Spinncer weight scale

c_models/src/my_models/plasticity/stdp/weight_dependence/my_weight_impl.c

@@ -5,10 +5,7 @@ plasticity_weight_region_data_t *plasticity_weight_region_data;
 
 address_t weight_initialise(
         address_t address, uint32_t n_synapse_types,
-        uint32_t *ring_buffer_to_input_buffer_left_shifts) {
-
-    // This can be used to indicate the scaling used on the weights
-    use(ring_buffer_to_input_buffer_left_shifts);
+		UNUSED REAL *min_weights) {
 
     log_info("weight_initialise: starting");
     log_info("\tSTDP my weight dependence");

nmt_integration_tests/test_my_model_curr_exp_stdp.py

@@ -54,7 +54,7 @@ def do_run(self):
         neo = test_pop.get_data('all')
         sim.end()
         for _, _, weight in weights:
-            self.assertEqual(weight, 0.5)
+            self.assertAlmostEqual(weight, 0.5, places=2)
         self.check_results(neo, [201, 402, 603, 804])
 
     def test_do_run(self):

python_models8/neuron/plasticity/stdp/timing_dependence/my_timing_dependence.py

@@ -1,10 +1,14 @@
 from spinn_utilities.overrides import overrides
 from spinn_front_end_common.interface.ds import DataType
-from spinn_front_end_common.utilities.constants import BYTES_PER_WORD
+from spinn_front_end_common.utilities.constants import (
+    BYTES_PER_WORD, MICRO_TO_MILLISECOND_CONVERSION)
 from spynnaker.pyNN.models.neuron.plasticity.stdp.timing_dependence import (
     AbstractTimingDependence)
 from spynnaker.pyNN.models.neuron.plasticity.stdp.synapse_structure import (
     SynapseStructureWeightOnly)
+from spynnaker.pyNN.models.neuron.plasticity.stdp.common import (
+    get_exp_lut_array, get_min_lut_value)
+from spynnaker.pyNN.data import SpynnakerDataView
 
 
 class MyTimingDependence(AbstractTimingDependence):
@@ -13,6 +17,8 @@ class MyTimingDependence(AbstractTimingDependence):
         "_a_plus",
         "_my_depression_parameter",
         "_my_potentiation_parameter",
+        "_my_depression_data",
+        "_my_potentiation_data",
         "_synapse_structure"]
 
     NUM_PARAMETERS = 2
@@ -38,6 +44,13 @@ def __init__(
         self._a_plus = A_plus
         self._a_minus = A_minus
 
+        ts = SpynnakerDataView.get_simulation_time_step_ms()
+        ts = ts / MICRO_TO_MILLISECOND_CONVERSION
+        self._my_potentiation_data = get_exp_lut_array(
+            ts, self._my_potentiation_parameter)
+        self._my_depression_data = get_exp_lut_array(
+            ts, self._my_depression_parameter)
+
     # TODO: Add getters and setters for parameters
 
     @property
@@ -113,6 +126,15 @@ def write_parameters(
     def get_parameter_names(self):
         return ['my_potentiation_parameter', 'my_depression_parameter']
 
+    @overrides(AbstractTimingDependence.minimum_delta)
+    def minimum_delta(self, max_stdp_spike_delta):
+        ts = SpynnakerDataView.get_simulation_time_step_ms()
+        return [
+            get_min_lut_value(self._my_potentiation_data, ts,
+                              max_stdp_spike_delta),
+            get_min_lut_value(self._my_depression_data, ts,
+                              max_stdp_spike_delta)]
+
     @property
     def synaptic_structure(self):
         """ Get the synaptic structure of the plastic part of the rows

python_models8/neuron/plasticity/stdp/weight_dependence/my_weight_dependence.py

@@ -118,6 +118,21 @@ def weight_maximum(self):
         # give to a synapse
         return self._w_max
 
+    @property
+    def weight_minimum(self):
+        """ The minimum weight that will ever be set in a synapse as a result\
+            of this rule
+        """
+        # TODO: update to return the minimum weight that this rule will ever
+        # give to a synapse
+        return self._w_min
+
+    @overrides(AbstractWeightDependence.weight_change_minimum)
+    def weight_change_minimum(self, min_delta):
+        pot, dep = min_delta
+        return min(pot * self._my_weight_parameter,
+                   dep * self._my_weight_parameter)
+
     @overrides(AbstractWeightDependence.get_parameter_names)
     def get_parameter_names(self):
         return ['w_min', 'w_max', 'my_weight_parameter']