def test_get_weights(self):
# Population parameters
cell_params = {
'cm': 0.2, # nF
'i_offset': 0.2,
'tau_m': 20.0,
'tau_refrac': 5.0,
'tau_syn_E': 5.0,
'tau_syn_I': 10.0,
'v_reset': -60.0,
'v_rest': -60.0,
'v_thresh': -50.0
}
# Reduce number of neurons to simulate on each core
sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 10)
# Build inhibitory plasticity model
stdp_model = sim.STDPMechanism(
timing_dependence=sim.SpikePairRule(tau_plus=20.0,
tau_minus=12.7,
nearest=True),
weight_dependence=sim.AdditiveWeightDependence(w_min=0.0,
w_max=1.0,
A_plus=0.05),
mad=True)
# Build plastic network
plastic_ex_pop, plastic_ie_projection =\
self.build_network(sim.SynapseDynamics(slow=stdp_model),
cell_params)
# Run simulation
sim.run(1000)
# Get plastic spikes and save to disk
plastic_spikes = plastic_ex_pop.getSpikes(compatible_output=True)
#numpy.save("plastic_spikes.npy", plastic_spikes)
plastic_weights = plastic_ie_projection.getWeights(format="array")
# mean_weight = numpy.average(plastic_weights)
# End simulation on SpiNNaker
sim.end()
tau_x=101,
tau_y=114),
weight_dependence=sim.AdditiveWeightDependence(
w_min=0.0,
w_max=1.0,
A_plus=param_scale * 0.0,
A_minus=param_scale * 7.1e-3,
A3_plus=param_scale * 6.5e-3,
A3_minus=param_scale * 0.0))
projections[-1].append(
sim.Projection(
pre_pop,
post_pop,
sim.OneToOneConnector(weights=start_w),
synapse_dynamics=sim.SynapseDynamics(slow=stdp_model)))
print("Simulating for %us" % (sim_time / 1000))
# Run simulation
sim.run(sim_time)
# Read weights from each parameter value being tested
weights = []
for projection_delta_t in projections:
weights.append([p.getWeights()[0] for p in projection_delta_t])
# End simulation on SpiNNaker
sim.end(stop_on_board=True)
#-------------------------------------------------------------------
def start_sim(self,sim_time):
#simulation setup
self.simtime = sim_time
sim.setup(timestep=self.setup_cond["timestep"], min_delay=self.setup_cond["min_delay"], max_delay=self.setup_cond["max_delay"])
#initialise the neuron population
spikeArrayOn = {'spike_times': self.in_spike}
pre_pop = sim.Population(self.pre_pop_size, sim.SpikeSourceArray,
spikeArrayOn, label='inputSpikes_On')
post_pop= sim.Population(self.post_pop_size,sim.IF_curr_exp,
self.cell_params_lif, label='post_1')
stdp_model = sim.STDPMechanism(timing_dependence=sim.SpikePairRule(tau_plus= self.stdp_param["tau_plus"],
tau_minus= self.stdp_param["tau_minus"],
nearest=True),
weight_dependence=sim.MultiplicativeWeightDependence(w_min= self.stdp_param["w_min"],
w_max= self.stdp_param["w_max"],
A_plus= self.stdp_param["A_plus"],
A_minus= self.stdp_param["A_minus"]))
#initialise connectiviity of neurons
#exitatory connection between pre-synaptic and post-synaptic neuron population
if(self.inhibitory_spike_mode):
connectionsOn = sim.Projection(pre_pop, post_pop, sim.AllToAllConnector(weights = self.I_syn_weight,delays=1,
allow_self_connections=False),
target='inhibitory')
else:
if(self.STDP_mode):
if(self.allsameweight):
connectionsOn = sim.Projection(pre_pop, post_pop,
sim.AllToAllConnector(weights = self.E_syn_weight,delays=1),
synapse_dynamics=sim.SynapseDynamics(slow=stdp_model),target='excitatory')
else:
connectionsOn = sim.Projection(pre_pop, post_pop,
sim.FromListConnector(self.conn_list),
synapse_dynamics=sim.SynapseDynamics(slow=stdp_model),target='excitatory')
else:
if(self.allsameweight):
connectionsOn = sim.Projection(pre_pop, post_pop, sim.AllToAllConnector(weights = self.E_syn_weight,delays=1),
target='excitatory')
else:
connectionsOn = sim.Projection(pre_pop, post_pop, sim.FromListConnector(self.conn_list),
target='excitatory')
#sim.Projection.setWeights(self.E_syn_weight)
#inhibitory between the neurons post-synaptic neuron population
connection_I = sim.Projection(post_pop, post_pop, sim.AllToAllConnector(weights = self.I_syn_weight,delays=1,
allow_self_connections=False),
target='inhibitory')
pre_pop.record()
post_pop.record()
post_pop.record_v()
sim.run(self.simtime)
self.pre_spikes = pre_pop.getSpikes(compatible_output=True)
self.post_spikes = post_pop.getSpikes(compatible_output=True)
self.post_spikes_v = post_pop.get_v(compatible_output=True)
self.trained_weights = connectionsOn.getWeights(format='array')
sim.end()
#print self.conn_list
#print self.trained_weights
'''scipy.io.savemat('trained_weight.mat',{'initial_weight':self.init_weights,
'trained_weight':self.trained_weights
})'''
scipy.io.savemat('trained_weight0.mat',{'trained_weight':self.trained_weights
})
MyModelCurrExp, {
"my_parameter": -70.0,
"i_offset": i_offset,
},
label="my_model_pop")
stdp = p.STDPMechanism(timing_dependence=MyTimingDependence(
my_potentiation_parameter=2.0, my_depression_parameter=0.1),
weight_dependence=MyWeightDependence(w_min=0.0,
w_max=10.0,
my_parameter=0.5),
mad=True)
p.Projection(input_pop, my_model_stdp_pop, p.OneToOneConnector(weights=weight))
stdp_connection = p.Projection(input_pop,
my_model_stdp_pop,
p.OneToOneConnector(weights=0),
synapse_dynamics=p.SynapseDynamics(slow=stdp))
my_model_pop.record_v()
my_model_my_synapse_type_pop.record_v()
my_model_my_additional_input_pop.record_v()
my_model_my_threshold_pop.record_v()
p.run(run_time)
print stdp_connection.getWeights()
create_v_graph(my_model_pop, "My Model")
create_v_graph(my_model_my_synapse_type_pop, "My Model with My Synapse Type")
create_v_graph(my_model_my_additional_input_pop,
"My Model with My Additional Input")
create_v_graph(my_model_my_threshold_pop, "My Model with My Threshold")
import spynnaker.pyNN as p
p.setup(timestep=1.0, min_delay=1.0, max_delay=144.0)
nNeurons = 100
#p.set_number_of_neurons_per_core("IF_curr_exp", nNeurons)
input = p.Population(1024, p.SpikeSourcePoisson, {'rate': 10}, "input")
relay_on = p.Population(1024, p.IF_curr_exp, {}, "input")
weight_to_spike = 2.0
delay = 17
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)
in_to_relay_on = p.Projection(input, relay_on, p.OneToOneConnector(weights=1),synapse_dynamics = s_d_LGN, target='excitatory')
p.run(1000)
p.end()
# | Creation of connections |
# +-------------------------------------------------------------------+
# Connection type between noise poisson generator and excitatory populations
ee_connector = p.OneToOneConnector(weights=2)
p.Projection(pre_stim, pre_pop, ee_connector, target='excitatory')
p.Projection(post_stim, post_pop, ee_connector, target='excitatory')
# Plastic Connections between pre_pop and post_pop
stdp_model = p.STDPMechanism(
timing_dependence = p.SpikePairRule(tau_plus = 20.0, tau_minus = 50.0),
weight_dependence = p.AdditiveWeightDependence(w_min = 0, w_max = 1, A_plus=0.02, A_minus = 0.02)
)
p.Projection(pre_pop, post_pop, p.OneToOneConnector(),
synapse_dynamics = p.SynapseDynamics(slow= stdp_model)
)
# Record spikes
pre_pop.record()
post_pop.record()
# Run simulation
p.run(sim_time)
# 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")
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)))
projections.append(
p.Projection(populations[1], populations[0],
p.FromListConnector(injectionConnection)))
projections.append(
p.Projection(populations[2],
populations[0],
p.FromListConnector(injectionConnection),
synapse_dynamics=p.SynapseDynamics(slow=stdp_model1)))
projections.append(
p.Projection(populations[3],
populations[0],
p.FromListConnector(injectionConnection),
synapse_dynamics=p.SynapseDynamics(slow=stdp_model2)))
projections.append(
p.Projection(populations[4],
populations[0],
p.FromListConnector(injectionConnection),
synapse_dynamics=p.SynapseDynamics(slow=stdp_model3)))
# currently only slow SynapseDynamics are supported, therefore fast ones need to
# be removed
#projections.append(p.Projection(populations[5], populations[0],
# p.FromListConnector(injectionConnection),
# synapse_dynamics=
