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.

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.

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).

In recent years, technological development has focused on the construction of ever smaller devices, characterized by dimensions limited to the nanometer order and by a very low energy requirement to be able to function. This allows them to be integrated into chips, which are then able to perform many tasks from filtering to computation. Here, we present a magnetic switch capable of working with surface plasmon polaritons.

Stigmergy is a communication method based on changing the surrounding environment according to reference feedbacks. It is typical within animal colonies that are able to process even complex information by releasing signals into the environment, which are subsequently received and processed by other elements of the colony. For example, ants searching for food leave traces of a pheromone, like Hansel and Gretel’s breadcrumbs, along the way. When food is found, they return to the anthill reinforcing this pheromone trace as a signal and reminder to all the others. Similar techniques are used in routing software even if stigmergic hardware might be even more efficient, fast, and energy saving. Recently, a stigmergic photonic gate based on soliton waveguides has been proposed; this particular stigmergic hardware can switch the output ratio of the channels as a result of optical feedback. Based on these results, in this study, we analyze stigmergic electronic gates that can be addressed through external feedback, as the photonic ones do. We show that the nonlinear response of such gates must be based on quadratic saturating conductances driven by feedback signals. For this purpose, networks of stigmergic gates require two parallel and communicating current circuits: one to transmit information, and another for feedback signals to control the gate switching. We also show that by increasing the number of terminals per single gate, from 2 × 2 to 3 × 3 or higher, the overall power consumption can be reduced by a few orders of magnitude.