Cellular Automata (CAs) have potential as powerful parallel computational systems, which has lead to the use of CAs as reservoirs in reservoir computing. However, why certain Cellular Automaton (CA) rules, sizes and input encodings are better or worse at a given task is not well understood. We present a method that enables identification and visualization of the specific information content, flow and transformations within the space-time diagram of CA. We interpret each spatio-temporal location in CA’s space-time diagram as a function of its input and call this novel notion the CA’s Canonical Computations (CCs). This allows us to analyze the available information from the space-time diagrams as partitions of the input set. The method also reveals how input-encoder-rule interactions transform the information flow by changing features like spatial and temporal location stability as well as the specific information produced. This general approach for analysing CA is discussed for the engineering of reservoir computing systems.

Random Boolean networks (RBN) and Cellular Automata (CA) operate in a very similar way. They update their state with simple deterministic functions called Boolean function or Transition Table (TT), both being essentially the same mechanism under different names. This paper applies a concept most known from CA called Minimum Equivalence (ME). ME is applied to RBN and shows how to calculate the number of unique computations for a given number of neighbours. Crucially, it is shown how RBN rules are even more equivalent than in CA, how the set can be reduced into even fewer unique rules, and how the concept becomes more relevant with larger neighbourhoods. For example, switching transformation alone reduces the number of unique rules in RBN with 4 neighbours from 65 536 to only 3 984 (6.1%) rules. Additionally, this paper examines the ME and transformations in substrates beyond Elementary CA (ECA), such as CA with additional spatial dimensions and number of states.

Reservoir Computing with Cellular Automata (ReCA) is a promising concept by virtue of its potential for efficient hardware implementation and theoretical understanding of Cellular Auotmata (CA). However, ReCA has so far only been studied in exploratory studies. In this work, we take a more in depth view of the landscape of Elementary Cellular Automata for Reservoir Computing. In this paper, the ReCA is applied to the X-bit memory benchmark with a thorough exploration for key parameters including number of random mappings ( R ), number of bits ( N b ) and size of the vector that the random mapping is mapped to ( L d ). Our evidence shows that the parameter space, including the full panoply of CA rules, is much richer then what previous evidence indicates. This suggests that some CA rules would require careful consideration and custom parameters setup.

In the Gathering of the Hive project, the societal and ecological implications, as well as technological possibilities of swarm robotics are explored through artistic methodology applied to Artificial Life. These matters are examined through an algorithm inspired by the clustering behaviour of honeybees applied to a swarm of Thymio robots interacting in a physical, changing environment. This work is a part of the ongoing FELT project (Futures of Living Technologies), which explores artificial life systems through art and technology.

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.