def testFixedProbability(self):
"""For all connections created with "fixedProbability"..."""
for srcP in [self.source5, self.source22]:
for tgtP in [self.target1, self.target6, self.target33]:
prj1 = sim.Projection(srcP,
tgtP,
sim.FixedProbabilityConnector(0.5),
rng=random.NumpyRNG(12345))
prj2 = sim.Projection(srcP,
tgtP,
sim.FixedProbabilityConnector(0.5),
rng=random.NativeRNG(12345))
for prj in prj1, prj2:
assert (0 < len(prj) < len(srcP) * len(tgtP)
), 'len(prj) = %d, len(srcP)*len(tgtP) = %d' % (
len(prj), len(srcP) * len(tgtP))
facilitating_synapse_ee = sim.TsodyksMarkramSynapse(weight=0.05,
delay=0.5,
U=0.04,
tau_rec=100.0,
tau_facil=1000)
static_synapse = sim.StaticSynapse(weight=0.05, delay=0.5)
Input_E_connection = sim.Projection(Excinp,
Pexc,
sim.AllToAllConnector(),
static_synapse,
receptor_type='excitatory')
E_E_connection = sim.Projection(Pexc,
Pexc,
sim.FixedProbabilityConnector(p_connect=0.5),
depressing_synapse_ee,
receptor_type='excitatory')
Excinp.record('spikes')
# Excinp[1].record('v')
Pexc.record('spikes')
Pexc[5:6].record('v')
for i in range(no_run):
sim.run(run_time)
spikes = Excinp.get_data()
spike = Pexc.get_data()
# print connection.get('weight',format = 'array')
# print E_E_connection.get('weight', format = 'array')
# print connection.get('tau_facil', format = 'array')
# todo: the Population structure
# ==========create neuron connection======================
# the network topology is based on 'compensating inhomogeneities of neuromorphic VLSI devices via short-term synaptic plasticity'
# todo: generate a concrete connection probability and weight (not chaged for each run)
# connection probability
# todo: connection probability based on position
P_c = 0.1 # scaling factor of connection probability; make sure the largest p (i.e. p_ii if (N_e > N_i)) is smaller than 1
p_ee = P_c
p_ie = 2 * P_c
p_ei = N_e / N_i * P_c
p_ii = 2 * N_e / N_i * P_c
connector_ee = sim.FixedProbabilityConnector(
p_connect=p_ee) # connector algorithm
connector_ie = sim.FixedProbabilityConnector(p_connect=p_ie)
connector_ei = sim.FixedProbabilityConnector(p_connect=p_ei)
connector_ii = sim.FixedProbabilityConnector(p_connect=p_ii)
# connection weight
# todo: weight distribution according to position
W_c = 0.5 # scaling factor of weight
w_ee = W_c * 1
w_ie = 0.5 * W_c * 0.1
w_ei = W_c * 3
w_ii = 0.5 * W_c * 0.5
# type of synaptic connection
depressing_synapse_ee = sim.TsodyksMarkramSynapse(weight=w_ee,
delay=0.2,
weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=0.04),
dendritic_delay_fraction=0)
#Error: The pyNN.brian backend does not currently support dendritic delays:
# for the purpose of STDP calculations all delays are assumed to be axonal
#for brian dendritic_delay_fraction=0 default value 1.0
'''
Connection algorithms
'''
connector = sim.AllToAllConnector(allow_self_connections=False) # no autapses
#default True
connector = sim.OneToOneConnector()
#Connecting neurons with a fixed probability
connector = sim.FixedProbabilityConnector(p_connect=0.2)
#Connecting neurons with a position-dependent probability
DDPC = sim.DistanceDependentProbabilityConnector
connector = DDPC("exp(-d)")
connector = DDPC("d<3")
#The constructor requires a string d_expression, which should be a distance expression,
# as described above for delays, but returning a probability (a value between 0 and 1)
#Divergent/fan-out connections
#connects each pre-synaptic neuron to exactly n post-synaptic neurons chosen at random
connector = sim.FixedNumberPostConnector(n=30)
distr_npost = RandomDistribution(distribution='binomial', n=100, p=0.3)
connector = sim.FixedNumberPostConnector(n=distr_npost)
Pexc = sim.Population(
N_e, cell_type, label="excitotary neurons") # excitatory neuron population
# todo: the Population structure
# ==========create neuron connection======================
# the network topology is based on 'compensating inhomogeneities of neuromorphic VLSI devices via short-term synaptic plasticity'
# todo: generate a concrete connection probability and weight (not chaged for each run)
# connection probability
# todo: connection probability based on position
P_c = 0.1 # scaling factor of connection probability; make sure the largest p (i.e. p_ii if (N_e > N_i)) is smaller than 1
p_ee = P_c
connector_ee = sim.FixedProbabilityConnector(
p_connect=p_ee) # connector algorithm
# connection weight
# todo: weight distribution according to position
W_c = 0.5 # scaling factor of weight
w_ee = W_c * 1
# type of synaptic connection
# syn = sim.StaticSynapse(weight = 0.05, delay = 0.5)
depressing_synapse_ee = sim.TsodyksMarkramSynapse(weight=w_ee,
delay=0.2,
U=0.5,
tau_rec=800.0,
tau_facil=0.01)
# connect the neuronal network
# todo: the initail parameters of neurons might be modified
cell_type_parameters = {'tau_refrac': 0.1, 'v_thresh': -50.0, 'tau_m': 20.0, 'tau_syn_E': 0.5, 'v_rest': -65.0,\
'cm': 1.0, 'v_reset': -65.0, 'tau_syn_I': 0.5, 'i_offset': 0.0}
# print(sim.IF_curr_alpha.default_parameters)
cell_type = sim.IF_cond_exp(**cell_type_parameters) # neuron type of population
Pexc = sim.Population(N_e, cell_type, label = "excitotary neurons") # excitatory neuron population
Pinh = sim.Population(N_i, cell_type, label = "inhibitatory neurons") # inhibitoty neuron population
all_cells = sim.Assembly(Pexc, Pinh) # assembly for all neuron population for the purpose of data recording
# todo: the Population structure
# ==========injecting neuron currents OR connect to the input signal=======================
syn = sim.StaticSynapse(weight = weight_inp[0], delay = 0.5)
Ie_E_connections = sim.Projection(Excinp, Pexc, sim.FixedProbabilityConnector(p_connect = 0.5), syn, receptor_type = 'excitatory', label = "excitatory input to excitatory neurons")
Ii_E_connections = sim.Projection(Inhinp, Pexc, sim.FixedProbabilityConnector(p_connect = 0.5), syn, receptor_type = 'inhibitory', label = "inhibitory input to excitatory neurons")
Ie_I_connections = sim.Projection(Excinp, Pinh, sim.FixedProbabilityConnector(p_connect = 0.5), syn, receptor_type = 'excitatory', label = "excitatory input to inhibitory neurons")
Ii_I_connections = sim.Projection(Inhinp, Pinh, sim.FixedProbabilityConnector(p_connect = 0.5), syn, receptor_type = 'inhibitory', label = "inhibitory input to inhibitory neurons")
# ==========define the parameter need to be record=================
all_cells.record('spikes')
# Excinp.record('spikes')
for i, j in enumerate(weight_inp):
Ie_E_connections.set(weight = j)
Ii_E_connections.set(weight = j)
Ie_I_connections.set(weight = j)
Ii_E_connections.set(weight = j)
sim.run(run_time)
# ==========retrieve the data======================
# todo: the initail parameters of neurons might be modified
cell_type_parameters = {'tau_refrac': 0.1, 'v_thresh': -50.0, 'tau_m': 20.0, 'tau_syn_E': 0.5, 'v_rest': -65.0,\
'cm': 1.0, 'v_reset': -65.0, 'tau_syn_I': 0.5, 'i_offset': 0.0}
# print(sim.IF_curr_alpha.default_parameters)
cell_type = sim.IF_cond_exp(**cell_type_parameters) # neuron type of population
Pexc = sim.Population(3, cell_type, label = "excitotary neurons") # excitatory neuron population
# ==========generate OR read in the input spikes data=====================
noSpikes = 20 # number of spikes per chanel per simulation run
stimSpikes = RandomDistribution('uniform', low = 0, high = 500, rng = NumpyRNG(seed = 72386)).next(noSpikes) # generate a time uniform distributed signal with Exc_in + Inh_in chanels and noSpikes for each chanel
Excinp = sim.Population(3, sim.SpikeSourceArray(spike_times = stimSpikes))
syn = sim.StaticSynapse(weight = 0.05, delay = 0.5)
Ie_A_connections = sim.Projection(Excinp, Pexc, sim.FixedProbabilityConnector(p_connect = 0.5), syn, receptor_type = 'excitatory', label = "excitatory input")
for i in range(ite_no):
sim.run(100)
# ==========write the data======================
sim.reset()
Ie_A_connections.set(weight = (i + 2) * 0.05)
print Ie_A_connections.get('weight', format = 'list')
# Ie_A_connections.set(weight = 0.02)
# print Ie_A_connections.get('weight', format = 'list')
# sim.run(200)
# sim.reset()
