def structural_eliminate_to_empty():
p.setup(1.0)
stim = p.Population(9, p.SpikeSourceArray(range(10)), label="stim")
# These populations should experience elimination
pop = p.Population(9, p.IF_curr_exp(), label="pop")
# Make a full list
# Elimination with random selection (0 probability formation)
proj = p.Projection(
stim, pop, p.AllToAllConnector(),
p.StructuralMechanismStatic(
partner_selection=p.RandomSelection(),
formation=p.DistanceDependentFormation([3, 3], 0.0),
elimination=p.RandomByWeightElimination(4.0, 1.0, 1.0),
f_rew=1000,
initial_weight=4.0,
initial_delay=3.0,
s_max=9,
seed=0,
weight=0.0,
delay=1.0))
pop.record("rewiring")
p.run(1000)
# Get the final connections
conns = list(proj.get(["weight", "delay"], "list"))
rewiring = pop.get_data("rewiring")
formation_events = rewiring.segments[0].events[0]
elimination_events = rewiring.segments[0].events[1]
num_forms = len(formation_events.times)
num_elims = len(elimination_events.times)
first_elim = elimination_events.labels[0]
p.end()
# These should have no connections since all should be eliminated
assert (len(conns) == 0)
assert (num_elims == 81)
assert (num_forms == 0)
assert (first_elim == "7_5_elimination")
def structural_formation_to_full():
p.setup(1.0)
stim = p.Population(4, p.SpikeSourceArray(range(10)), label="stim")
# These populations should experience formation
pop = p.Population(4, p.IF_curr_exp(), label="pop")
# Formation with random selection (0 probability elimination), setting
# with_replacement=False means an all-to-all connection will be the result
proj = p.Projection(
stim, pop, p.FromListConnector([]),
p.StructuralMechanismStatic(
partner_selection=p.LastNeuronSelection(),
formation=p.DistanceDependentFormation([2, 2], 1.0),
elimination=p.RandomByWeightElimination(4.0, 0, 0),
f_rew=1000,
initial_weight=4.0,
initial_delay=3.0,
s_max=4,
seed=0,
weight=0.0,
delay=1.0,
with_replacement=False))
pop.record("rewiring")
p.run(1000)
# Get the final connections
conns = proj.get(["weight", "delay"], "list")
rewiring = pop.get_data("rewiring")
formation_events = rewiring.segments[0].events[0]
elimination_events = rewiring.segments[0].events[1]
num_forms = len(formation_events.times)
num_elims = len(elimination_events.times)
first_f = formation_events.labels[0]
p.end()
return conns, num_forms, num_elims, first_f
def mission_impossible_2():
sim.setup(0.1, time_scale_factor=1)
# Can't do structural on multiple synapse cores
source = sim.Population(1, sim.SpikeSourcePoisson(rate=10))
pop = sim.Population(128,
sim.IF_curr_exp(),
additional_parameters={
"splitter":
SplitterAbstractPopulationVertexNeuronsSynapses(2)
})
sim.Projection(
source, pop, sim.FromListConnector([]),
sim.StructuralMechanismStatic(sim.RandomSelection(),
sim.DistanceDependentFormation(),
sim.RandomByWeightElimination(0.5)))
with pytest.raises(SynapticConfigurationException):
sim.run(100)
A_minus=a_minus),
weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=g_max),
weight=g_max)
partner_selection_last_neuron = sim.LastNeuronSelection()
formation_distance = sim.DistanceDependentFormation(
grid=grid, # spatial org of neurons
sigma_form_forward=
sigma_form_forward, # spread of feadforward receptive field
sigma_form_lateral=sigma_form_lateral, # spread of lateral receptive field
p_form_forward=p_form_forward, # feedforward formation probability
p_form_lateral=p_form_lateral # lateral formation probability
)
elimination_weight = sim.RandomByWeightElimination(
threshold=g_max /
2., # Use same weight as initial weight for static connections
prob_elim_depressed=p_elim_dep,
prob_elim_potentiated=p_elim_pot)
if args.case == CASE_CORR_AND_REW:
structure_model_w_stdp = sim.StructuralMechanismSTDP(
# Partner selection, formation and elimination rules from above
partner_selection_last_neuron,
formation_distance,
elimination_weight,
# Use this weight when creating a new synapse
initial_weight=g_max,
# Use this weight for synapses at start of simulation
weight=g_max,
# Use this delay when creating a new synapse
initial_delay=args.delay,
def structural_shared():
p.setup(1.0)
pre_spikes = numpy.array(range(0, 10, 2))
A_plus = 0.01
A_minus = 0.01
tau_plus = 20.0
tau_minus = 20.0
w_min = 0.0
w_max = 5.0
w_init = 5.0
delay_init = 2.0
stim = p.Population(1, p.SpikeSourceArray(pre_spikes), label="stim")
pop = p.Population(1, p.IF_curr_exp(), label="pop")
pop_2 = p.Population(1, p.IF_curr_exp(), label="pop_2")
pop_3 = p.Population(1, p.IF_curr_exp(), label="pop_3")
pop_4 = p.Population(1, p.IF_curr_exp(), label="pop_4")
pop.record("spikes")
pop_2.record("spikes")
struct_pl_static = p.StructuralMechanismStatic(
partner_selection=p.LastNeuronSelection(),
formation=p.DistanceDependentFormation([1, 1], 1.0),
elimination=p.RandomByWeightElimination(2.0, 0, 0),
f_rew=1000, initial_weight=w_init, initial_delay=delay_init,
s_max=1, seed=0, weight=0.0, delay=1.0)
struct_pl_stdp = p.StructuralMechanismSTDP(
partner_selection=p.LastNeuronSelection(),
formation=p.DistanceDependentFormation([1, 1], 0.0),
elimination=p.RandomByWeightElimination(4.0, 1.0, 1.0),
timing_dependence=p.SpikePairRule(
tau_plus, tau_minus, A_plus, A_minus),
weight_dependence=p.AdditiveWeightDependence(w_min, w_max),
f_rew=1000, initial_weight=2.0, initial_delay=5.0,
s_max=1, seed=0, weight=0.0, delay=1.0)
proj = p.Projection(
stim, pop, p.FromListConnector([]), struct_pl_static)
proj_2 = p.Projection(
stim, pop_2, p.FromListConnector([]), struct_pl_static)
proj_3 = p.Projection(
stim, pop_3, p.FromListConnector([(0, 0)]), struct_pl_stdp)
proj_4 = p.Projection(
stim, pop_4, p.FromListConnector([(0, 0)]), struct_pl_stdp)
p.Projection(pop_3, pop_4, p.AllToAllConnector(),
p.StaticSynapse(weight=1, delay=3))
p.run(10)
conns = list(proj.get(["weight", "delay"], "list"))
conns_2 = list(proj_2.get(["weight", "delay"], "list"))
conns_3 = list(proj_3.get(["weight", "delay"], "list"))
conns_4 = list(proj_4.get(["weight", "delay"], "list"))
p.end()
print(conns)
print(conns_2)
print(conns_3)
print(conns_4)
assert(len(conns) == 1)
assert(tuple(conns[0]) == (0, 0, w_init, delay_init))
assert(len(conns_2) == 1)
assert(tuple(conns_2[0]) == (0, 0, w_init, delay_init))
assert(len(conns_3) == 0)
assert(len(conns_4) == 0)
def structural_with_stdp():
p.setup(1.0)
pre_spikes = numpy.array(range(0, 10, 2))
pre_spikes_last_neuron = pre_spikes[pre_spikes > 0]
A_plus = 0.01
A_minus = 0.01
tau_plus = 20.0
tau_minus = 20.0
w_min = 0.0
w_max = 5.0
w_init_1 = 5.0
delay_1 = 2.0
w_init_2 = 4.0
delay_2 = 1.0
stim = p.Population(1, p.SpikeSourceArray(pre_spikes), label="stim")
pop = p.Population(1, p.IF_curr_exp(), label="pop")
pop_2 = p.Population(1, p.IF_curr_exp(), label="pop_2")
pop_3 = p.Population(1, p.IF_curr_exp(), label="pop_3")
pop_4 = p.Population(1, p.IF_curr_exp(), label="pop_4")
pop.record("spikes")
pop_2.record("spikes")
proj = p.Projection(
stim, pop, p.FromListConnector([]), p.StructuralMechanismSTDP(
partner_selection=p.LastNeuronSelection(),
formation=p.DistanceDependentFormation([1, 1], 1.0),
elimination=p.RandomByWeightElimination(2.0, 0, 0),
timing_dependence=p.SpikePairRule(
tau_plus, tau_minus, A_plus, A_minus),
weight_dependence=p.AdditiveWeightDependence(w_min, w_max),
f_rew=1000, initial_weight=w_init_1, initial_delay=delay_1,
s_max=1, seed=0, weight=0.0, delay=1.0))
proj_2 = p.Projection(
stim, pop_2, p.FromListConnector([]), p.StructuralMechanismSTDP(
partner_selection=p.RandomSelection(),
formation=p.DistanceDependentFormation([1, 1], 1.0),
elimination=p.RandomByWeightElimination(4.0, 0, 0),
timing_dependence=p.SpikePairRule(
tau_plus, tau_minus, A_plus, A_minus),
weight_dependence=p.AdditiveWeightDependence(w_min, w_max),
f_rew=1000, initial_weight=w_init_2, initial_delay=delay_2,
s_max=1, seed=0, weight=0.0, delay=1.0))
proj_3 = p.Projection(
stim, pop_3, p.FromListConnector([(0, 0)]),
p.StructuralMechanismSTDP(
partner_selection=p.LastNeuronSelection(),
formation=p.DistanceDependentFormation([1, 1], 0.0),
elimination=p.RandomByWeightElimination(4.0, 1.0, 1.0),
timing_dependence=p.SpikePairRule(
tau_plus, tau_minus, A_plus, A_minus),
weight_dependence=p.AdditiveWeightDependence(w_min, w_max),
f_rew=1000, initial_weight=2.0, initial_delay=5.0,
s_max=1, seed=0, weight=0.0, delay=1.0))
proj_4 = p.Projection(
stim, pop_4, p.FromListConnector([(0, 0)]),
p.StructuralMechanismSTDP(
partner_selection=p.RandomSelection(),
formation=p.DistanceDependentFormation([1, 1], 0.0),
elimination=p.RandomByWeightElimination(4.0, 1.0, 1.0),
timing_dependence=p.SpikePairRule(
tau_plus, tau_minus, A_plus, A_minus),
weight_dependence=p.AdditiveWeightDependence(w_min, w_max),
f_rew=1000, initial_weight=4.0, initial_delay=3.0,
s_max=1, seed=0, weight=0.0, delay=1.0))
p.run(10)
conns = list(proj.get(["weight", "delay"], "list"))
conns_2 = list(proj_2.get(["weight", "delay"], "list"))
conns_3 = list(proj_3.get(["weight", "delay"], "list"))
conns_4 = list(proj_4.get(["weight", "delay"], "list"))
spikes_1 = [s.magnitude
for s in pop.get_data("spikes").segments[0].spiketrains]
spikes_2 = [s.magnitude
for s in pop_2.get_data("spikes").segments[0].spiketrains]
p.end()
print(conns)
print(conns_2)
print(conns_3)
print(conns_4)
w_final_1 = calculate_spike_pair_additive_stdp_weight(
pre_spikes_last_neuron, spikes_1[0], w_init_1, delay_1, w_max,
A_plus, A_minus, tau_plus, tau_minus)
w_final_2 = calculate_spike_pair_additive_stdp_weight(
pre_spikes, spikes_2[0], w_init_2, delay_2, w_max, A_plus, A_minus,
tau_plus, tau_minus)
print(w_final_1, spikes_1[0])
print(w_final_2, spikes_2[0])
assert(len(conns) == 1)
assert(conns[0][3] == delay_1)
assert(conns[0][2] >= w_final_1 - 0.01 and
conns[0][2] <= w_final_1 + 0.01)
assert(len(conns_2) == 1)
assert(conns_2[0][3] == delay_2)
assert(conns_2[0][2] >= w_final_2 - 0.01 and
conns_2[0][2] <= w_final_2 + 0.01)
assert(len(conns_3) == 0)
assert(len(conns_4) == 0)
def structural_without_stdp():
p.setup(1.0)
stim = p.Population(1, p.SpikeSourceArray(range(10)), label="stim")
# These populations should experience formation
pop = p.Population(1, p.IF_curr_exp(), label="pop")
pop_2 = p.Population(1, p.IF_curr_exp(), label="pop_2")
# These populations should experience elimination
pop_3 = p.Population(1, p.IF_curr_exp(), label="pop_3")
pop_4 = p.Population(1, p.IF_curr_exp(), label="pop_4")
# Formation with last-neuron selection (0 probability elimination)
proj = p.Projection(
stim, pop, p.FromListConnector([]), p.StructuralMechanismStatic(
partner_selection=p.LastNeuronSelection(),
formation=p.DistanceDependentFormation([1, 1], 1.0),
elimination=p.RandomByWeightElimination(2.0, 0, 0),
f_rew=1000, initial_weight=2.0, initial_delay=5.0,
s_max=1, seed=0, weight=0.0, delay=1.0))
# Formation with random selection (0 probability elimination)
proj_2 = p.Projection(
stim, pop_2, p.FromListConnector([]), p.StructuralMechanismStatic(
partner_selection=p.RandomSelection(),
formation=p.DistanceDependentFormation([1, 1], 1.0),
elimination=p.RandomByWeightElimination(4.0, 0, 0),
f_rew=1000, initial_weight=4.0, initial_delay=3.0,
s_max=1, seed=0, weight=0.0, delay=1.0))
# Elimination with last neuron selection (0 probability formation)
proj_3 = p.Projection(
stim, pop_3, p.FromListConnector([(0, 0)]),
p.StructuralMechanismStatic(
partner_selection=p.LastNeuronSelection(),
formation=p.DistanceDependentFormation([1, 1], 0.0),
elimination=p.RandomByWeightElimination(4.0, 1.0, 1.0),
f_rew=1000, initial_weight=2.0, initial_delay=5.0,
s_max=1, seed=0, weight=0.0, delay=1.0))
# Elimination with random selection (0 probability formation)
proj_4 = p.Projection(
stim, pop_4, p.FromListConnector([(0, 0)]),
p.StructuralMechanismStatic(
partner_selection=p.RandomSelection(),
formation=p.DistanceDependentFormation([1, 1], 0.0),
elimination=p.RandomByWeightElimination(4.0, 1.0, 1.0),
f_rew=1000, initial_weight=4.0, initial_delay=3.0,
s_max=1, seed=0, weight=0.0, delay=1.0))
p.run(10)
# Get the final connections
conns = list(proj.get(["weight", "delay"], "list"))
conns_2 = list(proj_2.get(["weight", "delay"], "list"))
conns_3 = list(proj_3.get(["weight", "delay"], "list"))
conns_4 = list(proj_4.get(["weight", "delay"], "list"))
p.end()
print(conns)
print(conns_2)
print(conns_3)
print(conns_4)
# These should be formed with specified parameters
assert(len(conns) == 1)
assert(tuple(conns[0]) == (0, 0, 2.0, 5.0))
assert(len(conns_2) == 1)
assert(tuple(conns_2[0]) == (0, 0, 4.0, 3.0))
# These should have no connections since eliminated
assert(len(conns_3) == 0)
assert(len(conns_4) == 0)