Bio-Inspired Evolutionary Model of Spiking Neural Networks in Ionic Liquid Space.

One of the biggest struggles while working with artificial neural networks is being able to come up with models which closely match biological observations. Biological neural networks seem to capable of creating and pruning dendritic spines, leading to synapses being changed, which results in higher learning capability. The latter forms the basis of the present study in which a new ionic model for reservoir-like networks, consisting of spiking neurons, is introduced.

A facile approach for reducing the working voltage of Au/TiO/Au nanostructured memristors by enhancing the local electric field.

Memristor devices have attracted tremendous interest due to different applications ranging from nonvolatile data storage to neuromorphic computing units. Exploring the role of surface roughness of the bottom electrode (BE)/active layer interface provides useful guidelines for the optimization of the memristor switching performance. This study focuses on the effect of surface roughness of the BE electrode on the switching characteristics of Au/TiO/Au three-layer memristor devices.

Memristive Neuro-Fuzzy System.

In this paper, a novel neuro-fuzzy computing system is proposed where its learning is based on the creation of fuzzy relations by using a new implication method without utilizing any exact mathematical techniques. Then, a simple memristor crossbar-based analog circuit is designed to implement this neuro-fuzzy system which offers very interesting properties. In addition to high connectivity between neurons and being fault tolerant, all synaptic weights in our proposed method are always non-negative, and there is no need to adjust them precisely.