The biological efficacy of the transfer function

The most popular neural network strategy is back propagation. This strategy initiated general interest in neural networks among researchers. While back propagation can solve nonlinear problems, it is considered to be a poor example of neuron functioning. Recently, Gardner (1993) has made a strong case for a back propagating phenomenon in networks of living neurons. In this paper, we present a few simple computational examples that investigate another component of the typical back propagation network. The effects of varying transfer functions are illustrated along with the resulting variations in possible synaptic weights. Graphic presentations in 3-D space of the relationship between transfer functions and synaptic weights suggest neural analogies of cell-firing rate and network control.