Although deep learning has recently increased in popularity, it suffers from various problems
including high computational complexity, energy greedy computation, and lack of scalability …

Abstract We overview Reservoir Computing (RC) with physical systems from an
Unconventional Computing (UC) perspective. We discuss challenges present in both fields …

The rapidly growing computational demands of deep neural networks require novel
hardware designs. Recently, tuneable nanoelectronic devices were developed based on …

In this work, we report the preliminary analysis of the electrophysiological behavior of in vitro
neuronal networks to identify when the networks are in a critical state based on the size …

The Reservoir Computing (RC) framework is said to have the potential to transfer onto any
input-driven dynamical system, provided two properties are present:(i) a fading memory, and …

Modern computing relies on future and emergent technologies which have been conceived
via interaction between computer science, engineering, chemistry, physics and biology. This …

We discuss some claims that certain UCOMP devices can perform hypercomputation
(compute Turing-uncomputable functions) or perform super-Turing computation (solve NP …

We construct logical gates via topology optimisation (aimed to solve a station problem of
heat conduction) of a conductive material layout. Values of logical variables are represented …

Logical gates are constructed based on a method of numerical modeling of natural erosion
of sandstone. Values of logical variables are determined by the presence of sandstone. The …

We present ideas aimed at bringing revolutionary changes on architectures and buildings of
tomorrow by radically advancing the technology for the building material concrete and …