Scalable Reconfigurable Memristive Synaptic Structures as a Basis for the Plasticity Mechanisms in Developing and Self-organizing Networks of Artificial Pulsed Neurons

Of particular importance is the property of plasticity for networks of large dimension and scale, since the large dimension of the network is a necessary condition for the processes of self - organization and for "intellectual" data processing. However, the hardware implementation of this property is rather complicated. Using functional micro and nano-elements with memristive properties is a justified way of achieving the required high degree of reconfigurable synaptic structures integration and scale. Analysis of the chemical and biological mechanisms of STP and LTPl confirms the hypothesis of their relative independence and parallel learning of biological neural systems at different time scales. In the paper, the expediency of spatial and temporal scaling of artificial synaptic structures based on memristive elements, functioning on different physical principles, is proved.