From 8ce3372f74d4a734dfbb5a5e0b08280de02fd5ea Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Thu, 12 Jan 2023 14:02:53 +0100
Subject: [PATCH 01/15] Added makefile for IF_curr_delta neurons to be used in
 convolutions

---
 .../IF_curr_delta_conv/Makefile               | 26 +++++++++++++++++++
 .../makefiles/local_only_combined/Makefile    |  3 ++-
 2 files changed, 28 insertions(+), 1 deletion(-)
 create mode 100644 neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile

diff --git a/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile b/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
new file mode 100644
index 0000000000..8fdc82724e
--- /dev/null
+++ b/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
@@ -0,0 +1,26 @@
+# Copyright (c) 2021-2022 The University of Manchester
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+APP = $(notdir $(CURDIR))
+
+NEURON_MODEL = $(NEURON_DIR)/neuron/models/neuron_model_lif_impl.c
+NEURON_MODEL_H = $(NEURON_DIR)/neuron/models/neuron_model_lif_impl.h
+INPUT_TYPE_H = $(NEURON_DIR)/neuron/input_types/input_type_current.h
+NEURON_IMPL_H = $(NEURON_DIR)/neuron/implementations/neuron_impl_standard.h
+THRESHOLD_TYPE_H = $(NEURON_DIR)/neuron/threshold_types/threshold_type_static.h
+SYNAPSE_TYPE_H = $(NEURON_DIR)/neuron/synapse_types/synapse_types_delta_impl.h
+LOCAL_ONLY_IMPL = $(NEURON_DIR)/neuron/local_only/local_only_conv_impl.c
+
+include ../local_only.mk
diff --git a/neural_modelling/makefiles/local_only_combined/Makefile b/neural_modelling/makefiles/local_only_combined/Makefile
index 4b67130b99..b3b9193129 100644
--- a/neural_modelling/makefiles/local_only_combined/Makefile
+++ b/neural_modelling/makefiles/local_only_combined/Makefile
@@ -13,7 +13,8 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
-MODELS = IF_curr_exp_conv\
+MODELS = IF_curr_delta_conv\
+		 IF_curr_exp_conv\
          IF_curr_exp_pool_dense
 
 all:

From 7accc3f51f16d1c79cfa47bb6b08f9067446fbe6 Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Thu, 12 Jan 2023 14:02:53 +0100
Subject: [PATCH 02/15] Added makefile for IF_curr_delta neurons to be used in
 convolutions

---
 .../IF_curr_delta_conv/Makefile               | 26 +++++++++++++++++++
 .../makefiles/local_only_combined/Makefile    |  3 ++-
 2 files changed, 28 insertions(+), 1 deletion(-)
 create mode 100644 neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile

diff --git a/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile b/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
new file mode 100644
index 0000000000..8fdc82724e
--- /dev/null
+++ b/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
@@ -0,0 +1,26 @@
+# Copyright (c) 2021-2022 The University of Manchester
+#
+# This program is free software: you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation, either version 3 of the License, or
+# (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+APP = $(notdir $(CURDIR))
+
+NEURON_MODEL = $(NEURON_DIR)/neuron/models/neuron_model_lif_impl.c
+NEURON_MODEL_H = $(NEURON_DIR)/neuron/models/neuron_model_lif_impl.h
+INPUT_TYPE_H = $(NEURON_DIR)/neuron/input_types/input_type_current.h
+NEURON_IMPL_H = $(NEURON_DIR)/neuron/implementations/neuron_impl_standard.h
+THRESHOLD_TYPE_H = $(NEURON_DIR)/neuron/threshold_types/threshold_type_static.h
+SYNAPSE_TYPE_H = $(NEURON_DIR)/neuron/synapse_types/synapse_types_delta_impl.h
+LOCAL_ONLY_IMPL = $(NEURON_DIR)/neuron/local_only/local_only_conv_impl.c
+
+include ../local_only.mk
diff --git a/neural_modelling/makefiles/local_only_combined/Makefile b/neural_modelling/makefiles/local_only_combined/Makefile
index 4b67130b99..b3b9193129 100644
--- a/neural_modelling/makefiles/local_only_combined/Makefile
+++ b/neural_modelling/makefiles/local_only_combined/Makefile
@@ -13,7 +13,8 @@
 # You should have received a copy of the GNU General Public License
 # along with this program.  If not, see <http://www.gnu.org/licenses/>.
 
-MODELS = IF_curr_exp_conv\
+MODELS = IF_curr_delta_conv\
+		 IF_curr_exp_conv\
          IF_curr_exp_pool_dense
 
 all:

From 6f70b3012a280c359f060f36fdd4edf950a7c5a5 Mon Sep 17 00:00:00 2001
From: emijan-kth <56410542+emijan-kth@users.noreply.github.com>
Date: Thu, 2 Feb 2023 16:17:32 +0100
Subject: [PATCH 03/15] Multiple fixes to convolutions. (#2)

Arbitrary size of convolution kernels (including non-square, non-odd sized)
Strides, in particular non-square
Fixed [row, column] order on convolution parameters (eg. padding and strides), in line with documentation comments
---
 .../IF_curr_delta_conv/Makefile               |  2 +-
 .../neuron/local_only/local_only_conv_impl.c  | 56 ++++++++++------
 .../connectors/convolution_connector.py       | 66 ++++++++++++-------
 3 files changed, 77 insertions(+), 47 deletions(-)

diff --git a/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile b/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
index 8fdc82724e..5026a0cca8 100644
--- a/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
+++ b/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
@@ -17,7 +17,7 @@ APP = $(notdir $(CURDIR))
 
 NEURON_MODEL = $(NEURON_DIR)/neuron/models/neuron_model_lif_impl.c
 NEURON_MODEL_H = $(NEURON_DIR)/neuron/models/neuron_model_lif_impl.h
-INPUT_TYPE_H = $(NEURON_DIR)/neuron/input_types/input_type_current.h
+INPUT_TYPE_H = $(NEURON_DIR)/neuron/input_types/input_type_delta.h
 NEURON_IMPL_H = $(NEURON_DIR)/neuron/implementations/neuron_impl_standard.h
 THRESHOLD_TYPE_H = $(NEURON_DIR)/neuron/threshold_types/threshold_type_static.h
 SYNAPSE_TYPE_H = $(NEURON_DIR)/neuron/synapse_types/synapse_types_delta_impl.h
diff --git a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
index 5b82bdbd95..709c7213e6 100644
--- a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
+++ b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
@@ -60,6 +60,7 @@ typedef struct {
     lc_shape_t kernel;
     lc_shape_t padding;
     lc_coord_t recip_strides;
+    lc_coord_t strides;
     lc_coord_t recip_pool_strides;
     uint16_t positive_synapse_type;
     uint16_t negative_synapse_type;
@@ -138,7 +139,15 @@ bool local_only_impl_initialise(void *address){
     return true;
 }
 
-//! \brief Multiply an integer by a 16-bit reciprocal and return the floored
+//! \brief Calculate the remainder from a division
+static inline int16_t calc_remainder(int16_t dividend, int16_t divisor, int16_t quotient) {
+    int16_t remainder = dividend - quotient * divisor;
+    log_debug("remainder: %d = %d * %d + %d",
+            dividend, quotient, divisor, remainder);
+    return remainder;
+}
+
+//! \brief Calculate remainder Multiply an integer by a 16-bit reciprocal and return the floored
 //!        integer result
 static inline int16_t recip_multiply(int16_t integer, int16_t recip) {
     int32_t i = integer;
@@ -146,19 +155,17 @@ static inline int16_t recip_multiply(int16_t integer, int16_t recip) {
     return (int16_t) ((i * r) >> RECIP_FRACT_BITS);
 }
 
-//! \brief Do a mapping from pre to post 2D spaces, we use the standard
-//! padding, kernel, strides from Convolutional Neural Networks
-//! because of the way we're looping through the kernel, we divide the kernel
-//! shape by 2.
-static inline lc_coord_t map_pre_to_post(connector *connector, lc_coord_t pre,
-        int16_t half_kh, int16_t half_kw) {
-    lc_coord_t post = pre;
-    post.col = recip_multiply(post.col, connector->recip_pool_strides.col);
-    post.row = recip_multiply(post.row, connector->recip_pool_strides.row);
-    post.col = post.col - half_kw + connector->padding.width;
-    post.row = post.row - half_kh + connector->padding.height;
-    post.col = recip_multiply(post.col, connector->recip_strides.col);
-    post.row = recip_multiply(post.row, connector->recip_strides.row);
+//! \brief Do a mapping from pre to post 2D spaces
+static inline lc_coord_t map_pre_to_post(connector *connector, lc_coord_t pre, lc_coord_t *start_i) {
+    pre.col = recip_multiply(pre.col, connector->recip_pool_strides.col);
+    pre.row = recip_multiply(pre.row, connector->recip_pool_strides.row);
+    pre.col += connector->padding.width;
+    pre.row += connector->padding.height;
+    lc_coord_t post;
+    post.col = recip_multiply(pre.col, connector->recip_strides.col);
+    post.row = recip_multiply(pre.row, connector->recip_strides.row);
+    start_i->col = calc_remainder(pre.col, connector->strides.col, post.col);
+    start_i->row = calc_remainder(pre.row, connector->strides.row, post.row);
     return post;
 }
 
@@ -169,21 +176,26 @@ static inline lc_coord_t map_pre_to_post(connector *connector, lc_coord_t pre,
 static inline void do_convolution_operation(
         uint32_t time, lc_coord_t pre_coord, connector *connector,
         uint16_t *ring_buffers) {
-    int32_t half_kh = connector->kernel.height / 2;
-    int32_t half_kw = connector->kernel.width / 2;
-    lc_coord_t post_coord = map_pre_to_post(connector, pre_coord, half_kh, half_kw);
+    lc_coord_t start_i;
+    log_debug("kernel height: %d, kernel width: %d, padding height: %d, padding width: %d, strides row: %d, strides col: %d", connector->kernel.height, connector->kernel.width, connector->padding.height, connector->padding.width, connector->strides.row, connector->strides.col);
+    lc_coord_t post_coord = map_pre_to_post(connector, pre_coord, &start_i);
     log_debug("pre row %d, col %d AS post row %d, col %d",
             pre_coord.row, pre_coord.col, post_coord.row, post_coord.col);
 
     int32_t kw = connector->kernel.width;
-    for (int32_t r = -half_kh, kr = 0; r <= half_kh; r++, kr++) {
-        int32_t tmp_row = post_coord.row + r;
+    for (int32_t i_row = start_i.row, tmp_row = post_coord.row; i_row < connector->kernel.height; i_row += connector->strides.row, --tmp_row) {
+        int32_t kr = connector->kernel.height - 1 - i_row;
+        log_debug("i_row = %u, kr = %u, tmp_row = %u", i_row, kr, tmp_row);
+
         if ((tmp_row < config.post_start.row) || (tmp_row > config.post_end.row)) {
+            log_debug("tmp_row outside");
             continue;
         }
-        for (int32_t c = -half_kw, kc = 0; c <= half_kw; c++, kc++) {
-            int32_t tmp_col = post_coord.col + c;
+        for (int32_t i_col = start_i.col, tmp_col = post_coord.col; i_col < connector->kernel.width; i_col += connector->strides.col, --tmp_col) {
+            int32_t kc = connector->kernel.width - 1 - i_col;
+            log_debug("i_col = %u, kc = %u, tmp_col = %u", i_col, kc, tmp_col);
             if ((tmp_col < config.post_start.col) || (tmp_col > config.post_end.col)) {
+                log_debug("tmp_col outside");
                 continue;
             }
 
@@ -192,8 +204,10 @@ static inline void do_convolution_operation(
                 ((tmp_row - config.post_start.row) * config.post_shape.width)
                     + (tmp_col - config.post_start.col);
             uint32_t k = (kr * kw) + kc;
+            log_debug("weight index = %u", k);
             lc_weight_t weight = connector->weights[k];
             if (weight == 0) {
+                log_debug("zero weight");
                 continue;
             }
             uint32_t rb_index = 0;
diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index 55d699f554..9bda1e36c8 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -33,7 +33,7 @@
 
 #: The number of 16-bit shorts in the connector struct,
 #: ignoring the source_key_info struct and the weights (which are dynamic)
-CONNECTOR_CONFIG_SHORTS = 12
+CONNECTOR_CONFIG_SHORTS = 14
 
 
 class ConvolutionConnector(AbstractConnector):
@@ -215,11 +215,10 @@ def get_post_shape(self, shape):
             shape = (post_pool_shape // self.__pool_stride) + 1
 
         kernel_shape = numpy.array(self.__kernel_weights.shape)
-        post_shape = (shape - (kernel_shape - 1) +
-                      (2 * self.__padding_shape))
+        post_shape = shape - kernel_shape + (2 * self.__padding_shape)
 
         return numpy.clip(
-            post_shape // self.__strides, 1, numpy.inf).astype('int')
+            post_shape // self.__strides + 1, 1, numpy.inf).astype('int')
 
     @overrides(AbstractConnector.validate_connection)
     def validate_connection(self, application_edge, synapse_info):
@@ -230,7 +229,9 @@ def validate_connection(self, application_edge, synapse_info):
                 "The ConvolutionConnector only works where the Populations"
                 " of a Projection are both 2D.  Please ensure that both the"
                 " Populations use a Grid2D structure.")
-        expected_post_shape = tuple(self.get_post_shape(pre.atoms_shape))
+        pre_shape = pre.atoms_shape
+        expected_post_shape = tuple(self.get_post_shape((pre_shape[1], pre_shape[0])))
+        expected_post_shape = expected_post_shape[1], expected_post_shape[0]
         if expected_post_shape != post.atoms_shape:
             raise ConfigurationException(
                 f"With a source population with shape {pre.atoms_shape}, "
@@ -281,10 +282,20 @@ def get_connected_vertices(self, s_info, source_vertex, target_vertex):
         pre_slices = [m_vertex.vertex_slice for m_vertex in pre_vertices]
         pre_slices_x = [vtx_slice.get_slice(0) for vtx_slice in pre_slices]
         pre_slices_y = [vtx_slice.get_slice(1) for vtx_slice in pre_slices]
-        pre_ranges = [[[px.start, py.start], [px.stop - 1, py.stop - 1]]
+        pre_ranges = [[[py.start, px.start], [py.stop - 1, px.stop - 1]]
                       for px, py in zip(pre_slices_x, pre_slices_y)]
-        pres_as_posts = self.__pre_as_post(pre_ranges)
-        hlf_k_w, hlf_k_h = numpy.array(self.__kernel_weights.shape) // 2
+        pre_vertex_in_post_layer, start_i = self.__pre_as_post(pre_ranges)
+
+        pre_vertex_in_post_layer_upper_left = pre_vertex_in_post_layer[:,0]
+        pre_vertex_in_post_layer_lower_right = pre_vertex_in_post_layer[:,1]
+
+        kernel_shape = numpy.array(self.__kernel_weights.shape)
+
+        j = (kernel_shape - 1 - start_i) // self.__strides
+        j_upper_left = j[:,0]
+
+        pre_vertex_max_reach_in_post_layer_upper_left = pre_vertex_in_post_layer_upper_left - j_upper_left
+        pre_vertex_max_reach_in_post_layer_lower_right = pre_vertex_in_post_layer_lower_right
 
         connected = list()
         for post in target_vertex.splitter.get_in_coming_vertices(
@@ -293,18 +304,18 @@ def get_connected_vertices(self, s_info, source_vertex, target_vertex):
             post_slice_x = post_slice.get_slice(0)
             post_slice_y = post_slice.get_slice(1)
 
-            # Get ranges allowed in post
-            min_x = post_slice_x.start - hlf_k_w
-            max_x = (post_slice_x.stop + hlf_k_w) - 1
-            min_y = post_slice_y.start - hlf_k_h
-            max_y = (post_slice_y.stop + hlf_k_h) - 1
+            # Get ranges allowed in post vertex
+            min_x = post_slice_x.start
+            max_x = post_slice_x.stop - 1
+            min_y = post_slice_y.start
+            max_y = post_slice_y.stop - 1
 
             # Test that the start coords are in range i.e. less than max
             start_in_range = numpy.logical_not(
-                numpy.any(pres_as_posts[:, 0] > [max_x, max_y], axis=1))
+                numpy.any(pre_vertex_max_reach_in_post_layer_upper_left > [max_y, max_x], axis=1))
             # Test that the end coords are in range i.e. more than min
             end_in_range = numpy.logical_not(
-                numpy.any(pres_as_posts[:, 1] < [min_x, min_y], axis=1))
+                numpy.any(pre_vertex_max_reach_in_post_layer_lower_right < [min_y, min_x], axis=1))
             # When both things are true, we have a vertex in range
             pre_in_range = pre_vertices[
                 numpy.logical_and(start_in_range, end_in_range)]
@@ -315,17 +326,18 @@ def get_connected_vertices(self, s_info, source_vertex, target_vertex):
     def __pre_as_post(self, pre_coords):
         """ Write pre coords as post coords.
 
-        :param Iterable pre_coords: An iterable of (x, y) coordinates
+        :param Iterable pre_coords: An iterable of (y, x) coordinates
         :rtype: numpy.ndarray
         """
         coords = numpy.array(pre_coords)
         if self.__pool_stride is not None:
             coords //= self.__pool_stride
 
-        kernel_shape = numpy.array(self.__kernel_weights.shape)
-        coords = coords - kernel_shape // 2 + self.__padding_shape
-        coords //= self.__strides
-        return coords
+        coords += self.__padding_shape
+        coord_by_strides = coords // self.__strides
+        start_i = coords % self.__strides
+
+        return coord_by_strides, start_i
 
     @property
     def local_only_n_bytes(self):
@@ -343,9 +355,9 @@ def write_local_only_data(
             weight_scales):
         # Get info about things
         kernel_shape = self.__kernel_weights.shape
-        ps_x, ps_y = 1, 1
+        ps_y, ps_x = 1, 1
         if self.__pool_stride is not None:
-            ps_x, ps_y = self.__pool_stride
+            ps_y, ps_x = self.__pool_stride
 
         # Write source key info
         spec.write_value(key, data_type=DataType.UINT32)
@@ -376,13 +388,17 @@ def write_local_only_data(
         # Write remaining connector details
         spec.write_value(start[1], data_type=DataType.INT16)
         spec.write_value(start[0], data_type=DataType.INT16)
-        spec.write_value(kernel_shape[1], data_type=DataType.INT16)
         spec.write_value(kernel_shape[0], data_type=DataType.INT16)
-        spec.write_value(self.__padding_shape[1], data_type=DataType.INT16)
+        spec.write_value(kernel_shape[1], data_type=DataType.INT16)
         spec.write_value(self.__padding_shape[0], data_type=DataType.INT16)
+        spec.write_value(self.__padding_shape[1], data_type=DataType.INT16)
+        spec.write_value(self.__recip(self.__strides[0]),
+                         data_type=DataType.INT16)
         spec.write_value(self.__recip(self.__strides[1]),
                          data_type=DataType.INT16)
-        spec.write_value(self.__recip(self.__strides[0]),
+        spec.write_value(self.__strides[0],
+                         data_type=DataType.INT16)
+        spec.write_value(self.__strides[1],
                          data_type=DataType.INT16)
         spec.write_value(self.__recip(ps_y), data_type=DataType.INT16)
         spec.write_value(self.__recip(ps_x), data_type=DataType.INT16)

From bb90642846ec993975146c9eeee6e9880609c0e5 Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Fri, 10 Feb 2023 20:30:20 +0100
Subject: [PATCH 04/15] Fixed merge error: local_only_delays should have
 increased size of connector struct.

---
 .../neural_projections/connectors/convolution_connector.py      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index 2a6ac1ffc0..eb0b09a85f 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -34,7 +34,7 @@
 
 #: The number of 16-bit shorts in the connector struct,
 #: ignoring the source_key_info struct and the weights (which are dynamic)
-CONNECTOR_CONFIG_SHORTS = 14
+CONNECTOR_CONFIG_SHORTS = 16
 
 
 class ConvolutionConnector(AbstractConnector):

From 91eeed643a03be914b79467fadc0e941a1e3d818 Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Fri, 10 Feb 2023 20:39:36 +0100
Subject: [PATCH 05/15] Added option to ConvolutionConnector for delays varying
 horizontally over kernel.

---
 .../src/neuron/local_only/local_only_conv_impl.c    | 13 +++++++++++--
 .../connectors/convolution_connector.py             | 13 ++++++++++---
 2 files changed, 21 insertions(+), 5 deletions(-)

diff --git a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
index 58d2c98c6d..0d50f3e684 100644
--- a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
+++ b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
@@ -65,6 +65,7 @@ typedef struct {
     uint16_t positive_synapse_type;
     uint16_t negative_synapse_type;
     uint32_t delay;
+    uint32_t strides_delay_step;
     lc_weight_t weights[]; // n_weights = next_even(kernel.width * kernel.height)
 } connector;
 
@@ -192,6 +193,14 @@ static inline void do_convolution_operation(
             log_debug("tmp_row outside");
             continue;
         }
+
+        uint32_t delay = connector->delay;
+        if (connector->strides_delay_step != 0)
+        {
+            delay -= start_i.col * connector->strides_delay_step;
+            log_debug("start_i.col = %u, delay = %u", start_i.col, delay);
+        }
+        
         for (int32_t i_col = start_i.col, tmp_col = post_coord.col; i_col < connector->kernel.width; i_col += connector->strides.col, --tmp_col) {
             int32_t kc = connector->kernel.width - 1 - i_col;
             log_debug("i_col = %u, kc = %u, tmp_col = %u", i_col, kc, tmp_col);
@@ -213,12 +222,12 @@ static inline void do_convolution_operation(
             }
             uint32_t rb_index = 0;
             if (weight > 0) {
-                rb_index = synapse_row_get_ring_buffer_index(time + connector->delay,
+                rb_index = synapse_row_get_ring_buffer_index(time + delay,
                     connector->positive_synapse_type, post_index,
                     synapse_type_index_bits, synapse_index_bits,
                     synapse_delay_mask);
             } else {
-                rb_index = synapse_row_get_ring_buffer_index(time + connector->delay,
+                rb_index = synapse_row_get_ring_buffer_index(time + delay,
                     connector->negative_synapse_type, post_index,
                     synapse_type_index_bits, synapse_index_bits,
                     synapse_delay_mask);
diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index eb0b09a85f..5dcf453bb9 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -34,7 +34,7 @@
 
 #: The number of 16-bit shorts in the connector struct,
 #: ignoring the source_key_info struct and the weights (which are dynamic)
-CONNECTOR_CONFIG_SHORTS = 16
+CONNECTOR_CONFIG_SHORTS = 18
 
 
 class ConvolutionConnector(AbstractConnector):
@@ -51,13 +51,16 @@ class ConvolutionConnector(AbstractConnector):
         "__pool_shape",
         "__pool_stride",
         "__positive_receptor_type",
-        "__negative_receptor_type"
+        "__negative_receptor_type",
+        "__horizontal_delay_step"
     ]
 
     def __init__(self, kernel_weights, kernel_shape=None, strides=None,
                  padding=None, pool_shape=None, pool_stride=None,
                  positive_receptor_type="excitatory",
-                 negative_receptor_type="inhibitory", safe=True,
+                 negative_receptor_type="inhibitory",
+                 horizontal_delay_step=0,
+                 safe=True,
                  verbose=False, callback=None):
         """
         :param kernel_weights:
@@ -135,6 +138,8 @@ def __init__(self, kernel_weights, kernel_shape=None, strides=None,
         self.__positive_receptor_type = positive_receptor_type
         self.__negative_receptor_type = negative_receptor_type
 
+        self.__horizontal_delay_step = horizontal_delay_step
+
     @property
     def positive_receptor_type(self):
         return self.__positive_receptor_type
@@ -416,6 +421,8 @@ def write_local_only_data(
         spec.write_value(app_edge.post_vertex.synapse_dynamics.delay *
                          SpynnakerDataView.get_simulation_time_step_per_ms())
 
+        spec.write_value(self.__horizontal_delay_step, data_type=DataType.UINT32)
+
         # Encode weights with weight scaling
         encoded_kernel_weights = self.__kernel_weights.flatten()
         if len(encoded_kernel_weights) % 2 != 0:

From 4003c952a555714f1426274eabd1ab255c45c11d Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Wed, 15 Feb 2023 10:07:23 +0100
Subject: [PATCH 06/15] shapes and delays

---
 .../neural_projections/connectors/convolution_connector.py   | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index 5dcf453bb9..88d78fc1ba 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -138,6 +138,9 @@ def __init__(self, kernel_weights, kernel_shape=None, strides=None,
         self.__positive_receptor_type = positive_receptor_type
         self.__negative_receptor_type = negative_receptor_type
 
+        if horizontal_delay_step:
+            self.__kernel_shape = self.__get_kernel_shape(kernel_shape)
+
         self.__horizontal_delay_step = horizontal_delay_step
 
     @property
@@ -187,6 +190,8 @@ def __decode_kernel(self, w, shape):
             raise SynapticConfigurationException(
                 f"Unknown combination of kernel_weights ({w}) and"
                 f" kernel_shape ({shape})")
+        
+        self.__kernel_shape = self.__kernel_weights.shape
 
     @staticmethod
     def __to_2d_shape(shape, param_name):

From 584ed954881ec3430fe428d1162be0cd105f74b2 Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Thu, 16 Feb 2023 11:25:25 +0100
Subject: [PATCH 07/15] Set kernel_shape overrides shape of kernel_weights

---
 .../connectors/convolution_connector.py                  | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index 88d78fc1ba..f396edee3f 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -46,6 +46,7 @@ class ConvolutionConnector(AbstractConnector):
 
     __slots__ = [
         "__kernel_weights",
+        "__kernel_shape",
         "__strides",
         "__padding_shape",
         "__pool_shape",
@@ -225,7 +226,7 @@ def get_post_shape(self, shape):
             post_pool_shape = shape - (self.__pool_shape - 1)
             shape = (post_pool_shape // self.__pool_stride) + 1
 
-        kernel_shape = numpy.array(self.__kernel_weights.shape)
+        kernel_shape = numpy.array(self.__kernel_shape)
         post_shape = shape - kernel_shape + (2 * self.__padding_shape)
 
         return numpy.clip(
@@ -274,12 +275,12 @@ def get_delay_maximum(self, synapse_info):
     def get_n_connections_from_pre_vertex_maximum(
             self, n_post_atoms, synapse_info, min_delay=None,
             max_delay=None):
-        w, h = self.__kernel_weights.shape
+        w, h = self.__kernel_shape
         return numpy.clip(w * h, 0, n_post_atoms)
 
     @overrides(AbstractConnector.get_n_connections_to_post_vertex_maximum)
     def get_n_connections_to_post_vertex_maximum(self, synapse_info):
-        w, h = self.__kernel_weights.shape
+        w, h = self.__kernel_shape
         return numpy.clip(w * h, 0, synapse_info.n_pre_neurons)
 
     @overrides(AbstractConnector.get_weight_maximum)
@@ -300,7 +301,7 @@ def get_connected_vertices(self, s_info, source_vertex, target_vertex):
         pre_vertex_in_post_layer_upper_left = pre_vertex_in_post_layer[:,0]
         pre_vertex_in_post_layer_lower_right = pre_vertex_in_post_layer[:,1]
 
-        kernel_shape = numpy.array(self.__kernel_weights.shape)
+        kernel_shape = numpy.array(self.__kernel_shape)
 
         j = (kernel_shape - 1 - start_i) // self.__strides
         j_upper_left = j[:,0]

From 13cf51e3c053fd1e86ba2a0b0baf98c76a69c8bd Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Thu, 16 Feb 2023 21:43:21 +0100
Subject: [PATCH 08/15] Multisynaptic connections with varying delays.

---
 .../neuron/local_only/local_only_conv_impl.c  | 109 +++++++++++-------
 .../connectors/convolution_connector.py       |  25 ++--
 2 files changed, 85 insertions(+), 49 deletions(-)

diff --git a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
index 0d50f3e684..b133b969ff 100644
--- a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
+++ b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
@@ -64,8 +64,14 @@ typedef struct {
     lc_coord_t recip_pool_strides;
     uint16_t positive_synapse_type;
     uint16_t negative_synapse_type;
-    uint32_t delay;
-    uint32_t strides_delay_step;
+    union {
+        uint32_t delay;
+        struct {
+            uint16_t multisynaptic_delay_max;
+            uint16_t multisynaptic_delay_step;
+        };
+    };
+    uint32_t num_multisynaptic_connections;
     lc_weight_t weights[]; // n_weights = next_even(kernel.width * kernel.height)
 } connector;
 
@@ -194,55 +200,74 @@ static inline void do_convolution_operation(
             continue;
         }
 
-        uint32_t delay = connector->delay;
-        if (connector->strides_delay_step != 0)
-        {
-            delay -= start_i.col * connector->strides_delay_step;
-            log_debug("start_i.col = %u, delay = %u", start_i.col, delay);
-        }
-        
         for (int32_t i_col = start_i.col, tmp_col = post_coord.col; i_col < connector->kernel.width; i_col += connector->strides.col, --tmp_col) {
             int32_t kc = connector->kernel.width - 1 - i_col;
+
+            uint32_t delay;
+            if (connector->num_multisynaptic_connections == 1)
+            {
+                delay = connector->delay;
+            }
+            else
+            {
+                kc = connector->kernel.width - 1 - connector->num_multisynaptic_connections * i_col;
+                if (kc < 0)
+                {
+                    log_debug("Multisynaptic connection: i_col = %u, kc = %u, tmp_col = %u", i_col, kc, tmp_col);
+                    break;
+                }
+                delay = connector->multisynaptic_delay_max;
+                log_debug("Multisynaptic connection: start_i.col = %u, delay = %u", start_i.col, delay);
+            }
+
             log_debug("i_col = %u, kc = %u, tmp_col = %u", i_col, kc, tmp_col);
+
             if ((tmp_col < config.post_start.col) || (tmp_col > config.post_end.col)) {
                 log_debug("tmp_col outside");
                 continue;
             }
 
-            // This the neuron id relative to the neurons on this core
-            uint32_t post_index =
-                ((tmp_row - config.post_start.row) * config.post_shape.width)
-                    + (tmp_col - config.post_start.col);
-            uint32_t k = (kr * kw) + kc;
-            log_debug("weight index = %u", k);
-            lc_weight_t weight = connector->weights[k];
-            if (weight == 0) {
-                log_debug("zero weight");
-                continue;
-            }
-            uint32_t rb_index = 0;
-            if (weight > 0) {
-                rb_index = synapse_row_get_ring_buffer_index(time + delay,
-                    connector->positive_synapse_type, post_index,
-                    synapse_type_index_bits, synapse_index_bits,
-                    synapse_delay_mask);
-            } else {
-                rb_index = synapse_row_get_ring_buffer_index(time + delay,
-                    connector->negative_synapse_type, post_index,
-                    synapse_type_index_bits, synapse_index_bits,
-                    synapse_delay_mask);
-                weight = -weight;
-            }
-            log_debug("Updating ring_buffers[%u] for post neuron %u = %u, %u, with weight %u",
-                    rb_index, post_index, tmp_col, tmp_row, weight);
-
-            // Add weight to current ring buffer value, avoiding saturation
-            uint32_t accumulation = ring_buffers[rb_index] + weight;
-            uint32_t sat_test = accumulation & 0x10000;
-            if (sat_test) {
-                accumulation = sat_test - 1;
+            for (int32_t multisynapse_index = 0;
+                multisynapse_index < connector->num_multisynaptic_connections;
+                ++multisynapse_index, delay -= connector->multisynaptic_delay_step, --kc)
+            {
+                log_debug("kc = %u, delay = %u", kc, delay);
+
+                // This the neuron id relative to the neurons on this core
+                uint32_t post_index =
+                    ((tmp_row - config.post_start.row) * config.post_shape.width)
+                        + (tmp_col - config.post_start.col);
+                uint32_t k = (kr * kw) + kc;
+                log_debug("weight index = %u", k);
+                lc_weight_t weight = connector->weights[k];
+                if (weight == 0) {
+                    log_debug("zero weight");
+                    continue;
+                }
+                uint32_t rb_index = 0;
+                if (weight > 0) {
+                    rb_index = synapse_row_get_ring_buffer_index(time + delay,
+                        connector->positive_synapse_type, post_index,
+                        synapse_type_index_bits, synapse_index_bits,
+                        synapse_delay_mask);
+                } else {
+                    rb_index = synapse_row_get_ring_buffer_index(time + delay,
+                        connector->negative_synapse_type, post_index,
+                        synapse_type_index_bits, synapse_index_bits,
+                        synapse_delay_mask);
+                    weight = -weight;
+                }
+                log_debug("Updating ring_buffers[%u] for post neuron %u = %u, %u, with weight %u",
+                        rb_index, post_index, tmp_col, tmp_row, weight);
+
+                // Add weight to current ring buffer value, avoiding saturation
+                uint32_t accumulation = ring_buffers[rb_index] + weight;
+                uint32_t sat_test = accumulation & 0x10000;
+                if (sat_test) {
+                    accumulation = sat_test - 1;
+                }
+                ring_buffers[rb_index] = accumulation;
             }
-            ring_buffers[rb_index] = accumulation;
         }
     }
 }
diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index f396edee3f..a1c1b5c653 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -53,14 +53,16 @@ class ConvolutionConnector(AbstractConnector):
         "__pool_stride",
         "__positive_receptor_type",
         "__negative_receptor_type",
-        "__horizontal_delay_step"
+        "__num_multisynaptic_connections",
+        "__multisynaptic_delay_min"
     ]
 
     def __init__(self, kernel_weights, kernel_shape=None, strides=None,
                  padding=None, pool_shape=None, pool_stride=None,
                  positive_receptor_type="excitatory",
                  negative_receptor_type="inhibitory",
-                 horizontal_delay_step=0,
+                 num_multisynaptic_connections=1,
+                 multisynaptic_delay_min=0,
                  safe=True,
                  verbose=False, callback=None):
         """
@@ -139,10 +141,11 @@ def __init__(self, kernel_weights, kernel_shape=None, strides=None,
         self.__positive_receptor_type = positive_receptor_type
         self.__negative_receptor_type = negative_receptor_type
 
-        if horizontal_delay_step:
+        self.__num_multisynaptic_connections = num_multisynaptic_connections
+        if num_multisynaptic_connections != 1:
             self.__kernel_shape = self.__get_kernel_shape(kernel_shape)
+            self.__multisynaptic_delay_min = multisynaptic_delay_min
 
-        self.__horizontal_delay_step = horizontal_delay_step
 
     @property
     def positive_receptor_type(self):
@@ -424,10 +427,18 @@ def write_local_only_data(
         spec.write_value(neg_synapse_type, data_type=DataType.UINT16)
 
         # Write delay
-        spec.write_value(app_edge.post_vertex.synapse_dynamics.delay *
-                         SpynnakerDataView.get_simulation_time_step_per_ms())
+        max_delay = app_edge.post_vertex.synapse_dynamics.delay * \
+                            SpynnakerDataView.get_simulation_time_step_per_ms()
 
-        spec.write_value(self.__horizontal_delay_step, data_type=DataType.UINT32)
+        if self.__num_multisynaptic_connections == 1:
+            spec.write_value(max_delay)
+        else:
+            spec.write_value(max_delay, data_type=DataType.UINT16)
+            delay_range = max_delay - self.__multisynaptic_delay_min
+            delay_step = delay_range / (self.__num_multisynaptic_connections - 1)
+            spec.write_value(delay_step, data_type=DataType.UINT16)
+
+        spec.write_value(self.__num_multisynaptic_connections, data_type=DataType.UINT32)
 
         # Encode weights with weight scaling
         encoded_kernel_weights = self.__kernel_weights.flatten()

From 986aa80d5174953f17c6d2eddd3236fa2ba6bd6d Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Mon, 27 Feb 2023 11:02:43 +0100
Subject: [PATCH 09/15] Revert "Added option to ConvolutionConnector for delays
 varying horizontally over kernel."

This reverts commit 91eeed643a03be914b79467fadc0e941a1e3d818.
---
 .../src/neuron/local_only/local_only_conv_impl.c    | 13 ++-----------
 .../connectors/convolution_connector.py             | 13 +++----------
 2 files changed, 5 insertions(+), 21 deletions(-)

diff --git a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
index 53dd5bc891..2f2e2616f7 100644
--- a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
+++ b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
@@ -65,7 +65,6 @@ typedef struct {
     uint16_t positive_synapse_type;
     uint16_t negative_synapse_type;
     uint32_t delay;
-    uint32_t strides_delay_step;
     lc_weight_t weights[]; // n_weights = next_even(kernel.width * kernel.height)
 } connector;
 
@@ -193,14 +192,6 @@ static inline void do_convolution_operation(
             log_debug("tmp_row outside");
             continue;
         }
-
-        uint32_t delay = connector->delay;
-        if (connector->strides_delay_step != 0)
-        {
-            delay -= start_i.col * connector->strides_delay_step;
-            log_debug("start_i.col = %u, delay = %u", start_i.col, delay);
-        }
-        
         for (int32_t i_col = start_i.col, tmp_col = post_coord.col; i_col < connector->kernel.width; i_col += connector->strides.col, --tmp_col) {
             int32_t kc = connector->kernel.width - 1 - i_col;
             log_debug("i_col = %u, kc = %u, tmp_col = %u", i_col, kc, tmp_col);
@@ -222,12 +213,12 @@ static inline void do_convolution_operation(
             }
             uint32_t rb_index = 0;
             if (weight > 0) {
-                rb_index = synapse_row_get_ring_buffer_index(time + delay,
+                rb_index = synapse_row_get_ring_buffer_index(time + connector->delay,
                     connector->positive_synapse_type, post_index,
                     synapse_type_index_bits, synapse_index_bits,
                     synapse_delay_mask);
             } else {
-                rb_index = synapse_row_get_ring_buffer_index(time + delay,
+                rb_index = synapse_row_get_ring_buffer_index(time + connector->delay,
                     connector->negative_synapse_type, post_index,
                     synapse_type_index_bits, synapse_index_bits,
                     synapse_delay_mask);
diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index 2cdb4fe286..74b7224b3c 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -34,7 +34,7 @@
 
 #: The number of 16-bit shorts in the connector struct,
 #: ignoring the source_key_info struct and the weights (which are dynamic)
-CONNECTOR_CONFIG_SHORTS = 18
+CONNECTOR_CONFIG_SHORTS = 16
 
 
 class ConvolutionConnector(AbstractConnector):
@@ -51,16 +51,13 @@ class ConvolutionConnector(AbstractConnector):
         "__pool_shape",
         "__pool_stride",
         "__positive_receptor_type",
-        "__negative_receptor_type",
-        "__horizontal_delay_step"
+        "__negative_receptor_type"
     ]
 
     def __init__(self, kernel_weights, kernel_shape=None, strides=None,
                  padding=None, pool_shape=None, pool_stride=None,
                  positive_receptor_type="excitatory",
-                 negative_receptor_type="inhibitory",
-                 horizontal_delay_step=0,
-                 safe=True,
+                 negative_receptor_type="inhibitory", safe=True,
                  verbose=False, callback=None):
         """
         :param kernel_weights:
@@ -138,8 +135,6 @@ def __init__(self, kernel_weights, kernel_shape=None, strides=None,
         self.__positive_receptor_type = positive_receptor_type
         self.__negative_receptor_type = negative_receptor_type
 
-        self.__horizontal_delay_step = horizontal_delay_step
-
     @property
     def positive_receptor_type(self):
         return self.__positive_receptor_type
@@ -426,8 +421,6 @@ def write_local_only_data(
                           app_edge.post_vertex.splitter.max_support_delay())
         spec.write_value(local_delay)
 
-        spec.write_value(self.__horizontal_delay_step, data_type=DataType.UINT32)
-
         # Encode weights with weight scaling
         encoded_kernel_weights = self.__kernel_weights.flatten()
         if len(encoded_kernel_weights) % 2 != 0:

From 7d2a5bfbd800942a186b6b9a16bc9c9a5ff2902c Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Mon, 27 Feb 2023 12:04:54 +0100
Subject: [PATCH 10/15] Fixed merge error: should have increased size of
 connector struct.

---
 .../neural_projections/connectors/convolution_connector.py      | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index 64d8b258f3..8d876b1d15 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -34,7 +34,7 @@
 
 #: The number of 16-bit shorts in the connector struct,
 #: ignoring the source_key_info struct and the weights (which are dynamic)
-CONNECTOR_CONFIG_SHORTS = 16
+CONNECTOR_CONFIG_SHORTS = 18
 
 
 class ConvolutionConnector(AbstractConnector):

From af90e25d483744ce942231cb10a654374733caaa Mon Sep 17 00:00:00 2001
From: Emil Jansson <emijan@kth.se>
Date: Sat, 4 Mar 2023 10:21:23 +0100
Subject: [PATCH 11/15] Completed merge.

---
 .../neuron/local_only/local_only_conv_impl.c  | 64 +++++++--------
 .../connectors/convolution_connector.py       | 77 +++++++------------
 2 files changed, 56 insertions(+), 85 deletions(-)

diff --git a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
index b05244b9e5..87fa7f517b 100644
--- a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
+++ b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
@@ -89,7 +89,15 @@ typedef struct {
 // The main configuration data
 static conv_config *config;
 
-static lc_weight_t *weights;t_simulation_time_st
+static lc_weight_t *weights;
+
+//! \brief Load the required data into DTCM.
+bool local_only_impl_initialise(void *address){
+    log_info("+++++++++++++++++ CONV init ++++++++++++++++++++");
+    conv_config* sdram_config = address;
+    uint32_t n_bytes = sizeof(conv_config) +
+    		(sizeof(connector) * sdram_config->n_connectors);
+    config = spin1_malloc(n_bytes);
     if (config == NULL) {
     	log_error("Can't allocate memory for config!");
     	return false;
@@ -187,7 +195,7 @@ static inline void do_convolution_operation(
         int32_t kr = connector->kernel.height - 1 - i_row;
         log_debug("i_row = %u, kr = %u, tmp_row = %u", i_row, kr, tmp_row);
 
-        if ((tmp_row < config.post_start.row) || (tmp_row > config.post_end.row)) {
+        if ((tmp_row < config->post_start.row) || (tmp_row > config->post_end.row)) {
             log_debug("tmp_row outside");
             continue;
         }
@@ -214,7 +222,7 @@ static inline void do_convolution_operation(
 
             log_debug("i_col = %u, kc = %u, tmp_col = %u", i_col, kc, tmp_col);
 
-            if ((tmp_col < config.post_start.col) || (tmp_col > config.post_end.col)) {
+            if ((tmp_col < config->post_start.col) || (tmp_col > config->post_end.col)) {
                 log_debug("tmp_col outside");
                 continue;
             }
@@ -227,8 +235,8 @@ static inline void do_convolution_operation(
 
             // This the neuron id relative to the neurons on this core
             uint32_t post_index =
-                ((tmp_row - config.post_start.row) * config.post_shape.width)
-                    + (tmp_col - config.post_start.col);
+                ((tmp_row - config->post_start.row) * config->post_shape.width)
+                    + (tmp_col - config->post_start.col);
             uint32_t k = (kr * kw) + kc;
             log_debug("weight index = %u", k);
             lc_weight_t weight = connector_weights[k];
@@ -266,8 +274,8 @@ static inline void do_convolution_operation(
 
 static inline bool key_to_index_lookup(uint32_t spike, connector **conn,
         uint32_t *core_local_col, uint32_t *core_local_row) {
-    for (uint32_t i = 0; i < config.n_connectors; i++) {
-        connector *c = connectors[i];
+    for (uint32_t i = 0; i < config->n_connectors; i++) {
+        connector *c = &(config->connectors[i]);
         if ((spike & c->key_info.mask) == c->key_info.key) {
         	uint32_t local_spike = (spike & ~c->key_info.mask) >> c->key_info.n_colour_bits;
             *conn = c;
@@ -279,14 +287,6 @@ static inline bool key_to_index_lookup(uint32_t spike, connector **conn,
     return false;
 }
 
-static inline void get_row_col(uint32_t spike, uint32_t index,
-		uint32_t *core_local_col, uint32_t *core_local_row) {
-	connector *c = connectors[index];
-	uint32_t local_spike = (spike & ~c->key_info.mask) >> c->key_info.n_colour_bits;
-	*core_local_col = (local_spike & c->key_info.col_mask) >> c->key_info.col_shift;
-	*core_local_row = (local_spike & c->key_info.row_mask) >> c->key_info.row_shift;
-}
-
 //! \brief Process incoming spikes. In this implementation we need to:
 //! 1. Check if it's in the population table
 //! 2. Convert the relative (per core) Id to a global (per population) one
@@ -295,30 +295,24 @@ static inline void get_row_col(uint32_t spike, uint32_t index,
 //! 4. Add the weights to the appropriate current buffers
 void local_only_impl_process_spike(
         uint32_t time, uint32_t spike, uint16_t* ring_buffers) {
+    connector *connector;
+    uint32_t core_local_col;
+    uint32_t core_local_row;
 
     // Lookup the spike, and if found, get the appropriate parts
-    uint32_t start;
-    uint32_t end;
-    if (!key_to_index_lookup(spike, &start, &end)) {
-    	log_warning("Spike %u didn't match any connectors!", spike);
+    if (!key_to_index_lookup(
+            spike, &connector, &core_local_col, &core_local_row)) {
         return;
     }
-    log_debug("Received spike %u, using connectors between %u and %u", spike, start, end);
 
     // compute the population-based coordinates
-    for (uint32_t i = start; i < end; i++) {
-		uint32_t core_local_col;
-		uint32_t core_local_row;
-		connector *connector = connectors[i];
-		get_row_col(spike, i, &core_local_col, &core_local_row);
-		lc_coord_t pre_coord = {
-				core_local_row + connector->pre_start.row,
-				core_local_col + connector->pre_start.col
-		};
-		log_debug("Spike %u = %u, %u (Global: %u, %u)", spike, core_local_col, core_local_row,
-				pre_coord.col, pre_coord.row);
-
-		// Compute the convolution
-		do_convolution_operation(time, pre_coord, connector, ring_buffers);
-    }
+    lc_coord_t pre_coord = {
+            core_local_row + connector->pre_start.row,
+            core_local_col + connector->pre_start.col
+    };
+    log_debug("Received spike %u = %u, %u (Global: %u, %u)", spike, core_local_col, core_local_row,
+            pre_coord.col, pre_coord.row);
+
+    // Compute the convolution
+    do_convolution_operation(time, pre_coord, connector, ring_buffers);
 }
diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index e0d3b57725..2c540d6009 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -33,7 +33,7 @@
 
 #: The number of 16-bit shorts in the connector struct,
 #: ignoring the source_key_info struct and the weights (which are dynamic)
-CONNECTOR_CONFIG_SHORTS = 18
+CONNECTOR_CONFIG_SHORTS = 20
 
 
 class ConvolutionConnector(AbstractConnector):
@@ -340,13 +340,25 @@ def get_connected_vertices(self, s_info, source_vertex, target_vertex):
         return connected
 
     def get_max_n_incoming_slices(self, source_vertex, target_vertex):
-        pre_slices = list(source_vertex.splitter.get_out_going_slices())
+        pre_vertices = numpy.array(
+            source_vertex.splitter.get_out_going_vertices(SPIKE_PARTITION_ID))
+        pre_slices = [m_vertex.vertex_slice for m_vertex in pre_vertices]
         pre_slices_x = [vtx_slice.get_slice(0) for vtx_slice in pre_slices]
         pre_slices_y = [vtx_slice.get_slice(1) for vtx_slice in pre_slices]
-        pre_ranges = [[[px.start, py.start], [px.stop - 1, py.stop - 1]]
+        pre_ranges = [[[py.start, px.start], [py.stop - 1, px.stop - 1]]
                       for px, py in zip(pre_slices_x, pre_slices_y)]
-        pres_as_posts = self.__pre_as_post(pre_ranges)
-        hlf_k_w, hlf_k_h = numpy.array(self.__kernel_weights.shape) // 2
+        pre_vertex_in_post_layer, start_i = self.__pre_as_post(pre_ranges)
+
+        pre_vertex_in_post_layer_upper_left = pre_vertex_in_post_layer[:,0]
+        pre_vertex_in_post_layer_lower_right = pre_vertex_in_post_layer[:,1]
+
+        kernel_shape = numpy.array(self.__kernel_shape)
+
+        j = (kernel_shape - 1 - start_i) // self.__strides
+        j_upper_left = j[:,0]
+
+        pre_vertex_max_reach_in_post_layer_upper_left = pre_vertex_in_post_layer_upper_left - j_upper_left
+        pre_vertex_max_reach_in_post_layer_lower_right = pre_vertex_in_post_layer_lower_right
 
         max_connected = 0
         for post_slice in target_vertex.splitter.get_in_coming_slices():
@@ -372,39 +384,6 @@ def get_max_n_incoming_slices(self, source_vertex, target_vertex):
 
         return max_connected
 
-    def get_max_n_incoming_slices(self, source_vertex, target_vertex):
-        pre_slices = list(source_vertex.splitter.get_out_going_slices())
-        pre_slices_x = [vtx_slice.get_slice(0) for vtx_slice in pre_slices]
-        pre_slices_y = [vtx_slice.get_slice(1) for vtx_slice in pre_slices]
-        pre_ranges = [[[px.start, py.start], [px.stop - 1, py.stop - 1]]
-                      for px, py in zip(pre_slices_x, pre_slices_y)]
-        pres_as_posts = self.__pre_as_post(pre_ranges)
-        hlf_k_w, hlf_k_h = numpy.array(self.__kernel_weights.shape) // 2
-
-        max_connected = 0
-        for post_slice in target_vertex.splitter.get_in_coming_slices():
-            post_slice_x = post_slice.get_slice(0)
-            post_slice_y = post_slice.get_slice(1)
-
-            # Get ranges allowed in post
-            min_x = post_slice_x.start - hlf_k_w
-            max_x = (post_slice_x.stop + hlf_k_w) - 1
-            min_y = post_slice_y.start - hlf_k_h
-            max_y = (post_slice_y.stop + hlf_k_h) - 1
-
-            # Test that the start coords are in range i.e. less than max
-            start_in_range = numpy.logical_not(
-                numpy.any(pres_as_posts[:, 0] > [max_x, max_y], axis=1))
-            # Test that the end coords are in range i.e. more than min
-            end_in_range = numpy.logical_not(
-                numpy.any(pres_as_posts[:, 1] < [min_x, min_y], axis=1))
-            # When both things are true, we have a vertex in range
-            pre_in_range = numpy.logical_and(start_in_range, end_in_range)
-            n_connected = pre_in_range.sum()
-            max_connected = max(max_connected, n_connected)
-
-        return max_connected
-
     def __pre_as_post(self, pre_coords):
         """ Write pre coords as post coords.
 
@@ -465,8 +444,6 @@ def get_local_only_data(
             values.extend([col_mask, 0, row_mask, n_bits_col])
 
         # Do a new list for remaining connector details as uint16s
-
-        # Write synapse information
         pos_synapse_type = app_edge.post_vertex.get_synapse_id_by_target(
             self.__positive_receptor_type)
         neg_synapse_type = app_edge.post_vertex.get_synapse_id_by_target(
@@ -476,15 +453,10 @@ def get_local_only_data(
             kernel_shape[0], kernel_shape[1],
             self.__padding_shape[0], self.__padding_shape[1],
             self.__recip(self.__strides[0]), self.__recip(self.__strides[1]),
-            self.__strides[0], self.__strides[1]
+            self.__strides[0], self.__strides[1],
             self.__recip(ps_y), self.__recip(ps_x),
             pos_synapse_type, neg_synapse_type], dtype="uint16")
 
-        data = [numpy.array(values, dtype="uint32"),
-                short_values.view("uint32"),
-                numpy.array([weight_index], dtype="uint32")]
-        return data
-
         # Write delay
         max_delay = app_edge.post_vertex.synapse_dynamics.delay * \
                             SpynnakerDataView.get_simulation_time_step_per_ms()
@@ -493,14 +465,19 @@ def get_local_only_data(
                           app_edge.post_vertex.splitter.max_support_delay())
 
         if self.__num_multisynaptic_connections == 1:
-            spec.write_value(max_delay)
+            delay_view = numpy.array([max_delay], dtype="uint32")
         else:
-            spec.write_value(max_delay, data_type=DataType.UINT16)
             delay_range = max_delay - self.__multisynaptic_delay_min
             delay_step = delay_range / (self.__num_multisynaptic_connections - 1)
-            spec.write_value(delay_step, data_type=DataType.UINT16)
+            delay_view = numpy.array([max_delay, delay_step], dtype="uint16").view("uint32")
 
-        spec.write_value(self.__num_multisynaptic_connections, data_type=DataType.UINT32)
+        # Compile all values
+        data = [numpy.array(values, dtype="uint32"),
+                short_values.view("uint32"),
+                delay_view,
+                numpy.array([self.__num_multisynaptic_connections], dtype="uint32"),
+                numpy.array([weight_index], dtype="uint32")]
+        return data
 
     def get_encoded_kernel_weights(self, app_edge, weight_scales):
         # Encode weights with weight scaling

From b6a447e13a8507105edb9fef0989c0e008b5f9ac Mon Sep 17 00:00:00 2001
From: Andrew Rowley <Andrew.Rowley@manchester.ac.uk>
Date: Tue, 14 Mar 2023 10:20:47 +0000
Subject: [PATCH 12/15] Fix counting

---
 .../neural_projections/connectors/convolution_connector.py  | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index 6e48cffc5f..ed5cf3f0f2 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -360,9 +360,7 @@ def get_max_n_incoming_slices(self, source_vertex, target_vertex):
             pre_vertex_in_post_layer_lower_right)
 
         max_connected = 0
-        for post in target_vertex.splitter.get_in_coming_vertices(
-                SPIKE_PARTITION_ID):
-            post_slice = post.vertex_slice
+        for post_slice in target_vertex.splitter.get_in_coming_slices():
             post_slice_x = post_slice.get_slice(0)
             post_slice_y = post_slice.get_slice(1)
 
@@ -383,7 +381,7 @@ def get_max_n_incoming_slices(self, source_vertex, target_vertex):
             # When both things are true, we have a vertex in range
             pre_in_range = pre_vertices[
                 numpy.logical_and(start_in_range, end_in_range)]
-            n_connected = pre_in_range.sum()
+            n_connected = len(pre_in_range)
             max_connected = max(max_connected, n_connected)
 
         return max_connected

From 8022750dd68b152dfdeaebbf18a9741ed2e409e8 Mon Sep 17 00:00:00 2001
From: Andrew Rowley <Andrew.Rowley@manchester.ac.uk>
Date: Tue, 14 Mar 2023 11:11:12 +0000
Subject: [PATCH 13/15] Line too long

---
 neural_modelling/src/neuron/local_only/local_only_conv_impl.c | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
index 184c597771..5488b10dd8 100644
--- a/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
+++ b/neural_modelling/src/neuron/local_only/local_only_conv_impl.c
@@ -163,7 +163,8 @@ static inline int16_t recip_multiply(int16_t integer, int16_t recip) {
 }
 
 //! \brief Do a mapping from pre to post 2D spaces
-static inline lc_coord_t map_pre_to_post(connector *connector, lc_coord_t pre, lc_coord_t *start_i) {
+static inline lc_coord_t map_pre_to_post(connector *connector, lc_coord_t pre,
+		lc_coord_t *start_i) {
     pre.col = recip_multiply(pre.col, connector->recip_pool_strides.col);
     pre.row = recip_multiply(pre.row, connector->recip_pool_strides.row);
     pre.col += connector->padding.width;

From 6ff18c829382421a665a5ab0bce61421e4950bbd Mon Sep 17 00:00:00 2001
From: Andrew Rowley <Andrew.Rowley@manchester.ac.uk>
Date: Tue, 14 Mar 2023 11:11:55 +0000
Subject: [PATCH 14/15] Flake8

---
 .../neural_projections/connectors/convolution_connector.py     | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
index c29251d33b..f1aff1bfa4 100644
--- a/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
+++ b/spynnaker/pyNN/models/neural_projections/connectors/convolution_connector.py
@@ -486,7 +486,8 @@ def get_local_only_data(
         data = [numpy.array(values, dtype="uint32"),
                 short_values.view("uint32"),
                 delay_view,
-                numpy.array([self.__num_multisynaptic_connections], dtype="uint32"),
+                numpy.array([self.__num_multisynaptic_connections],
+                            dtype="uint32"),
                 numpy.array([weight_index], dtype="uint32")]
         return data
 

From 0b216a2d0019cfe7a4b83ad22a90d742018ee770 Mon Sep 17 00:00:00 2001
From: Andrew Rowley <Andrew.Rowley@manchester.ac.uk>
Date: Tue, 14 Mar 2023 11:19:13 +0000
Subject: [PATCH 15/15] Rats

---
 .../IF_curr_delta_conv/Makefile               | 21 +++++++++----------
 1 file changed, 10 insertions(+), 11 deletions(-)

diff --git a/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile b/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
index 5026a0cca8..c87b8ef7e8 100644
--- a/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
+++ b/neural_modelling/makefiles/local_only_combined/IF_curr_delta_conv/Makefile
@@ -1,17 +1,16 @@
-# Copyright (c) 2021-2022 The University of Manchester
+# Copyright (c) 2023 The University of Manchester
 #
-# This program is free software: you can redistribute it and/or modify
-# it under the terms of the GNU General Public License as published by
-# the Free Software Foundation, either version 3 of the License, or
-# (at your option) any later version.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
 #
-# This program is distributed in the hope that it will be useful,
-# but WITHOUT ANY WARRANTY; without even the implied warranty of
-# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-# GNU General Public License for more details.
+#     https://www.apache.org/licenses/LICENSE-2.0
 #
-# You should have received a copy of the GNU General Public License
-# along with this program.  If not, see <http://www.gnu.org/licenses/>.
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 
 APP = $(notdir $(CURDIR))