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Building Blocks of Neural Nets


The following paragraph describes a generic model for those neural nets that can be generated by the SNNS simulator. The basic principles and the terminology used in dealing with the graphical interface are also briefly introduced. A more general and more detailed introduction to connectionism can, e.g., be found in [RM86]. For readers fluent in German, the most comprehensive and up to date book on neural network learning algorithms, simulation systems and neural hardware is probably [Zel94]

      A network consists of unitsgif and directed, weighted links (connections) between them. In analogy to activation passing in biological neurons, each unit receives a net input that is computed from the weighted outputs of prior units with connections leading to this unit. Picture gif shows a small network.

Figure: A small network with three layers of units

The actual information processing within the units is modeled in the     SNNS simulator with the activation function and the output function. The activation function first computes the net input of the unit from the weighted output values of prior units. It then computes the new activation from this net input (and possibly its previous activation). The output function takes this result to generate the output of the unit.gif These functions can be arbitrary C functions linked to the simulator kernel and may be different for each unit.

Our simulator uses a discrete clock. Time is not modeled explicitly (i.e. there is no propagation delay or explicit modeling of activation functions varying over time). Rather, the net executes in update steps, where is the activation of a unit one step after .

The SNNS simulator, just like the Rochester Connectionist Simulator (RCS, [God87]), offers the use of sites as   additional network element. Sites are a simple model of the dendrites of a neuron which allow a grouping and different treatment of the input signals of a cell. Each site can have a different site function. This selective treatment of incoming information allows more powerful connectionist models. Figure gif shows one unit with sites and one without.

Figure: One unit with sites and one without

In the following all the various network elements are described in detail.

next up previous contents index
Next: Units Up: Neural Network Terminology Previous: Neural Network Terminology
Tue Nov 28 10:30:44 MET 1995