diff --git a/p8_integration_tests/test_stdp/test_IF_cond_exp_stdp_mad_pair_additive.py b/p8_integration_tests/test_stdp/test_IF_cond_exp_stdp_mad_pair_additive.py
new file mode 100644
index 000000000..3995f6d57
--- /dev/null
+++ b/p8_integration_tests/test_stdp/test_IF_cond_exp_stdp_mad_pair_additive.py
@@ -0,0 +1,122 @@
+import spynnaker8 as p
+from p8_integration_tests.base_test_case import BaseTestCase
+import numpy
+import math
+import unittest
+
+
+class TestIFCondExpSTDPPairAdditive(BaseTestCase):
+
+    def test_potentiation_and_depression(self):
+        p.setup(1)
+        runtime = 100
+        initial_run = 1000  # to negate any initial conditions
+
+        # STDP parameters
+        a_plus = 0.1
+        a_minus = 0.0375
+        tau_plus = 20
+        tau_minus = 64
+        plastic_delay = 1
+        initial_weight = 0.05
+        max_weight = 0.1
+        min_weight = 0
+
+        spike_times = [10, 50]
+        spike_times2 = [30]
+
+        for i in range(len(spike_times)):
+            spike_times[i] += initial_run
+
+        for i in range(len(spike_times2)):
+            spike_times2[i] += initial_run
+
+        # Spike source to send spike via plastic synapse
+        pop_src1 = p.Population(1, p.SpikeSourceArray,
+                                {'spike_times': spike_times}, label="src1")
+
+        # Spike source to send spike via static synapse to make
+        # post-plastic-synapse neuron fire
+        pop_src2 = p.Population(1, p.SpikeSourceArray,
+                                {'spike_times': spike_times2}, label="src2")
+
+        # Post-plastic-synapse population
+        pop_exc = p.Population(1, p.IF_cond_exp(),  label="test")
+
+        # Create projections
+        p.Projection(
+            pop_src1, pop_exc, p.OneToOneConnector(),
+            p.StaticSynapse(weight=0.1, delay=1), receptor_type="excitatory")
+
+        p.Projection(
+            pop_src2, pop_exc, p.OneToOneConnector(),
+            p.StaticSynapse(weight=0.1, delay=1), receptor_type="excitatory")
+
+        syn_plas = p.STDPMechanism(
+            timing_dependence=p.SpikePairRule(tau_plus=tau_plus,
+                                              tau_minus=tau_minus,
+                                              A_plus=a_plus, A_minus=a_minus),
+            weight_dependence=p.AdditiveWeightDependence(w_min=min_weight,
+                                                         w_max=max_weight),
+            weight=initial_weight, delay=plastic_delay)
+
+        plastic_synapse = p.Projection(pop_src1, pop_exc,
+                                       p.OneToOneConnector(),
+                                       synapse_type=syn_plas,
+                                       receptor_type='excitatory')
+
+        pop_src1.record('all')
+        pop_exc.record("all")
+        p.run(initial_run + runtime)
+        weights = []
+
+        weights.append(plastic_synapse.get('weight', 'list',
+                                           with_address=False)[0])
+
+        # pre_spikes = pop_src1.get_data('spikes')
+        # v = pop_exc.get_data('v')
+        spikes = pop_exc.get_data('spikes')
+
+        potentiation_time_1 = (spikes.segments[0].spiketrains[0].magnitude[0] +
+                               plastic_delay) - spike_times[0]
+        potentiation_time_2 = (spikes.segments[0].spiketrains[0].magnitude[1] +
+                               plastic_delay) - spike_times[0]
+
+        depression_time_1 = spike_times[1] - (
+            spikes.segments[0].spiketrains[0].magnitude[0] + plastic_delay)
+        depression_time_2 = spike_times[1] - (
+            spikes.segments[0].spiketrains[0].magnitude[1] + plastic_delay)
+
+        potentiation_1 = max_weight * a_plus * \
+            math.exp(-potentiation_time_1/tau_plus)
+        potentiation_2 = max_weight * a_plus * \
+            math.exp(-potentiation_time_2/tau_plus)
+
+        depression_1 = max_weight * a_minus * \
+            math.exp(-depression_time_1/tau_minus)
+        depression_2 = max_weight * a_minus * \
+            math.exp(-depression_time_2/tau_minus)
+
+        new_weight_exact = (initial_weight + potentiation_1 + potentiation_2
+                            - depression_1 - depression_2)
+
+        print("Pre neuron spikes at: {}".format(spike_times))
+        print("Post-neuron spikes at: {}".format(
+            spikes.segments[0].spiketrains[0].magnitude))
+        print("Potentiation time differences: {}, {},\
+             \nDepression time difference: {}, {}".format(
+                 potentiation_time_1, potentiation_time_2,
+                 depression_time_1, depression_time_2))
+        print("Ammounts to potentiate: {}, {},\
+            \nAmount to depress: {}, {},".format(
+                potentiation_1, potentiation_2, depression_1, depression_2))
+        print("New weight exact: {}".format(new_weight_exact))
+        print("New weight SpiNNaker: {}".format(weights[0]))
+
+        self.assertTrue(numpy.allclose(weights[0],
+                                       new_weight_exact, rtol=0.001))
+        p.end()
+
+
+if __name__ == '__main__':
+    unittest.main()