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()
import numpy, pylab, random, sys
#import NeuroTools.signals as nt
import IPython
import pacman103.front.pynn as sim
# SpiNNaker setup
sim.setup(timestep=1.0, min_delay=1.0, max_delay=10.0)
# +-------------------------------------------------------------------+
# | General Parameters |
# +-------------------------------------------------------------------+
# Population parameters
model = sim.IF_curr_exp
cell_params = {'cm' : 0.25, # nF
'i_offset' : 0.0,
'tau_m' : 10.0,
'tau_refrac': 2.0,
'tau_syn_E' : 2.5,
'tau_syn_I' : 2.5,
'v_reset' : -70.0,
'v_rest' : -65.0,
'v_thresh' : -55.4
}
# Other simulation parameters
e_rate = 200
in_rate = 350
delta_t = -10
time_between_pairs = 150
"""
Synfirechain-like example
"""
#!/usr/bin/python
import pacman103.front.pynn as p
import visualiser.visualiser_modes as modes
import pylab
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
nNeurons = 200 # number of neurons in each population
p.set_number_of_neurons_per_core("IF_curr_exp", nNeurons / 2)
p.set_number_of_neurons_per_core("DelayExtension", nNeurons / 2)
cell_params_lif = {
'cm': 0.25, # nF
'i_offset': 0.0,
'tau_m': 20.0,
'tau_refrac': 2.0,
'tau_syn_E': 5.0,
'tau_syn_I': 5.0,
'v_reset': -70.0,
'v_rest': -65.0,
'v_thresh': -50.0
}
populations = list()
projections = list()
weight_to_spike = 2.0
delay = 17
"""
Synfirechain-like example
"""
#!/usr/bin/python
import pacman103.front.pynn as p
import pylab
p.setup(timestep=1.0, min_delay = 1.0, max_delay = 32.0)
p.set_number_of_neurons_per_core("IZK_curr_exp", 100)
nNeurons = 200 # number of neurons in each population
cell_params_izk = {
'a': 0.02,
'b': 0.2,
'c': -65,
'd': 8,
'v_init': -75,
'u_init': 0,
'tau_syn_E': 2,
'tau_syn_I': 2,
'i_offset': 0
}
populations = list()
projections = list()
weight_to_spike = 40
delay = 1
loopConnections = list()
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()
#import gaborcreatejose as retina_lib
#import gaussiancreatejose as retina_lib
FWD = 0
BWD = 1
input_size = 128 # Size of each population
subsample_size = 32
runtime = 60000 * 2
#runtime = 60
n_orientations = 4
size_gabor = 7
# Simulation Setup
p.setup(timestep=1.0, min_delay=1.0, max_delay=11.0
) # Will add some extra parameters for the spinnPredef.ini in here
p.set_number_of_neurons_per_core('IF_curr_exp',
128) # this will set one population per core
cell_params = {
'tau_m': 64,
'i_offset': 0,
'v_rest': -75,
'v_reset': -95,
'v_thresh': -40,
'tau_syn_E': 15,
'tau_syn_I': 15,
'tau_refrac': 2
}
"""
Test example, for getting PACMAN103 up and running
"""
#!/usr/bin/python
import pacman103.front.pynn as p
import numpy, pylab
import os
#p.setup(timestep=1.0, min_delay = 1.0, max_delay = 8.0, machine="amu12", output=os.getcwd(), db_name='simple_test.sqlite')
p.setup(timestep=1.0, min_delay = 1.0, max_delay = 8.0, machine="spinn-7", output=os.getcwd(), db_name='simple_test.sqlite')
n_pop = 16 # number of populations
nNeurons = 200 # number of neurons in each population
cell_params_lif_in = { 'tau_m' : 32,
'cm' : 0.35, # added to make PACMAN103 work
'v_init' : -80,
'v_rest' : -75,
'v_reset' : -95,
'v_thresh' : -55,
'tau_syn_E' : 5,
'tau_syn_I' : 10,
'tau_refrac' : 100,
'i_offset' : 1
}
cell_params_lif = { 'tau_m' : 32,
'cm' : 0.35, # added to make PACMAN103 work
'v_init' : -80,
'v_rest' : -70,
