These networks do indeed want to smooth their inputs which, due to limitations of sensors, comes in non-continuous bursts, say thirty times a second. To do that, they might accept these inputs, sum that data, and then produce an output by, for example, applying a hyperbolic tangent as a transfer function. In this manner, output values from the network are continuous and satisfy more real world interfaces. Other applications might simply sum and compare to a threshold, thereby producing one of two possible outputs, a zero or a one. Other functions scale the outputs to match the application, such as the values minus one and one.
Some functions even integrate the input data over time, creating time-dependent networks.
Those capabilities will be discussed later in this report. Figure 2.2.3 is a more detailed schematic of this still simplistic artificial neuron. In Figure 2.2.3, inputs enter into the processing element from the upper left. Then these modified inputs are fed into the summing function, which usually just sums these products. Yet, many different types of operations can be selected.