Annotation: Equivalence models (EM) advantages of neural networks (NN) are shown in paper. ЕМs are based on vectormatrix procedures with basic operations of continuous neurologic: normalized vector operations “equivalence”, “nonequivalence”. The capacity of NN on the basis of ЕМ and of its modifications, including auto-and heteroassociative memories for 2D images, exceeds in several times quantity of neurons. Such neuroparadigms are very perspective for processing, recognition, storing large size and strongly correlated images. A biologically motivated concept and time-pulse encoding principles of continuous logic photocurrent mirrors and sample-storage devices with pulse-width photoconverters have allowed us to design generalized structures for realization of the family of normalized linear vector operations “equivalence”-“nonequivalence”. Simulation results show, that processing time in such circuits does not exceed units of micro seconds. Circuits are simple, have low supply voltage (1-3)V, low power consumption (milliwatts), low levels of input signals (microwatts), integrated construction, satisfy the problem of interconnections and cascading.