Chapter 2. Prehension

386 A pp e n dic e s
thresholding
function
weights
OUTPUT
INPUT
activation level
of neuron i
Figure C.2 Simple pattern of connectivity between one input layer
and one output layer.
continuous (taking on real values that can be bounded between two
values or unbounded). A popular model, called a linear threshold
unit, uses an activation function that maps onto the binary set { 0,l } .
As seen in Figure C. 1, a McCulloch-Pitts neuron is a linear threshold
unit. A quasi-linear (or semi-linear) activation function2 is a non-
decreasing and differentiable function that produces values within an
interval.
To model a neuron’s activation after a certain threshold is reached,
an output functionf is used, producing the output (on the axon) from
neuron i as follows:
2This is also called a squashing function.