Realization of bio-inspired computational systems demands elements that willbe used for both memorizing and processing of the information, as occurs inthe brain. In this chapter we will consider organic memristive devices– elements that were designed and constructed for mimicking the mostimportant property of synapses, responsible for so-called Hebbian learning.The organization of the device and its important properties are considered.Its utilization in generators of auto-oscillation generators and in logicelements with memory is also considered. Finally, stochastic networks oforganic memristive devices have demonstrated several similarities withlearning of brains of animals and humans.

Introduction

A significant difference in the architecture of computers and the brain isthat in the computer the memory and the processor are different devices withno influence on each other. The information in this case plays a passiverole – it can be recorded, accessed, canceled, but it does not varyproperties of the system. In the brain, in contrast, the same elements areused for both memorizing and processing of the information. Thisarchitecture allows learning of the system at a hardware level. Informationbegins to play an active role: it is not only recorded, but it variesconnections within the processor, preparing it for more effective resolvingof similar tasks in the future. The other essential difference between thebrain and computer is the fact that in the brain we have highly parallelinformation processing. This is why it is much more effective for sometasks, such as, for example, the recognition and classification ofobjects.