Stochastic hybrid 3D matrix: learning and adaptation of electrical properties

Abstract

Memristive devices are electronic elements with memory properties. This feature marks them out as possible candidates for mimicking synapse properties. Development of systems capable of performing simple brain operations demands a high level of integration of elements and their 3D organization into networks. Here, we demonstrate the formation and electrical properties of stochastic polymeric matrices. Several features of the network revealed similarities with those of the nervous system. In particular, applying different training protocols, we obtained two kinds of learning comparable to the “baby” and “adult” learning in animals and humans. To mimic “adult” learning, multi-task training was applied simultaneously resulting in the formation of few parallel pathways for a given task, modifiable by successive training. To mimic “baby” learning (imprinting), single task training was applied at one time, resulting in the formation of multiple parallel signal pathways, scarcely influenced by successive training

Skeleton-supported stochastic networks of organic memristive devices: Adaptations and learning

“…If we make a comparison with systems, based on phase-separating self-assembling structure, 15 the ratio here is comparable with so called "adult" learning case, and about one order of magnitude less than in the case of so called "baby" learning (imprinting). However, considering that the presented approach is very simple, we can suggest that the further works in this direction, connected to the better choice of the skeleton template, optimization of the deposition techniques and variation of the device architecture, will allow to fabricate networks with high performance of characteristics.…”

“…Similarly to the previously reported systems, 15,16 three different signals were applied to the network: two types of training stimuli and testing signal. Testing signal value was chosen in such a way that it does not varies significantly the conductivity state of the system (+0.3 V).…”

“…15 Both systems have demonstrated adaptation capabilities. However, in the case of the fiber structure the stability of the electrical properties was found to be very low.…”

“…13,14 In, 13 authors report synapse-like behavior, connected to the barrier formation between nanoparticles and organic material. The barrier formation approach was used also in the case of the stochastic polymeric networks, 15 but for other reasons -distribution of threshold elements in the network.…”

First steps towards the realization of a double layer perceptron based on organic memristive devices

“…An important advantage is also the possibility to realize the polymeric stochastic memristive systems in which communication between the computing elements (neurons) can be arranged in 3D. 14 Regarding neural networks, where effective learning requires a precise knowledge of the conductivity state of all elements and kinetics of its variation, polyaniline based system has another very important advantage. Conductivity of polyaniline, and, therefore memristive elements, is directly connected to its color.…”