In neural networks
, information is represented conceptually in the strengths of the connections between neurons
, or between outside signal sources and neurons. Connection strengths in artificial
neural networks are often encoded in the values of connection weights
at the inputs of neurons. Input signals are modulated by the connection weight values
at the inputs, which, in turn, determine how much of the input signal is conveyed by the synapse
. . . . . . .
Connection-Strength vs. Weight Value
In conventional neural network models, Weight values
are numbers representing a combination of both the connection-strength, and the type (inhibitory
) of a connection. The connection-type (inhibitory
) can be accommodated in a variety of ways, but usually it is represented by the sign of the weight-value. If the weight-value is negative, the weight represents an inhibitory
connection and if the weight-value is positive it represents an excitatory
There is some confusion associated with these math-centric models that use signed, floating-point numbers to represent type:
- inhibitory connection-strengths are enhanced (made stronger) by being decreased (by being made more negative). Conversely, they are reduced by being increased (by being made less negative).
- Excitatory connection-strengths, on the other hand, work as you would expect. That is, they are enhanced by being increased (made more positive), and reduced by being decreased (made less positive).
Stated more generally, a connection-strength's relationship to a conventional neural network's signed weight-value is that the connection-strength is reduced when the value moves closer to zero, and the connection-strength is increased when the weight-value is moved farther away from zero.
Thus, when a weight value is negative, increasing
its value (making it more positive) reduces
the strength of the connection between the pre- and post-synaptic
One more way to say it:
of the connection is represented in the magnitude of the absolute
value of the weight, regardless of its sign (negative or positive), though, in conventional neural networks, when negative, it is increased
thereby making its strength lower. In Netlab, on the other hand, the connection-strength —not the weight's value
— is adjusted.
More about weight-values vs connection-strengths, and how Netlab™ accommodates the existing convention (i.e., adjusting value not strength), can be found in a section titled: "Netlab's Compatibility Mode"
, which is in this glossary's entry for weights
. . . . . . .
There are many ways to represent connection-strengths in neural networks. Any mechanism that can be altered in a way that can mimic the effect of changing strengths of a connection between two entities can be used to represent connection-strengths. A variable resistor is probably the most obvious example of this. These days, in fact, a lot of excitement is being generated around the possibility of using memristors
as weights in neural network circuits.
That said, it is important to understand that any mechanism that serves to modulate the strength of interactions between two things can be thought of, in a general sense, as a provider of connection-strengths. To give an extreme example, a gate between two fenced areas can be left half open, thereby modulating the connection strength between the two areas by half. This reduces the flow of live-stock between them.