Key Word Search
by term...

by definition...

for letter: "W"
Results

Wallace, Alfred Russel
 
Weight
 
Weight Jiggling
 
Weight Jogging
 
Weight Value
 
Weight-Set
 
Weight-to-Weight
 
Weight-to-Weight Learning
 
Werbos, Paul J.
 
Werner Heisenberg
 
William Jevons
 
William of Ockham
 



Unless otherwise indicated, all glossary content is:
(C) Copyright 2008-2014
Dominic John Repici
~ ALL RIGHTS RESERVED ~
No part of this Content may be copied without the express written permision of Dominic John Repici.













































 



 
Weight

 
Weight Value - Synapse Weight - A value used to modulate, or gate a single input signal (called an axon level) stimulating a neuron during signal propagation phase. Expressed in floating-point arithmetic, the value of the input signal is simply multiplied by the weight value to produce a result. The result of the multiplication is then summed by the neuron. During training or learning phase, weight values are changed in order to bring each neuron's output response in line with a desired response for a given set of inputs.

In conventional ANN models, a synapse's weight is a single, floating point, number that represents the connection-strength, and the type (inhibitory or excitatory) of a given connection. A negative value represents an inhibitory connection, and a positive value represents an excitatory connection. The absolute value of the weight represents the strength of the connection.



. . . . . . .
Netlab's Compatibility Mode


ANN models that use floating point signed-value weights in the conventional fashion are math-centric. That is, they typically are concerned only with the signed numeric weight-value, rather than with the connection-strength represented by its absolute value. In this case, for example, increasing the weight value will make it more positive, regardless of whether it is representing an excitatory or inhibitory connection.

Netlab's default behavior is to operate directly on connection-strength representations, regardless of how they are implemented internally. Netlab facilitates the conventional practice, however, by allowing it to be specified at the weight-layer learning method.

The table below shows how Netlab facilitates compatibility with existing practices. The table documents how the translation is carried out between the traditional math-centric convention, and Netlab's connection-strength-centric convention.


Connection-Type->
v--Operation       
Excitatory Inhibitory
Increase Increase
Connection Strength
Decrease
Connection Strength
Decrease Decrease
Connection Strength
Increase
Connection Strength
Translations performed when conventional practice is specified for a connection.


Also: Connection-Strength     Gate    

 
 




Web-based glossary software: (c) Creativyst, 2001-2014