Merge pull request #218 from Hananel-Hazan/sharath

minor documentation fix

bindsnet/network/topology.py

@@ -28,7 +28,7 @@ def __init__(self, source: Nodes, target: Nodes, nu: Optional[Union[float, Seque
 
         Keyword arguments:
 
-        :param function update_rule: Modifies connection parameters according to some rule.
+        :param LearningRule update_rule: Modifies connection parameters according to some rule.
         :param float wmin: The minimum value on the connection weights.
         :param float wmax: The maximum value on the connection weights.
         :param float norm: Total weight per target neuron normalization.
@@ -130,7 +130,7 @@ def __init__(self, source: Nodes, target: Nodes, nu: Optional[Union[float, Seque
 
         Keyword arguments:
 
-        :param function update_rule: Modifies connection parameters according to some rule.
+        :param LearningRule update_rule: Modifies connection parameters according to some rule.
         :param torch.Tensor w: Strengths of synapses.
         :param torch.Tensor b: Target population bias.
         :param float wmin: Minimum allowed value on the connection weights.
@@ -246,7 +246,7 @@ def __init__(self, source: Nodes, target: Nodes, kernel_size: Union[int, Tuple[i
 
         Keyword arguments:
 
-        :param function update_rule: Modifies connection parameters according to some rule.
+        :param LearningRule update_rule: Modifies connection parameters according to some rule.
         :param torch.Tensor w: Strengths of synapses.
         :param torch.Tensor b: Target population bias.
         :param float wmin: Minimum allowed value on the connection weights.
@@ -430,7 +430,7 @@ def __init__(self, source: Nodes, target: Nodes, kernel_size: Union[int, Tuple[i
 
         Keyword arguments:
 
-        :param function update_rule: Modifies connection parameters according to some rule.
+        :param LearningRule update_rule: Modifies connection parameters according to some rule.
         :param torch.Tensor w: Strengths of synapses.
         :param torch.Tensor b: Target population bias.
         :param float wmin: Minimum allowed value on the connection weights.
@@ -562,7 +562,7 @@ def __init__(self, source: Nodes, target: Nodes, nu: Optional[Union[float, Seque
 
         Keyword arguments:
 
-        :param function update_rule: Modifies connection parameters according to some rule.
+        :param LearningRule update_rule: Modifies connection parameters according to some rule.
         :param torch.Tensor w: Strengths of synapses.
         :param float wmin: Minimum allowed value on the connection weights.
         :param float wmax: Maximum allowed value on the connection weights.
@@ -637,7 +637,7 @@ def __init__(self, source: Nodes, target: Nodes, nu: Optional[Union[float, Seque
 
         :param torch.Tensor w: Strengths of synapses.
         :param float sparsity: Fraction of sparse connections to use.
-        :param function update_rule: Modifies connection parameters according to some rule.
+        :param LearningRule update_rule: Modifies connection parameters according to some rule.
         :param float wmin: Minimum allowed value on the connection weights.
         :param float wmax: Maximum allowed value on the connection weights.
         :param float norm: Total weight per target neuron normalization constant.