In recent years, the technological revolution has changed the way we see reality and interact with it. Inevitably, education and didactic planning have also had to deal with new technologies. Indeed, the presence of digital tools has radically changed people’s lives since childhood. Many educational realities have exploited this digital transformation to speed up and specialize learning, customizing study plans and type of software according to age groups. The activity of the Digital Education Lab is part of this context. It is a digital education school which uses the game software Minecraft Education Edition to teach its students the fundamental principles of computer science, geometry and mathematics. This article discusses learning key science concepts through game learning. The analysis carried out allows to see that students are facilitated in learning complex scientific concepts when these are shown through the game and can therefore be “experienced”. The learning of 186 students aged between 8 and 10, who are generally at the first approaches to the digital world, has been evaluated. To evaluate the acquisition of knowledge through these innovative methods, at the end of the didactic course we have administered anonymous tests through the Google classroom platform. The results show that learning through a game-software facilitates the learning of basic scientific information as well as fostering the capacity for interconnection and transversality.

The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural Heritage area, with the aim of predicting short-term microclimatic values based on data collected at Rosenborg Castle (Copenhagen), housing the Royal Danish Collection. Specifically, this study applied the NAR (Nonlinear Autoregressive) and NARX (Nonlinear Autoregressive with Exogenous) models to the Rosenborg microclimate time series. Even if the two models were applied to small datasets, they have shown a good adaptive capacity predicting short-time future values. This work explores the use of AI in very short forecasting of microclimate variables in museums as a potential tool for decision-support systems to limit the climate-induced damages of artworks within the scope of their preventive conservation. The proposed model could be a useful support tool for the management of the museums.

Neuromorphic models are proving capable of performing complex machine learning tasks, overcoming the structural limitations imposed by software algorithms and electronic architectures. Recently, both supervised and unsupervised learnings were obtained in photonic neurons by means of spatial-soliton-waveguide X-junctions. This paper investigates the behavior of networks based on these solitonic neurons, which are capable of performing complex tasks such as bit-to-bit information memorization and recognition. By exploiting photorefractive nonlinearity as if it were a biological neuroplasticity, the network modifies and adapts to the incoming signals, memorizing and recognizing them (photorefractive plasticity). The information processing and storage result in a plastic modification of the network interconnections. Theoretical description and numerical simulation of solitonic networks are reported and applied to the processing of 4-bit information.

For an optical technology to be feasible as a substitution of electronics one for neuromorphic applications, it is required the waveguides in which are capable of confining and directing light signals in much smaller dimensions than the operating wavelength of the light, i.e., subwavelength optical components must be realized. Hybrid nature of surface plasmon polariton addresses the problem with the diffraction limit of regular photonic components. Here, the authors present a two-dimensional numerical simulation of a passive photonic element based on a saturable absorber material as a hardware base analogy to the biological activation function existed at the cell body of neurons. They demonstrate that at telecom wavelengths, a highly confined SPP mode can be modulated in a nonlinear fashion by considering the carrier dynamics of graphene/PMMA polymeric composite as a two-level system. The hardware base sigmoid type nonlinear activation function derived from this study further characterized; and the parameters which appeared to be effective on the performance of the structure, such as saturation intensity, modulation depth, and thickness of the synaptic part as a Fabry–Pérot structure, have been studied.

In this Letter, the first implementation, to our knowledge, of X-junctions between photorefractive soliton waveguides in lithium niobate-on-insulator (LNOI) films is reported. The experiments were performed on 8 µm thick films of congruent undoped LiNbO₃. Compared with bulk crystals, the use of films reduces the soliton formation time, allows more control over the interaction between the injected soliton beams, and opens a route to integration with silicon optoelectronics functions. The created X-junction structures show effective supervised learning, directing the signals propagated inside the soliton waveguides into the output channels highlighted by the control assigned by the external supervisor. Thus, the obtained X-junctions have behaviors analogous to biological neurons.

The software implementations of neuronal systems have shown great effectiveness, even if the natural hardware separation between the processing and memory areas in computers slows down the analysis capacity. To overcome these limitations, new hardware configurations are moving towards neuromorphic models, capable of unifying the processing/memory dichotomy. Recently, integrated photonic X-junctions formed by waveguides written by spatial solitons have shown the ability to perform supervised learning. The solitonic technology, compared to the traditional one, offers the advantage of realizing plastic circuitry, a typical characteristic of biological neural networks. This work extensively studies both supervised and unsupervised learning of photonic soliton X-junctions. By exploiting the plasticity of the nonlinear refractive index at the base of the soliton formation, X-junctions can readdress their behaviours forwarding data to different outputs. In this article, we will extend the state-of-the-art: starting from supervised learning, for which all possible cases are now investigated, a material sensitive to the transported signals will be introduced to allow the junction to carry out unsupervised learning. In this way, the junction autonomously recognises the transported signals without the external intervention of the operator. Learning and memory now physically coincide in fact, learning means that the junction slowly switches based on the information sent; any further unknown information sent will find the junction in the modified state which corresponds to the learned information and will be recognised as well (reasoning based on comparison with stored information).