minor changes in doc

cnsproject/encoding/encoders.py

@@ -28,9 +28,9 @@ class AbstractEncoder(ABC):
     time : int
         Length of encoded tensor.
     dt : float, Optional
-        Simulation timestep. The default is 1.0.
+        Simulation time step. The default is 1.0.
     device : str, Optional
-        The device to do the comutations. The default is "cpu".
+        The device to do the computations. The default is "cpu".
 
     """
 

cnsproject/network/connections.py

@@ -8,7 +8,6 @@
 import torch
 
 from .neural_populations import NeuralPopulation
-# from ..learning.learning_rules import LearningRule, NoOp
 
 
 class AbstractConnection(ABC, torch.nn.Module):
@@ -121,7 +120,7 @@ def compute(self, s: torch.Tensor) -> None:
     @abstractmethod
     def update(self, **kwargs) -> None:
         """
-        Compute connection's learning rule.
+        Compute connection's learning rule and weight update.
 
         Keyword Arguments
         -----------------

cnsproject/network/network.py

@@ -79,6 +79,9 @@ def __init__(
 
         self.train(learning)
 
+        # Make sure that arguments of your reward and decision classes do not
+        # share same names. Their arguments are passed to the network as its
+        # keyword arguments.
         self.reward = reward(**kwargs)
         self.decision = decision(**kwargs)
 
@@ -201,8 +204,8 @@ def run(
             Mapping of connection names to boolean masks of the weights to clamp
             to zero.
 
-        Note: you can pass the reward and decision arguments as keyword\
-        argumeents to this function.
+        **Note:** you can pass the reward and decision methods' arguments as keyword\
+        arguments to this function.
 
         Returns
         -------