Forward mean-field learning rule options by name

CHANGELOG.md

@@ -7,6 +7,8 @@ see the [GitHub releases / tags](https://github.com/BindsNET/bindsnet/releases).
 ## [Unreleased]
 
 ### Fixed
+- Forward mean-field `weight_decay` to the learning rule instead of treating it as
+  the update reduction; explicit `reduction` arguments are now accepted as well.
 - `--seed` now makes GPU runs repeatable in `eth_mnist.py`, `batch_eth_mnist.py`,
   `supervised_mnist.py`, `conv_mnist.py`, `conv1d_MNIST.py`, `conv3d_MNIST.py`,
   `SOM_LM-SNNs.py` and `breakout/random_network_baseline.py`: they call

bindsnet/network/topology.py

@@ -2089,7 +2089,7 @@ def __init__(
             tensor of same size as w
         :param float norm: Total weight per target neuron normalization constant.
         """
-        super().__init__(source, target, nu, weight_decay, **kwargs)
+        super().__init__(source, target, nu=nu, weight_decay=weight_decay, **kwargs)
 
         w = kwargs.get("w", None)
         if w is None:

test/network/test_meanfield_update_options.py

@@ -0,0 +1,44 @@
+import pytest
+import torch
+
+from bindsnet.network import Network
+from bindsnet.network.nodes import Input, LIFNodes
+from bindsnet.network.topology import MeanFieldConnection
+
+
+@pytest.mark.parametrize("weight_decay", [0.0, 0.1, 1.0])
+@pytest.mark.parametrize("shared", [False, True])
+def test_meanfield_weight_decay_during_network_run(weight_decay, shared):
+    network = Network()
+    source, target = Input(n=2), LIFNodes(n=3)
+    network.add_layer(source, "X")
+    network.add_layer(target, "Y")
+    initial = torch.tensor(0.5) if shared else torch.tensor([0.25, 0.5, 0.75])
+    connection = MeanFieldConnection(
+        source, target, w=initial.clone(), weight_decay=weight_decay
+    )
+    network.add_connection(connection, "X", "Y")
+    parameter = connection.w
+    # No STDP rule or normalization: the common learning-rule decay acts alone.
+    for step in range(1, 4):
+        network.run({"X": torch.zeros(1, 1, 2)}, time=1)
+        expected = initial * (1 - weight_decay) ** step
+        torch.testing.assert_close(connection.w, expected)
+        assert connection.w is parameter
+
+
+@pytest.mark.parametrize(
+    "reduction,expected",
+    [(torch.sum, [5.0, 7.0, 9.0]), (torch.mean, [2.5, 3.5, 4.5])],
+)
+def test_meanfield_accepts_update_reduction(reduction, expected):
+    connection = MeanFieldConnection(
+        Input(n=2),
+        LIFNodes(n=3),
+        w=torch.tensor(0.5),
+        weight_decay=0.1,
+        reduction=reduction,
+    )
+    updates = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
+    actual = connection.update_rule.reduction(updates, dim=0)
+    torch.testing.assert_close(actual, torch.tensor(expected))