ABSTRACT
Neural networks are used to solve many engineering, medical, and business problems. A biological neuron is a complicated structure, which receives trains of pulses on hundreds of excitatory and inhibitory inputs. In the biological neuron, information is sent in the form of frequency-modulated pulse trains. Because incoming pulses are summed with time, the neuron generates a pulse train with a higher frequency for higher positive excitation. Both types of networks work the same way and it is very easy to transform bipolar neural network into unipolar neural network and vice versa. The soft activation functions allow for the gradient-based training of multilayer networks. Soft activation functions make neural network transparent for training. Neural network users often face a dilemma if they have to use unipolar or bipolar neurons. Feedforward neural networks allow only unidirectional signal flow. An example of such neural network, separating patterns from the rectangular area.
