Reservoir computing has become immensely popular for exploiting physical systems for computation. A multitude of physical reservoirs have been demonstrated, ranging from assembles of nanomagnets to living cultures of neurons. Unlike abstract software reservoirs, physical reservoirs are subject to spatial constraints which restricts the possible reservoir topologies. Here, we investigate lattice reservoirs, where nodes are placed on a regular lattice which defines the reservoir topology and its weights. Despite their simple regular structure, lattice reservoirs perform surprisingly well, in some cases outcompeting classical Echo State Networks. A key finding is the need for directed edges to facilitate information flow within the reservoir, highlighting the importance of symmetry breaking in physical reservoirs. We take advantage of the spatial nature of lattice reservoirs to discover key computational structures within, revealing what these reservoirs are actually doing. Lattice reservoirs bridge the gap between physics and computation, providing invaluable insight for the design and understanding of physical reservoirs.

Artificial spin ice is a self-organising system of interacting nanomagnets which exhibits interesting and complex behaviour. In this paper we put the art in artificial spin ice, presenting a novel mapping from a dynamical state trajectory to MIDI music for an ensemble of instruments. An evolutionary algorithm is used to search for new artificial spin ice geometries of higher musical quality, making use of Zipfian and entropic measures. Geometries of high fitness were discovered, and music resulting from the best geometry found is presented alongside this paper. Aside from the primary outcome of producing novel music, this unique viewpoint of artificial spin ice could allow for a more intuitive observation of its dynamical properties, interpreting their state trajectories through the medium of music.

Arrangements of nanomagnets known as artificial spin ices show great potential for use in unconventional computation. The majority of exploratory work done in this area considers just a small handful of well studied geometries (nanomagnetic arrangements), and uses them as if they were a black box. Here we detail a novel representation of artificial spin ice geometries, which lends itself to the tuning and evolutionary search of geometries. Using our representation we present geometries tuned to exhibit a desired computational or meta-material property. This is the first example of such a search performed on artificial spin ice.

Artificial spin ice (ASI) are systems of coupled nanomagnets arranged on a 2D lattice. ASIs are promising computing substrates due to the rich variety of emergent behavior, accompanied by considerable control and flexibility. Computational models may exploit the small-scale dynamics of the individual elements, or large-scale emergent behavior of the resulting metamaterial. We investigate the computational capabilities of “pinwheel” ASI, whose emergent ferromagnetic patterns can be observed at different scales. Within a reservoir computing framework, we examine how key system parameters affect performance using well-established reservoir quality metrics. As reservoir output, we consider system state at different granularities, ranging from individual magnets to the collective state of multiple magnets. Our results show that pinwheel ASI exhibits excellent computing capacity, including evidence of fading memory. Interestingly, a wide range of output granularities result in good performance, offering new insights into the scalability and robustness of reservoirs based on self-organized collective behavior. The apparent flexibility in output granularity show that ASIs have computational properties at different abstraction levels, from the small-scale dynamics of simple elements, to the large-scale spatial patterns of the metamaterial.

We explore artificial spin ice (ASI) as a substrate for material computation . ASI consists of large numbers of nanomagnets arranged in a 2D lattice. Local interactions between the magnets gives rise to a range of complex collective behavior. The ferromagnets form large networks of nonlinear nodes, which in many ways resemble artificial neural networks. In this work, we investigate key computational properties of ASI through micromagnetic simulations. Our nanomagnetic system exhibits a large number of reachable stable states and a wide range of available dynamics when perturbed by an external magnetic field. Furthermore, we find that the system is able to store and process temporal input patterns. The emergent behavior is highly tunable by varying the parameters of the external field. Our findings highlight ASI as a very promising substrate for in-materio computation at the nanoscale.

The human brain is a remarkable computing machine, i.e. vastly parallel, self-organizing, robust, and energy efficient. To gain a better understanding into how the brain works, a cyborg (cybernetic organism, a combination of machine and living tissue) is currently being made in an interdisciplinary effort, known as the Cyborg project. In this paper we describe how living cultures of neurons (biological neural networks) are successfully grown in-vitro over Micro-Electrode Arrays (MEAs), which allow them to be interfaced to a robotic body through electrical stimulation and neural recordings. Furthermore, we describe the bio- and nano-technological procedures utilized for the culture of such dissociated neural networks and the interface software and hardware framework used for creating a closed-loop hybrid neuro-system. A Reservoir Computing (RC) approach is used to harness the computational power of the neuronal culture.

Reservoir Computing has been highlighted as a promising methodology to perform computation in dynamical systems. This makes Reservoir Computing particularly interesting for exploiting physical systems directly as computing substrates, where the computation happens “for free” in the rich physical domain. In this work we consider a simple chaotic circuit as a reservoir: the Driven Chua’s circuit. Its rich variety of available dynamics makes it versatile as a reservoir. At the same time, its simplicity offers insight into what physical properties can be useful for computation. We demonstrate both through simulation and in-circuit experiments, that such a simple circuit can be readily exploited for computation. Our results show excellent performance on two non-temporal tasks. The fact that such a simple nonlinear circuit can be used, suggests that a wide variety of physical systems can be viewed as potential reservoirs.