp_post.spikes")
#pre_pop.print_v("results/stdp_pre.v")
#post_pop.print_v("results/stdp_post.v")
def plot_spikes(spikes, title):
if spikes != None:
pylab.figure()
pylab.xlim((0, sim_time))
pylab.plot([i[1] for i in spikes], [i[0] for i in spikes], ".")
pylab.xlabel('Time/ms')
pylab.ylabel('spikes')
pylab.title(title)
else:
print "No spikes received"
pre_spikes = pre_pop.getSpikes(compatible_output=True)
post_spikes = post_pop.getSpikes(compatible_output=True)
plot_spikes(pre_spikes, "pre-synaptic")
plot_spikes(post_spikes, "post-synaptic")
IPython.embed()
pylab.show()
# End simulation on SpiNNaker
sim.end()
pylab.show()
else:
print "No spikes received"
# Make some graphs
if v != None:
ticks = len(v) / nNeurons
pylab.figure()
pylab.xlabel('Time/ms')
pylab.ylabel('v')
pylab.title('v')
for pos in range(0, nNeurons, 20):
v_for_neuron = v[pos * ticks:(pos + 1) * ticks]
pylab.plot([i[1] for i in v_for_neuron], [i[2] for i in v_for_neuron])
pylab.show()
if gsyn != None:
ticks = len(gsyn) / nNeurons
pylab.figure()
pylab.xlabel('Time/ms')
pylab.ylabel('gsyn')
pylab.title('gsyn')
for pos in range(0, nNeurons, 20):
gsyn_for_neuron = gsyn[pos * ticks:(pos + 1) * ticks]
pylab.plot([i[1] for i in gsyn_for_neuron],
[i[2] for i in gsyn_for_neuron])
pylab.show()
p.end()
spikes = populations[0].getSpikes(compatible_output=True)
if spikes != None:
pylab.figure()
pylab.plot([i[1] for i in spikes], [i[0] for i in spikes], ".")
pylab.xlabel('Time/ms')
pylab.ylabel('spikes')
pylab.title('spikes')
pylab.show()
else:
print "No spikes received"
# Make some graphs
ticks = len(v) / nNeurons
if v != None:
pylab.figure()
pylab.xlabel('Time/ms')
pylab.ylabel('v')
pylab.title('v')
print v
for pos in range(0, nNeurons, 20):
#for pos in range(0, nNeurons):
v_for_neuron = v[pos * ticks : (pos + 1) * ticks]
print v_for_neuron
pylab.plot([i[1] for i in v_for_neuron],
[i[2] for i in v_for_neuron])
pylab.show()
p.end()
def test_pynn_basic(self):
pass
"""
TODO figure out how to take machine name as a parameter
"""
print "test_pynn_basic setup....."
pynn.setup(**{"machine": "spinn-7"})
n_atoms = 10
proj1_params = {"source": None, "delays": 1, "weights": 1, "target": "excitatory"}
proj2_params = {
"allow_self_connections": True,
"delays": 2,
"p_connect": 0.1,
"weights": 2,
"source": None,
"target": None,
}
cell_params = {
"tau_m": 64,
"v_init": -75,
"i_offset": 0,
"v_rest": -75,
"v_reset": -95,
"v_thresh": -40,
"tau_syn_E": 15,
"tau_syn_I": 15,
"tau_refrac": 10,
}
pop1 = pynn.Population(n_atoms, pynn.IF_curr_exp, cell_params, label="pop1")
pop2 = pynn.Population(n_atoms * 2, pynn.IF_curr_exp, cell_params, label="pop2")
proj1 = pynn.Projection(pop1, pop2, pynn.OneToOneConnector(weights=1, delays=1), target="excitatory")
proj2 = pynn.Projection(pop1, pop2, pynn.FixedProbabilityConnector(weights=2, delays=2, p_connect=0.1))
pynn.controller.map_model() # at this stage the model is mapped
print "checking vertices..."
self.assertEqual(pop1.vertex.parameters, cell_params)
self.assertEqual(pynn.controller.dao.vertices[1].parameters, cell_params)
self.assertEqual(pop1.vertex.model, pacman103.front.pynn.models.IF_curr_exp)
self.assertEqual(pynn.controller.dao.vertices[0].label, "pop1")
self.assertEqual(pynn.controller.dao.vertices[1].label, "pop2")
self.assertEqual(pop1.vertex.atoms, n_atoms)
self.assertEqual(pynn.controller.dao.vertices[1].atoms, n_atoms * 2)
print "checking edges..."
self.assertEqual(pynn.controller.dao.edges[0].model, pacman103.front.pynn.connectors.OneToOneConnector)
self.assertEqual(proj2.edge.model, pacman103.front.pynn.connectors.FixedProbabilityConnector)
self.assertEqual(proj1.edge.parameters, proj1_params)
self.assertEqual(pynn.controller.dao.edges[1].parameters, proj2_params)
# testing the mapping phase
self.assertEqual(len(pynn.controller.dao.subedges), 2)
self.assertEqual(len(pynn.controller.dao.subvertices), 2)
self.assertEqual(len(pynn.controller.dao.routings), 2)
# pynn.run(100)
pynn.end()
