cleaned up dense-syn

Author: ago109

ngclearn/components/synapses/denseSynapse.py

@@ -5,27 +5,6 @@
 from ngclearn.utils.weight_distribution import initialize_params
 from ngcsimlib.logger import info
 
-@jit
-def _compute_layer(inp, weight, biases=0., Rscale=1.):
-    """
-    Applies the transformation/projection induced by the synaptic efficacie
-    associated with this synaptic cable
-
-    Args:
-        inp: signal input to run through this synaptic cable
-
-        weight: this cable's synaptic value matrix
-
-        biases: this cable's bias value vector (default: 0.)
-
-        Rscale: scale factor to apply to synapses before transform applied
-            to input values (default: 1.)
-
-    Returns:
-        a projection/transformation of input "inp"
-    """
-    return jnp.matmul(inp, weight * Rscale) + biases
-
 class DenseSynapse(JaxComponent): ## base dense synaptic cable
     """
     A dense synaptic cable; no form of synaptic evolution/adaptation
@@ -51,7 +30,7 @@ class DenseSynapse(JaxComponent): ## base dense synaptic cable
             (Default: None, which turns off/disables biases)
 
         resist_scale: a fixed (resistance) scaling factor to apply to synaptic
-            transform (Default: 1.), i.e., yields: out = ((W * Rscale) * in)
+            transform (Default: 1.), i.e., yields: out = ((W * in) * resist_scale) + bias
 
         p_conn: probability of a connection existing (default: 1.); setting
             this to < 1 and > 0. will result in a sparser synaptic structure
@@ -98,7 +77,7 @@ def __init__(self, name, shape, weight_init=None, bias_init=None,
 
     @staticmethod
     def _advance_state(Rscale, inputs, weights, biases):
-        outputs = _compute_layer(inputs, weights, biases, Rscale)
+        outputs = (jnp.matmul(inputs, weights) * Rscale) + biases
         return outputs
 
     @resolver(_advance_state)
@@ -155,12 +134,12 @@ def help(cls): ## component help function
             "batch_size": "Batch size dimension of this component",
             "weight_init": "Initialization conditions for synaptic weight (W) values",
             "bias_init": "Initialization conditions for bias/base-rate (b) values",
-            "resist_scale": "Resistance level scaling factor (applied to output of transformation)",
+            "resist_scale": "Resistance level scaling factor (Rscale); applied to output of transformation",
             "p_conn": "Probability of a connection existing (otherwise, it is masked to zero)"
         }
         info = {cls.__name__: properties,
                 "compartments": compartment_props,
-                "dynamics": "outputs = [(W * Rscale) * inputs] + b",
+                "dynamics": "outputs = [W * inputs] * Rscale + b",
                 "hyperparameters": hyperparams}
         return info