Foundation of Notation and Classification of Nonconventional Static and Dynamic Neural Units

The paper introduces basic types of nonconventional artificial neural units and focuses their notation and classification: namely; the notation and classification of dynamic higher-order nonlinear neural units, time-delay dynamic neural units, and time-delay higher-order nonlinear neural units is introduced. Brief introduction into the simplified parallel of higher-order nonlinear aggregating function of artificial nonconventional neural units and synaptic and somatic operation of biological neurons is made. Based on still simplified mathematical notation, it is proposed that nonlinear aggregating function of neural inputs should be understood as composition of synaptic as well as partial somatic neural operation also for static neural units. Thus it unravels novel, simplified, yet universal insight into understanding more computationally powerful neurons. The classification of nonconventional artificial neural units is founded first according to nonlinearity of aggregating function, second according to the dynamic order, third according to time-delay implementation within neural units.