SpiNNakerManchester · rowleya · May 9, 2025 · Apr 25, 2025 · Apr 25, 2025 · May 2, 2025
diff --git a/balanced_random/split/balanced_random_split.py b/balanced_random/split/balanced_random_split.py
@@ -17,10 +17,8 @@
 from pyNN.random import RandomDistribution
 from pyNN.utility.plotting import Figure, Panel
 import pyNN.spiNNaker as p
-from spynnaker.pyNN.extra_algorithms.splitter_components import (
-    SplitterPoissonDelegate, SplitterPopulationVertexNeuronsSynapses)
 
-p.setup(timestep=0.1, time_scale_factor=1)
+p.setup(timestep=0.1)
 p.set_number_of_neurons_per_core(p.IF_curr_exp, 64)
 p.set_number_of_neurons_per_core(p.SpikeSourcePoisson, 64)
 n_neurons = 500
@@ -30,32 +28,15 @@
 weight_inh = -5.0 * weight_exc
 weight_input = 0.001
 
-pop_input_splitter = SplitterPoissonDelegate()
-pop_input = p.Population(100, p.SpikeSourcePoisson(rate=0.0),
-                         additional_parameters={
-                             "max_rate": 50.0,
-                             "seed": 0,
-                             "splitter": pop_input_splitter},
-                         label="Input")
-
-pop_exc_splitter = \
-    SplitterPopulationVertexNeuronsSynapses(1, 128, False)
-pop_exc = p.Population(n_exc, p.IF_curr_exp, label="Excitatory",
-                       additional_parameters={"splitter": pop_exc_splitter,
-                                              "seed": 1})
-pop_inh_splitter = \
-    SplitterPopulationVertexNeuronsSynapses(1, 128, False)
-pop_inh = p.Population(n_inh, p.IF_curr_exp, label="Inhibitory",
-                       additional_parameters={"splitter": pop_inh_splitter,
-                                              "seed": 2})
-stim_exc_splitter = SplitterPoissonDelegate()
+pop_input = p.Population(
+    100, p.SpikeSourcePoisson(rate=0.0), label="Input", seed=0, max_rate=50.0)
+
+pop_exc = p.Population(n_exc, p.IF_curr_exp, label="Excitatory", seed=1)
+pop_inh = p.Population(n_inh, p.IF_curr_exp, label="Inhibitory", seed=2)
 stim_exc = p.Population(
-    n_exc, p.SpikeSourcePoisson(rate=1000.0), label="Stim_Exc",
-    additional_parameters={"seed": 3, "splitter": stim_exc_splitter})
-stim_inh_splitter = SplitterPoissonDelegate()
+    n_exc, p.SpikeSourcePoisson(rate=1000.0), label="Stim_Exc", seed=3)
 stim_inh = p.Population(
-    n_inh, p.SpikeSourcePoisson(rate=1000.0), label="Stim_Inh",
-    additional_parameters={"seed": 4, "splitter": stim_inh_splitter})
+    n_inh, p.SpikeSourcePoisson(rate=1000.0), label="Stim_Inh", seed=4)
 
 delays_exc = RandomDistribution(
     "normal_clipped", mu=1.5, sigma=0.75, low=1.0, high=1.6)

diff --git a/balanced_random/split/spynnaker.cfg b/balanced_random/split/spynnaker.cfg
diff --git a/examples/external_devices_examples/live_examples/balanced_random_live_rate.py b/examples/external_devices_examples/live_examples/balanced_random_live_rate.py
@@ -19,7 +19,9 @@
 from pyNN.random import RandomDistribution
 from pyNN.utility.plotting import Figure, Panel
 
-p.setup(timestep=0.1)
+# We need a time scale factor here as we are interacting live, so too fast
+# otherwise!
+p.setup(timestep=0.1, time_scale_factor=10.0)
 p.set_number_of_neurons_per_core(p.SpikeSourcePoisson, 50)
 n_neurons = 500
 n_exc = int(round(n_neurons * 0.8))

diff --git a/examples/split_examples/pynnBrunnelSplit.py b/examples/split_examples/pynnBrunnelSplit.py
@@ -18,8 +18,6 @@
 import matplotlib.pyplot as plt
 from pyNN.random import RandomDistribution
 from pyNN.utility.plotting import Figure, Panel
-from spynnaker.pyNN.extra_algorithms.splitter_components import (
-    SplitterPopulationVertexNeuronsSynapses, SplitterPoissonDelegate)
 
 simulator_Name = 'spiNNaker'
 # exec('import pyNN.%s as pynn' % simulator_Name)
@@ -164,26 +162,16 @@ def poisson_generator(rate, rng, t_start=0.0, t_stop=1000.0, array=True,
 }
 
 # Set-up pynn Populations
-E_pop_splitter = SplitterPopulationVertexNeuronsSynapses(3, 128, False)
 E_pop = pynn.Population(
-    N_E, pynn.IF_curr_exp(**exc_cell_params), label="E_pop",
-    additional_parameters={"splitter": E_pop_splitter}, seed=1)
+    N_E, pynn.IF_curr_exp(**exc_cell_params), label="E_pop", seed=1)
 
-I_pop_splitter = SplitterPopulationVertexNeuronsSynapses(3, 128, False)
 I_pop = pynn.Population(
-    N_I, pynn.IF_curr_exp(**inh_cell_params), label="I_pop",
-    additional_parameters={"splitter": I_pop_splitter}, seed=2)
+    N_I, pynn.IF_curr_exp(**inh_cell_params), label="I_pop", seed=2)
 
-Poiss_ext_E_splitter = SplitterPoissonDelegate()
 Poiss_ext_E = pynn.Population(
-    N_E, pynn.SpikeSourcePoisson(rate=10.0), label="Poisson_pop_E",
-    additional_parameters={"seed": 3,
-                           "splitter": Poiss_ext_E_splitter})
-Poiss_ext_I_splitter = SplitterPoissonDelegate()
+    N_E, pynn.SpikeSourcePoisson(rate=10.0), label="Poisson_pop_E", seed=3)
 Poiss_ext_I = pynn.Population(
-    N_I, pynn.SpikeSourcePoisson(rate=10.0), label="Poisson_pop_I",
-    additional_parameters={"seed": 4,
-                           "splitter": Poiss_ext_I_splitter})
+    N_I, pynn.SpikeSourcePoisson(rate=10.0), label="Poisson_pop_I", seed=4)
 
 # Connectors
 E_conn = pynn.FixedProbabilityConnector(epsilon)

diff --git a/examples/split_examples/spynnaker.cfg b/examples/split_examples/spynnaker.cfg
diff --git a/examples/split_examples/stdp_neuromodulated_example_split.py b/examples/split_examples/stdp_neuromodulated_example_split.py
@@ -26,9 +26,6 @@
 post-synaptic neurons to the same stimuli.
 """
 
-from spynnaker.pyNN.extra_algorithms.splitter_components import (
-    SplitterPopulationVertexNeuronsSynapses)
-
 import pyNN.spiNNaker as sim
 import pylab
 
@@ -59,6 +56,7 @@
                }
 
 sim.setup(timestep=timestep)
+sim.set_number_of_synapse_cores(sim.IF_curr_exp, 1)
 
 # Create a population of dopaminergic neurons for reward and punishment
 reward_pop = sim.Population(n_neurons, sim.SpikeSourceArray,
@@ -70,7 +68,6 @@
 pre_pops = []
 stimulation = []
 post_pops = []
-post_splitters = []
 reward_projections = []
 punishment_projections = []
 plastic_projections = []
@@ -85,12 +82,11 @@
     weight=plastic_weights)
 
 for i in range(n_pops):
-    stimulation.append(sim.Population(n_neurons, sim.SpikeSourcePoisson,
-                       {'rate': stim_rate, 'duration': duration}, label="pre"))
-    post_splitters.append(SplitterPopulationVertexNeuronsSynapses(2))
+    stimulation.append(sim.Population(
+        n_neurons, sim.SpikeSourcePoisson(rate=stim_rate, duration=duration),
+        label="pre"))
     post_pops.append(sim.Population(
-        n_neurons, sim.IF_curr_exp, cell_params, label='post',
-        additional_parameters={"splitter": post_splitters[i]}))
+        n_neurons, sim.IF_curr_exp, cell_params, label='post'))
     plastic_projections.append(
         sim.Projection(stimulation[i], post_pops[i],
                        sim.OneToOneConnector(),

diff --git a/examples/split_examples/structural_plasticity_with_stdp_neuromodulated.py b/examples/split_examples/structural_plasticity_with_stdp_neuromodulated.py
@@ -31,9 +31,6 @@
 STDP, structural plasticity and neuron instructions would not fit on one core
 """
 
-from spynnaker.pyNN.extra_algorithms.splitter_components import (
-    SplitterPopulationVertexNeuronsSynapses)
-
 import pyNN.spiNNaker as sim
 import pylab
 import numpy as np
@@ -76,7 +73,6 @@
 pre_pops = []
 stimulation = []
 post_pops = []
-post_splitters = []
 reward_projections = []
 punishment_projections = []
 plastic_projections = []
@@ -118,12 +114,11 @@
     weight_dependence=sim.AdditiveWeightDependence(w_min=0, w_max=5.0))
 
 for i in range(n_pops):
-    stimulation.append(sim.Population(n_neurons, sim.SpikeSourcePoisson,
-                       {'rate': stim_rate, 'duration': duration}, label="pre"))
-    post_splitters.append(SplitterPopulationVertexNeuronsSynapses(1))
+    stimulation.append(sim.Population(
+        n_neurons, sim.SpikeSourcePoisson(rate=stim_rate, duration=duration),
+        label="pre"))
     post_pops.append(sim.Population(
-        n_neurons, sim.IF_curr_exp, cell_params, label='post',
-        additional_parameters={"splitter": post_splitters[i]}))
+        n_neurons, sim.IF_curr_exp, cell_params, label='post'))
     plastic_projections.append(
         sim.Projection(stimulation[i], post_pops[i],
                        sim.FixedProbabilityConnector(0.),  # no initial conns

diff --git a/examples/split_examples/va_benchmark_split.py b/examples/split_examples/va_benchmark_split.py
@@ -32,8 +32,6 @@
 from pyNN.utility import Timer
 from pyNN.utility.plotting import Figure, Panel
 import matplotlib.pyplot as plt
-from spynnaker.pyNN.extra_algorithms.splitter_components import (
-    SplitterPopulationVertexNeuronsSynapses)
 
 
 simulator_name = 'spiNNaker'
@@ -126,10 +124,8 @@
     timestep=dt, min_delay=delay, db_name='va_benchmark.sqlite', **extra)
 
 if simulator_name == 'spiNNaker':
-    p.set_number_of_neurons_per_core(p.IF_curr_exp, 100)      # this will set
-    #  100 neurons per core
-    p.set_number_of_neurons_per_core(p.IF_cond_exp, 50)      # this will set
-    # 50 neurons per core
+    p.set_number_of_neurons_per_core(p.IF_curr_exp, 100)
+    p.set_number_of_neurons_per_core(p.IF_cond_exp, 50)
 np = 1
 
 host_name = socket.gethostname()
@@ -158,14 +154,10 @@
 timer.start()
 
 print("%s Creating cell populations..." % node_id)
-exc_cells_splitter = SplitterPopulationVertexNeuronsSynapses(2)
 exc_cells = p.Population(
-    n_exc, celltype(**cell_params), label="Excitatory_Cells",
-    additional_parameters={"splitter": exc_cells_splitter}, seed=1)
-inh_cells_splitter = SplitterPopulationVertexNeuronsSynapses(3)
+    n_exc, celltype(**cell_params), label="Excitatory_Cells", seed=1)
 inh_cells = p.Population(
-    n_inh, celltype(**cell_params), label="Inhibitory_Cells",
-    additional_parameters={"splitter": inh_cells_splitter}, seed=2)
+    n_inh, celltype(**cell_params), label="Inhibitory_Cells", seed=2)
 exc_conn = None
 ext_stim = None
 if benchmark == "COBA":

diff --git a/learning/split/spynnaker.cfg b/learning/split/spynnaker.cfg
diff --git a/learning/split/stdp_split.py b/learning/split/stdp_split.py
@@ -15,38 +15,29 @@
 import pyNN.utility.plotting as plot
 import matplotlib.pyplot as plt
 import pyNN.spiNNaker as sim
-from spynnaker.pyNN.extra_algorithms.splitter_components import (
-    SplitterPopulationVertexNeuronsSynapses, SplitterPoissonDelegate)
 
 n_neurons = 192
 simtime = 5000
 
 sim.setup(timestep=1.0)
 sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 64)
+sim.set_number_of_synapse_cores(sim.IF_curr_exp, 1)
+sim.set_allow_delay_extensions(sim.IF_curr_exp, False)
 
-pre_splitter = SplitterPopulationVertexNeuronsSynapses(1, 128, False)
-pre_pop = sim.Population(
-    n_neurons, sim.IF_curr_exp(), label="Pre", additional_parameters={
-        "splitter": pre_splitter})
-post_splitter = SplitterPopulationVertexNeuronsSynapses(1, 128, False)
-post_pop = sim.Population(
-    n_neurons, sim.IF_curr_exp(), label="Post", additional_parameters={
-        "splitter": post_splitter})
+pre_pop = sim.Population(n_neurons, sim.IF_curr_exp(), label="Pre")
+post_pop = sim.Population(n_neurons, sim.IF_curr_exp(), label="Post")
 pre_noise = sim.Population(
-    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Pre",
-    additional_parameters={"splitter": SplitterPoissonDelegate()})
+    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Pre")
 post_noise = sim.Population(
-    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Post",
-    additional_parameters={"splitter": SplitterPoissonDelegate()})
+    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Post")
 
 pre_pop.record("spikes")
 post_pop.record("spikes")
 
 training = sim.Population(
     n_neurons,
     sim.SpikeSourcePoisson(rate=10.0, start=1500.0, duration=1500.0),
-    label="Training",
-    additional_parameters={"splitter": SplitterPoissonDelegate()})
+    label="Training")
 
 sim.Projection(pre_noise,  pre_pop,  sim.OneToOneConnector(),
                synapse_type=sim.StaticSynapse(weight=2.0))

diff --git a/learning/split/struct_pl_split.py b/learning/split/struct_pl_split.py
@@ -16,37 +16,28 @@
 import numpy
 import pyNN.utility.plotting as plot
 import pyNN.spiNNaker as sim
-from spynnaker.pyNN.extra_algorithms.splitter_components import (
-    SplitterPopulationVertexNeuronsSynapses, SplitterPoissonDelegate)
 
 n_neurons = 64
 simtime = 5000
 
 sim.setup(timestep=1.0)
+sim.set_number_of_synapse_cores(sim.IF_curr_exp, 1)
+sim.set_allow_delay_extensions(sim.IF_curr_exp, False)
 
-pre_splitter = SplitterPopulationVertexNeuronsSynapses(1, 128, False)
-pre_pop = sim.Population(
-    n_neurons, sim.IF_curr_exp(), label="Pre", additional_parameters={
-        "splitter": pre_splitter})
-post_splitter = SplitterPopulationVertexNeuronsSynapses(1, 128, False)
-post_pop = sim.Population(
-    n_neurons, sim.IF_curr_exp(), label="Post", additional_parameters={
-        "splitter": post_splitter})
+pre_pop = sim.Population(n_neurons, sim.IF_curr_exp(), label="Pre")
+post_pop = sim.Population(n_neurons, sim.IF_curr_exp(), label="Post")
 pre_noise = sim.Population(
-    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Pre",
-    additional_parameters={"splitter": SplitterPoissonDelegate()})
+    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Pre")
 post_noise = sim.Population(
-    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Post",
-    additional_parameters={"splitter": SplitterPoissonDelegate()})
+    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Post")
 
 pre_pop.record("spikes")
 post_pop.record("spikes")
 
 training = sim.Population(
     n_neurons,
     sim.SpikeSourcePoisson(rate=10.0, start=1500.0, duration=1500.0),
-    label="Training",
-    additional_parameters={"splitter": SplitterPoissonDelegate()})
+    label="Training")
 
 sim.Projection(pre_noise,  pre_pop,  sim.OneToOneConnector(),
                synapse_type=sim.StaticSynapse(weight=2.0))

diff --git a/learning/split/struct_pl_stdp_split.py b/learning/split/struct_pl_stdp_split.py
@@ -16,38 +16,29 @@
 import numpy
 import pyNN.spiNNaker as sim
 import pyNN.utility.plotting as plot
-from spynnaker.pyNN.extra_algorithms.splitter_components import (
-    SplitterPopulationVertexNeuronsSynapses, SplitterPoissonDelegate)
 
 n_neurons = 192
 simtime = 5000
 
 sim.setup(timestep=1.0)
+sim.set_number_of_synapse_cores(sim.IF_curr_exp, 1)
+sim.set_allow_delay_extensions(sim.IF_curr_exp, False)
 
 sim.set_number_of_neurons_per_core(sim.IF_curr_exp, 64)
-pre_splitter = SplitterPopulationVertexNeuronsSynapses(1, 128, False)
-pre_pop = sim.Population(
-    n_neurons, sim.IF_curr_exp(), label="Pre", additional_parameters={
-        "splitter": pre_splitter})
-post_splitter = SplitterPopulationVertexNeuronsSynapses(1, 128, False)
-post_pop = sim.Population(
-    n_neurons, sim.IF_curr_exp(), label="Post", additional_parameters={
-        "splitter": post_splitter})
+pre_pop = sim.Population(n_neurons, sim.IF_curr_exp(), label="Pre")
+post_pop = sim.Population(n_neurons, sim.IF_curr_exp(), label="Post")
 pre_noise = sim.Population(
-    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Pre",
-    additional_parameters={"splitter": SplitterPoissonDelegate()})
+    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Pre")
 post_noise = sim.Population(
-    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Post",
-    additional_parameters={"splitter": SplitterPoissonDelegate()})
+    n_neurons, sim.SpikeSourcePoisson(rate=10.0), label="Noise_Post")
 
 pre_pop.record("spikes")
 post_pop.record("spikes")
 
 training = sim.Population(
     n_neurons,
     sim.SpikeSourcePoisson(rate=10.0, start=1500.0, duration=1500.0),
-    label="Training",
-    additional_parameters={"splitter": SplitterPoissonDelegate()})
+    label="Training")
 
 sim.Projection(pre_noise,  pre_pop,  sim.OneToOneConnector(),
                synapse_type=sim.StaticSynapse(weight=2.0))