# plasticity
wdep_grcpcsynapsis = p.AdditiveWeightDependence(w_min=0.0,
w_max=0.5,
A_plus=0.0015,
A_minus=0.0018)
tdep_grcpcsynapsis = p.SpikePairRuleSinAdd(tau_minus=50.,
tau_plus=50.,
delay=100.0,
nearest=False) # delay 70-100
stdp_grcpcsynapsis = p.STDPMechanism(timing_dependence=tdep_grcpcsynapsis,
weight_dependence=wdep_grcpcsynapsis,
voltage_dependence=None)
syndyn_grcpcsynapsis = p.SynapseDynamics(slow=stdp_grcpcsynapsis)
rng = p.NumpyRNG()
grcpc_weights_distribution = p.RandomDistribution('uniform', [0.05, 0.5], rng)
pro_grcpcsynapsis_connector = p.FixedProbabilityConnector(
0.8, weights=grcpc_weights_distribution)
pro_grcpcsynapsis_left = p.Projection(prepop,
postpop,
pro_grcpcsynapsis_connector,
target="excitatory",
synapse_dynamics=syndyn_grcpcsynapsis,
label="grcpcsynapsis")
proj = pro_grcpcsynapsis_left
proj.projection_edge.postvertex.custom_max_atoms_per_core = max(
1, 4000 / proj.projection_edge.prevertex.atoms)
#plasticsyn.projection_edge.postvertex.custom_max_atoms_per_core = 100
"""
#!/usr/bin/python
import pyNN.spiNNaker as p
import numpy, pylab
p.setup(timestep=1.0, min_delay=1.0, max_delay=8.0, db_name='synfire.sqlite')
n_pop = 16 # number of populations
nNeurons = 10 # number of neurons in each population
p.get_db().set_number_of_neurons_per_core(
'IF_curr_exp', nNeurons) # this will set one population per core
# random distributions
rng = p.NumpyRNG(seed=28374)
delay_distr = p.RandomDistribution('uniform', [1, 10], rng=rng)
weight_distr = p.RandomDistribution('uniform', [0, 2], rng=rng)
v_distr = p.RandomDistribution('uniform', [-55, -95], rng)
cell_params_lif_in = {
'tau_m': 32,
'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
}
stimI_pop = sim.Population(num_inh,
sim.SpikeSourceArray,
{'spike_times': inh_stim_times},
label="inh network stimulation")
stdp_model = sim.STDPMechanism(
timing_dependence=sim.SpikePairRule(tau_plus=tau_plus,
tau_minus=tau_minus,
nearest=True),
weight_dependence=sim.AdditiveWeightDependence(w_min=min_weight,
w_max=max_weight,
A_plus=a_plus,
A_minus=a_minus))
rng = sim.NumpyRNG(seed=int(time.time()))
#rng = sim.NumpyRNG(seed=1)
e2e_lst, e2i_lst, i2e_lst, i2i_lst = connections(max_conn_per_neuron, num_exc,
num_inh, max_delay)
e2e_conn = sim.FromListConnector(e2e_lst)
e2i_conn = sim.FromListConnector(e2i_lst)
i2e_conn = sim.FromListConnector(i2e_lst)
i2i_conn = sim.FromListConnector(i2i_lst)
o2o_conn = sim.OneToOneConnector(weights=weight_to_spike, delays=1.)
#~ print("-----------------------------------------------------------------")
#~ print("-----------------------------------------------------------------")
#~ print("Excitatory to Excitatory connections")
'i_offset': izk_types[inv_cell_type]['I'],
#'tau_syn_E': 2,
#'tau_syn_I': 2,
}
inj_cell_params = {
'port': 12345,
}
inj_cell_type = ExternalDevices.SpikeInjector
#~ num_inh = int(num_neurons*(1./4.))
rngseed = int(time.time())
#rngseed = 1
rng = sim.NumpyRNG(seed=rngseed)
neuron_model = sim.IZK_curr_exp
sim.set_number_of_neurons_per_core(neuron_model, 128)
sim.setup(timestep=timestep, min_delay = min_delay, max_delay = max_delay)
############ stdp
stdp_model = sim.STDPMechanism(
timing_dependence=sim.SpikePairRule(tau_plus=20., tau_minus=20.0,
nearest=True),
weight_dependence=sim.AdditiveWeightDependence(w_min=0.01, w_max=6.,
A_plus=0.02, A_minus=0.02)
)
############ random dist objects
"""
import pyNN.spiNNaker as p
import numpy, pylab
p.setup(timestep=1.0, min_delay=1.0, max_delay=8.0, db_name='synfire.sqlite')
n_pop = 64
nNeurons = 10
p.get_db().set_number_of_neurons_per_core(
'IZK_curr_exp', nNeurons) # this will set one population per core
#p.get_db().set_number_of_neurons_per_core('IF_curr_exp', 2*nNeurons) # this will set 2 populations per core
#p.get_db().set_number_of_neurons_per_core('IF_curr_exp', nNeurons/2) # this will set 1 population every 2 cores
rng = p.NumpyRNG(seed=28374)
rng1 = p.NumpyRNG(seed=12345)
delay_distr = p.RandomDistribution('uniform', [1, 5], rng)
weight_distr = p.RandomDistribution('uniform', [0, 2], rng1)
v_distr = p.RandomDistribution('uniform', [-85, -95], rng)
v_inits = []
for i in range(nNeurons):
v_inits.append(v_distr.next())
RS_params = {
'a': 0.02,
'b': 0.2,
'c': -70,
