Chapter 2. Prehension

Appendix C - Computational Neural Modelling 387
OUTPUTLAYER
HIDDEN LAYER
INPUT LAYER
Figure C.3 Simple pattern of connectivity in multi-layered
network, containing one input layer, one hidden layer, and one
output layer.
The function f can be the identity function, or more usually, it is a
threshold function that ensures that a neuron only outputs a signal
when its activation exceeds a certain value, as typically seen in real
neurons.
In parallel distributed processing models, neurons are connected in
some pattern of connectivity, or network topology. A feedforward
network is a set of neurons connected in such a way that there are no
cyclic paths. This type of network has no dynamics; that is, after an
input pattern is applied to the network, within a finite number of
‘steps’ (synaptic delays) equal to the length of the longest path from
input to output, the network will settle into a static state of neural
activation. Such a network is seen in Figure C.3. This feedforward
network has three layers. Certain neurons are defined to be ‘input
units’ and certain ones are ‘output units’. Intermediate units, which
are not input or output are called ‘hidden units’. In 1969, Marvin
Minsky and Seymour Papert demonstrated that adding a layer of
neurons hidden between the input layer and output layer can allow the
inputs to be recoded into a sufficiently similar structure for correct