ABSTRACT
Networks with feedbacks show phenomena and processes not revealed by one-way networks. After stimulation, a network with feedback can generate thorough sequences of signals and phenomena as outputs (results of signal processing in n iteration) and return to the inputs of neurons to produce new signals, usually in n + 1 iteration. Specific phenomena and processes of recurrent networks arise from complicated signal circulations (e.g., vibrations varying between the rapid rise of alternate extremes), equally rapid suppressions, or chaotic roaming (that looks like undetermined progress).
