def do_run():
# this test ensures there is too much dtcm used up, thus crashes during
# initisation
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
input = p.Population(1024, p.SpikeSourcePoisson, {'rate': 10}, "input")
relay_on = p.Population(1024, p.IF_curr_exp, {}, "input")
t_rule_LGN = p.SpikePairRule(tau_plus=17, tau_minus=34)
w_rule_LGN = p.AdditiveWeightDependence(w_min=0.0,
w_max=0.3,
A_plus=0.01,
A_minus=0.0085)
stdp_model_LGN = p.STDPMechanism(timing_dependence=t_rule_LGN,
weight_dependence=w_rule_LGN)
s_d_LGN = p.SynapseDynamics(slow=stdp_model_LGN)
p.Projection(input,
relay_on,
p.OneToOneConnector(weights=1),
synapse_dynamics=s_d_LGN,
target='excitatory')
p.run(1000)
p.end()
# +-------------------------------------------------------------------+
# | Initial network setup |
# +-------------------------------------------------------------------+
# Putting this populations on chip 0 1 makes it easier to copy the provenance
# data somewhere else
# target_pop.set_constraint(PlacerChipAndCoreConstraint(0, 1))
# Connections
# Plastic Connections between pre_pop and post_pop
stdp_model = sim.STDPMechanism(
timing_dependence=sim.SpikePairRule(tau_plus=tau_plus,
tau_minus=tau_minus),
weight_dependence=sim.AdditiveWeightDependence(
w_min=0,
w_max=g_max,
# A_plus=0.02, A_minus=0.02
A_plus=a_plus,
A_minus=a_minus))
if args.case == CASE_CORR_AND_REW:
structure_model_w_stdp = sim.StructuralMechanism(
stdp_model=stdp_model,
weight=g_max,
s_max=s_max,
grid=grid,
f_rew=f_rew,
lateral_inhibition=args.lateral_inhibition,
random_partner=args.random_partner,
p_elim_dep=p_elim_dep,
p_elim_pot=p_elim_pot,
sigma_form_forward=sigma_form_forward,
def run_multiple_stdp_mechs_on_same_neuron(self):
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
nNeurons = 200 # number of neurons in each population
cell_params_lif = {
'cm': 0.25,
'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 = 1
connections = list()
for i in range(0, nNeurons):
singleConnection = (i, ((i + 1) % nNeurons), weight_to_spike,
delay)
connections.append(singleConnection)
# Plastic Connection between pre_pop and post_pop
stdp_model1 = p.STDPMechanism(
timing_dependence=p.SpikePairRule(tau_plus=16.7, tau_minus=33.7),
weight_dependence=p.AdditiveWeightDependence(w_min=0.0,
w_max=1.0,
A_plus=0.005,
A_minus=0.005),
)
# Plastic Connection between pre_pop and post_pop
stdp_model2 = p.STDPMechanism(
timing_dependence=p.SpikePairRule(tau_plus=16.7, tau_minus=33.7),
weight_dependence=p.AdditiveWeightDependence(w_min=0.0,
w_max=1.0,
A_plus=0.005,
A_minus=0.005),
)
# Plastic Connection between pre_pop and post_pop
stdp_model3 = p.STDPMechanism(
timing_dependence=p.SpikePairRule(tau_plus=16.7, tau_minus=33.7),
weight_dependence=p.MultiplicativeWeightDependence(w_min=0.0,
w_max=1.0,
A_plus=0.005,
A_minus=0.005),
)
injectionConnection = [(0, 0, weight_to_spike, 1)]
spikeArray1 = {'spike_times': [[0]]}
spikeArray2 = {'spike_times': [[25]]}
spikeArray3 = {'spike_times': [[50]]}
spikeArray4 = {'spike_times': [[75]]}
spikeArray5 = {'spike_times': [[100]]}
spikeArray6 = {'spike_times': [[125]]}
spikeArray7 = {'spike_times': [[150]]}
spikeArray8 = {'spike_times': [[175]]}
populations.append(
p.Population(nNeurons,
p.IF_curr_exp,
cell_params_lif,
label='pop_1'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spikeArray1,
label='inputSpikes_1'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spikeArray2,
label='inputSpikes_2'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spikeArray3,
label='inputSpikes_3'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spikeArray4,
label='inputSpikes_4'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spikeArray5,
label='inputSpikes_5'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spikeArray6,
label='inputSpikes_6'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spikeArray7,
label='inputSpikes_7'))
populations.append(
p.Population(1,
p.SpikeSourceArray,
spikeArray8,
label='inputSpikes_8'))
projections.append(
p.Projection(populations[0], populations[0],
p.FromListConnector(connections)))
pop = p.Projection(populations[1], populations[0],
p.FromListConnector(injectionConnection))
projections.append(pop)
synapse_dynamics = p.SynapseDynamics(slow=stdp_model1)
pop = p.Projection(populations[2],
populations[0],
p.FromListConnector(injectionConnection),
synapse_dynamics=synapse_dynamics)
projections.append(pop)
# This is expected to raise a SynapticConfigurationException
synapse_dynamics = p.SynapseDynamics(slow=stdp_model2)
pop = p.Projection(populations[3],
populations[0],
p.FromListConnector(injectionConnection),
synapse_dynamics=synapse_dynamics)
projections.append(pop)
synapse_dynamics = p.SynapseDynamics(slow=stdp_model3)
pop = p.Projection(populations[4],
populations[0],
p.FromListConnector(injectionConnection),
synapse_dynamics=synapse_dynamics)
projections.append(pop)
def do_run():
# SpiNNaker setup
p.setup(timestep=1.0, min_delay=1.0, max_delay=10.0)
# +-------------------------------------------------------------------+
# | General Parameters |
# +-------------------------------------------------------------------+
# Population parameters
model = p.IF_curr_exp
# model = sim.IF_cond_exp
"""
cell_params = {'i_offset' : .1, 'tau_refrac' : 3.0, 'v_rest' : -65.0,
'v_thresh' : -51.0, 'tau_syn_E' : 2.0,
'tau_syn_I': 5.0, 'v_reset' : -70.0}
"""
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
n_stim_test = 5
n_stim_pairing = 10
dur_stim = 20
pop_size = 40
ISI = 150.
start_test_pre_pairing = 200.
start_pairing = 1500.
start_test_post_pairing = 700.
simtime = start_pairing + start_test_post_pairing + \
ISI * (n_stim_pairing + n_stim_test) + 550. # let's make it 5000
# Initialisations of the different types of populations
IAddPre = []
IAddPost = []
# +-------------------------------------------------------------------+
# | Creation of neuron populations |
# +-------------------------------------------------------------------+
# Neuron populations
pre_pop = p.Population(pop_size, model, cell_params)
post_pop = p.Population(pop_size, model, cell_params)
# Test of the effect of activity of the pre_pop population on the post_pop
# population prior to the "pairing" protocol : only pre_pop is stimulated
for i in range(n_stim_test):
IAddPre.append(
p.Population(
pop_size, p.SpikeSourcePoisson, {
'rate': in_rate,
'start': start_test_pre_pairing + ISI * (i),
'duration': dur_stim
}))
# Pairing protocol : pre_pop and post_pop are stimulated with a 10 ms
# difference
for i in range(n_stim_pairing):
IAddPre.append(
p.Population(
pop_size, p.SpikeSourcePoisson, {
'rate': in_rate,
'start': start_pairing + ISI * (i),
'duration': dur_stim
}))
IAddPost.append(
p.Population(
pop_size, p.SpikeSourcePoisson, {
'rate': in_rate,
'start': start_pairing + ISI * (i) + 10.,
'duration': dur_stim
}))
# Test post pairing : only pre_pop is stimulated
# (and should trigger activity in Post)
for i in range(n_stim_test):
start = start_pairing + ISI * n_stim_pairing + \
start_test_post_pairing + ISI * i
IAddPre.append(
p.Population(pop_size, p.SpikeSourcePoisson, {
'rate': in_rate,
'start': start,
'duration': dur_stim
}))
# Noise inputs
INoisePre = p.Population(pop_size,
p.SpikeSourcePoisson, {
'rate': e_rate,
'start': 0,
'duration': simtime
},
label="expoisson")
params = {'rate': e_rate, 'start': 0, 'duration': simtime}
INoisePost = p.Population(pop_size,
p.SpikeSourcePoisson,
params,
label="expoisson")
# +-------------------------------------------------------------------+
# | Creation of connections |
# +-------------------------------------------------------------------+
# Connection parameters
JEE = 3.
# Connection type between noise poisson generator and
# excitatory populations
ee_connector = p.OneToOneConnector(weights=JEE * 0.05)
# Noise projections
p.Projection(INoisePre, pre_pop, ee_connector, target='excitatory')
p.Projection(INoisePost, post_pop, ee_connector, target='excitatory')
# Additional Inputs projections
for i in range(len(IAddPre)):
p.Projection(IAddPre[i], pre_pop, ee_connector, target='excitatory')
for i in range(len(IAddPost)):
p.Projection(IAddPost[i], post_pop, ee_connector, target='excitatory')
# Plastic Connections between pre_pop and post_pop
timing_dependence = p.SpikePairRule(tau_plus=20., tau_minus=50.0)
weight_dependence = p.AdditiveWeightDependence(w_min=0,
w_max=0.9,
A_plus=0.02,
A_minus=0.02)
stdp_model = p.STDPMechanism(timing_dependence=timing_dependence,
weight_dependence=weight_dependence)
plastic_projection = \
p.Projection(pre_pop, post_pop,
p.FixedProbabilityConnector(p_connect=0.5),
synapse_dynamics=p.SynapseDynamics(slow=stdp_model)
)
# +-------------------------------------------------------------------+
# | Simulation and results |
# +-------------------------------------------------------------------+
# Record neurons' potentials
pre_pop.record_v()
post_pop.record_v()
# Record spikes
pre_pop.record()
post_pop.record()
# Run simulation
p.run(simtime)
weights = plastic_projection.getWeights()
pre_spikes = pre_pop.getSpikes(compatible_output=True)
post_spikes = post_pop.getSpikes(compatible_output=True)
p.end()
return (weights, pre_spikes, post_spikes)
#dummy_stim = sim.Population(1,sim.SpikeSourceArray,{'spike_times': [sim_time]},label="dummy")
nn_pc = 10 # number of purkinje cells (and inferior olive cells)
teaching_stim = sim.Population(nn_pc, sim.SpikeSourceArray,
{'spike_times': [[teaching_time+j*ts for j in range(i)] for i in range(nn_pc)]},label="Inferior Olives")
# Neuron populations
population = sim.Population(nn_pc, model, cell_params, label="Purkinjes")
population.record("spikes")
population2 = sim.Population(nn_pc, sim.IF_curr_exp, cell_params, label="fake purk")
population2.record("spikes")
# Plastic Connection between pre_pop and post_pop
stdp_model = sim.STDPMechanism(
timing_dependence = TimingDependenceCerebellum(tau=tau, peak_time=peak_time),
weight_dependence = sim.AdditiveWeightDependence(w_min=1.0, w_max=15.0, A_plus=0.5, A_minus=0.01)
)
#stdp_model = sim.STDPMechanism(
# timing_dependence = sim.SpikePairRule(tau_plus=tau, tau_minus=tau),
# weight_dependence = sim.AdditiveWeightDependence(w_min=1.0, w_max=15.0, A_plus=0.1, A_minus=0.1)
#)
# Connections between spike sources and neuron populations
####### SET HERE THE PARALLEL FIBER-PURKINJE CELL LEARNING RULE
ee_connector = sim.AllToAllConnector(weights=1.0)
projection_pf = sim.Projection(pre_stim, population, ee_connector,
synapse_dynamics=sim.SynapseDynamics(slow=stdp_model),
target='excitatory')
def do_run():
# 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, '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}
delta_t = 10
time_between_pairs = 150
num_pre_pairs = 10
num_pairs = 100
num_post_pairs = 10
pop_size = 1
pairing_start_time = (num_pre_pairs * time_between_pairs) + delta_t
pairing_end_time = pairing_start_time + (num_pairs * time_between_pairs)
sim_time = pairing_end_time + (num_post_pairs * time_between_pairs)
# +-------------------------------------------------------------------+
# | Creation of neuron populations |
# +-------------------------------------------------------------------+
# Neuron populations
pre_pop = sim.Population(pop_size, model, cell_params)
post_pop = sim.Population(pop_size, model, cell_params)
# Stimulating populations
pre_stim = sim.Population(pop_size, sim.SpikeSourceArray,
{'spike_times':
[[i for i in range(0, sim_time,
time_between_pairs)], ]})
post_stim = sim.Population(pop_size, sim.SpikeSourceArray,
{'spike_times':
[[i for i in range(pairing_start_time,
pairing_end_time,
time_between_pairs)], ]})
# +-------------------------------------------------------------------+
# | Creation of connections |
# +-------------------------------------------------------------------+
# Connection type between noise poisson generator and
# excitatory populations
ee_connector = sim.OneToOneConnector(weights=2)
sim.Projection(pre_stim, pre_pop, ee_connector, target='excitatory')
sim.Projection(post_stim, post_pop, ee_connector, target='excitatory')
# Plastic Connections between pre_pop and post_pop
stdp_model = sim.STDPMechanism(
timing_dependence=sim.SpikePairRule(tau_plus=20.0, tau_minus=50.0),
weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=1,
A_plus=0.02,
A_minus=0.02))
sim.Projection(pre_pop, post_pop, sim.OneToOneConnector(),
synapse_dynamics=sim.SynapseDynamics(slow=stdp_model))
# Record spikes
pre_pop.record()
post_pop.record()
# Run simulation
sim.run(sim_time)
pre_spikes = pre_pop.getSpikes(compatible_output=True)
post_spikes = post_pop.getSpikes(compatible_output=True)
# End simulation on SpiNNaker
sim.end()
return (pre_spikes, post_spikes)
def run_sim(num_pre, num_post, spike_times, run_time, weight=5.6, delay=40., gom=False,
conn_type='one2one', use_stdp=False, mad=False, prob=0.5):
model = p.IF_curr_exp
p.setup( timestep = 1.0, min_delay = 1.0, max_delay = 144.0 )
# if num_pre <= 10:
# p.set_number_of_neurons_per_core(model, 4)
# p.set_number_of_neurons_per_core(p.SpikeSourceArray, 5)
# elif 10 < num_pre <= 50:
# p.set_number_of_neurons_per_core(model, 20)
# p.set_number_of_neurons_per_core(p.SpikeSourceArray, 21)
# elif 50 < num_pre <= 100:
# p.set_number_of_neurons_per_core(model, 50)
# p.set_number_of_neurons_per_core(p.SpikeSourceArray, 51)
# else:
if use_stdp:
p.set_number_of_neurons_per_core(model, 150)
p.set_number_of_neurons_per_core(p.SpikeSourceArray, 2000)
cell_params_lif = { 'cm' : 1.0, # nF
'i_offset' : 0.00,
'tau_m' : 10.0,
'tau_refrac': 4.0,
'tau_syn_E' : 1.0,
'tau_syn_I' : 1.0,
'v_reset' : -70.0,
'v_rest' : -65.0,
'v_thresh' : -60.0
}
cell_params_pos = {
'spike_times': spike_times,
}
w2s = weight
dly = delay
rng = NumpyRNG( seed = 1 )
if use_stdp:
td = p.SpikePairRule(tau_minus=1., tau_plus=1.)
wd = p.AdditiveWeightDependence(w_min=0, w_max=20., A_plus=0.0, A_minus=0.0)
stdp = p.STDPMechanism(timing_dependence=td, weight_dependence=wd)
syn_dyn = p.SynapseDynamics(slow=stdp)
else:
syn_dyn = None
sink = p.Population( num_post, model, cell_params_lif, label='sink')
# sink1 = p.Population( nNeuronsPost, model, cell_params_lif, label='sink1')
source0 = p.Population( num_pre, p.SpikeSourceArray, cell_params_pos,
label='source_0')
# source1 = p.Population( nNeurons, p.SpikeSourceArray, cell_params_pos,
# label='source_1')
if conn_type == 'one2one':
conn = p.OneToOneConnector(weights=w2s, delays=dly, generate_on_machine=gom)
elif conn_type == 'all2all':
conn = p.AllToAllConnector(weights=w2s, delays=dly, generate_on_machine=gom)
elif conn_type == 'fixed_prob':
conn = p.FixedProbabilityConnector(prob, weights=w2s, delays=dly,
generate_on_machine=gom)
else:
raise Exception("Not a valid connector for test")
proj = p.Projection( source0, sink, conn, target='excitatory',
synapse_dynamics=syn_dyn,
label=' source 0 to sink - EXC - delayed')
# sink.record_v()
# sink.record_gsyn()
sink.record()
print("Running for {} ms".format(run_time))
t0 = time.time()
p.run(run_time)
time_to_run = time.time() - t0
v = None
gsyn = None
spikes = None
# v = np.array(sink.get_v(compatible_output=True))
# gsyn = sink.get_gsyn(compatible_output=True)
spikes = sink.getSpikes(compatible_output=True)
w = proj.getWeights(format='array')
p.end()
return v, gsyn, spikes, w, time_to_run
for inpop, outpop in zip(PuC_layer_pop, DCN_layer_pop):
sim.Projection(inpop,
outpop,
sim.FromListConnector(connlist_PuC_DCN),
target="inhibitory",
label="proj_" + inpop.label + "_" + outpop.label)
pass
## Plastic synapses (GrC to PuC)
tau = 60. #450. # 60.
peak_time = 100. #100.
# Create cerebellar learning rule
stdp_model = sim.STDPMechanism(
timing_dependence=TimingDependenceCerebellum(tau=tau, peak_time=peak_time),
weight_dependence=sim.AdditiveWeightDependence(w_min=0.0,
w_max=W_MAX,
A_plus=0.01,
A_minus=0.01))
#weight_dependence = sim.AdditiveWeightDependence(w_min=0.0, w_max=W_MAX, A_plus=0.0015, A_minus=0.0020)
weigth_first = []
proj_GrC_PuC = []
for outpop in PuC_layer_pop:
proj_GrC_PuC.append(
sim.Projection(
GrC_layer_pop[0],
outpop,
sim.FixedProbabilityConnector(1.0, weights=weight_dist_GrC_PuC),
synapse_dynamics=sim.SynapseDynamics(slow=stdp_model),
target="excitatory",
label="proj_" + GrC_layer_pop[0].label + "_" + outpop.label))
weigth_first.append(proj_GrC_PuC[-1].getWeights())