arXiv subjects

Andrew Adamatzky

Publications and source records attributed to Andrew Adamatzky.

At least 19 recordsLinked to original sources

arXiv2026

Fungi as functors: A category-theoretic approach to mycelial organisation

We develop a rigorous, equation-free category-theoretic foundation for fungal organisation. A fungal organism is formalised as a functor from a category $\Env$ of structured environmental states and admissible transformations to a category $\Myc$ of mycelial network states and biologically meaningful morphisms. An operational program category $\Prog$ models time-ordered exposure protocols, and a semantics functor $\mathcal{F}_{\mathrm{prog}}:\Prog\to\Myc$ maps experimental perturbations to induced network transformations. Species and strain variability are expressed as natural transformations between fungal functors, and ecological feedback is captured via an adjunction between sensing and environment modification. Network fusion (anastomosis) is identified with pushouts in $\Myc$, and order effects in exposure sequences are quantified by a local Lie structure and a Baker--Campbell--Hausdorff expansion near the identity program. A minimal worked exposure example demonstrates how non-commutativity yields experimentally testable quadratic scaling of order asymmetry. The framework provides a structurally explicit and falsifiable basis for analysing compositional perturbations, mixture coupling, robustness limits, and cross-species comparability in fungal systems.

cs.ET↗

arXiv2026

Machine Learning Driven Prediction of the Behavior of Biohybrid Actuators

Skeletal muscle-based biohybrid actuators have proved to be a promising component in soft robotics, offering efficient movement. However, their intrinsic biological variability and nonlinearity pose significant challenges for controllability and predictability. To address these issues, this study investigates the application of supervised learning, a form of machine learning, to model and predict the behavior of biohybrid machines (BHMs), focusing on a muscle ring anchored on flexible polymer pillars. First, static prediction models (i.e., random forest and neural network regressors) are trained to estimate the maximum exerted force achieved from input variables such as muscle sample, electrical stimulation parameters, and baseline exerted force. Second, a dynamic modeling framework, based on Long Short-Term Memory networks, is developed to serve as a digital twin, replicating the time series of exerted forces observed in response to electrical stimulation. Both modeling approaches demonstrate high predictive accuracy. The best performance of the static models is characterized by R2 of 0.9425, whereas the dynamic model achieves R2 of 0.9956. The static models can enable optimization of muscle actuator performance for targeted applications and required force outcomes, while the dynamic model provides a foundation for developing robustly adaptive control strategies in future biohybrid robotic systems.

cs.RO↗

arXiv2026

Fungal systems for security and resilience

Modern security, infrastructure, and safety-critical systems increasingly operate in environments characterised by disruption, uncertainty, physical damage, and degraded communications. Conventional digital technologies -- centralised sensors, software-defined control, and energy-intensive monitoring -- often struggle under such conditions. We propose fungi, and in particular living mycelial networks, as a novel class of biohybride systems for security, resilience, and protection in extreme environments. We discuss how fungi can function as distributed sensing substrates, self-healing materials, and low-observability anomaly-detection layers. We map fungal properties -- such as decentralised control, embodied memory, and autonomous repair -- to applications in infrastructure protection, environmental monitoring, tamper evidence, and long-duration resilience.

cs.ET↗

arXiv2026

Directional Electrical Spiking, Bursting, and Information Propagation in Oyster Mycelium Recorded with a Star-Shaped Electrode Array

Electrical activity in fungal mycelium has been reported in numerous species and experimental contexts, yet its spatial organisation and propagation remain insufficiently characterised. In this study we investigate the spatiotemporal structure of electrical potential dynamics in oyster mushroom (\textit{Pleurotus ostreatus}) mycelium colonising a wood-shavings substrate. Electrical signals were recorded using an eight-channel star-shaped differential electrode array providing angular resolution around a central region of colonised substrate. We analyse spike statistics, bursting behaviour, inter-channel correlations, and event-based propagation delays. The results reveal strong directional heterogeneity in spiking frequency and amplitude, clustered bursting dynamics, partial and localised coupling between channels, and reproducible propagation patterns across spatial sectors. Electrical bursts originate preferentially in specific directions and recruit other regions with with characteristic delays ranging from seconds to minutes to hours. These findings support the interpretation of fungal mycelium as a spatially extended excitable medium capable of slow, distributed electrical signalling and signal integration.

cs.ET↗

arXiv2025

Evaluating Fitness Averaging Strategies in Cooperative NeuroCoEvolution for Automated Soft Actuator Design

Soft robotics are increasingly favoured in specific applications such as healthcare, due to their adaptability, which stems from the non-linear properties of their building materials. However, these properties also pose significant challenges in designing the morphologies and controllers of soft robots. The relatively short history of this field has not yet produced sufficient knowledge to consistently derive optimal solutions. Consequently, an automated process for the design of soft robot morphologies can be extremely helpful. This study focusses on the cooperative NeuroCoEvolution of networks that are indirect representations of soft robot actuators. Both the morphologies and controllers represented by Compositional Pattern Producing Networks are evolved using the well-established method NeuroEvolution of Augmented Topologies (CPPN-NEAT). The CoEvolution of controllers and morphologies is implemented using the top n individuals from the cooperating population, with various averaging methods tested to determine the fitness of the evaluated individuals. The test-case application for this research is the optimisation of a soft actuator for a drug delivery system. The primary metric used is the maximum displacement of one end of the actuator in a specified direction. Additionally, the robustness of the evolved morphologies is assessed against a range of randomly generated controllers to simulate potential noise in real-world applications. The results of this investigation indicate that CPPN-NEAT produces superior morphologies compared to previously published results from multi-objective optimisation, with reduced computational effort and time. Moreover, the best configuration is found to be CoEvolution with the two best individuals from the cooperative population and the averaging of their fitness using the weighted mean method.

cs.ET↗

arXiv2025

Quantum-like Coherence Derived from the Interaction between Chemical Reaction and Its Environment

By uncovering the contrast between Artificial Intelligence and Natural-born Intelligence as a computational process, we define closed computing and open computing, and implement open computing within chemical reactions. This involves forming a mixture and invalidation of the computational process and the execution environment, which are logically distinct, and coalescing both to create a system that adjusts fluctuations. We model chemical reactions by considering the computation as the chemical reaction and the execution environment as the degree of aggregation of molecules that interact with the reactive environment. This results in a chemical reaction that progresses while repeatedly clustering and de-clustering, where concentration no longer holds significant meaning. Open computing is segmented into Token computing, which focuses on the individual behavior of chemical molecules, and Type computing, which focuses on normative behavior. Ultimately, both are constructed as an interplay between the two. In this system, Token computing demonstrates self-organizing critical phenomena, while Type computing exhibits quantum logic. Through their interplay, the recruitment of fluctuations is realized, giving rise to interactions between quantum logical subspaces corresponding to quantum coherence across different Hilbert spaces. As a result, spike waves are formed, enabling signal transmission. This occurrence may be termed quantum-like coherence, implying the source of enzymes responsible for controlling spike waves and biochemical rhythms.

cs.AI↗

arXiv2025

Post-apocalyptic computing from cellular automata

Cellular automata are arrays of finite state machines that can exist in a finite number of states. These machines update their states simultaneously based on specific local rules that govern their interactions. This framework provides a simple yet powerful model for studying complex systems and emergent behaviors. We revisit and reconsider the traditional notion of an algorithm, proposing a novel perspective in which algorithms are represented through the dynamic state-space configurations of cellular automata. By doing so, we establish a conceptual framework that connects computation to physical processes in a unique and innovative way. This approach not only enhances our understanding of computation but also paves the way for the future development of unconventional computing devices. Such devices could be engineered to leverage the inherent computational capabilities of physical, chemical, and biological substrates. This opens up new possibilities for designing systems that are more efficient, adaptive, and capable of solving problems in ways that traditional silicon-based computers cannot. The integration of cellular automata into these domains highlights their potential as a transformative tool in the ongoing evolution of computational theory and practice.

nlin.CG↗

arXiv2025

NEAT and HyperNEAT based Design for Soft Actuator Controllers

Since soft robotics are composed of compliant materials, they perform better than conventional rigid robotics in specific fields, such as medical applications. However, the field of soft robotics is fairly new, and the design process of their morphology and their controller strategies has not yet been thoroughly studied. Consequently, here, an automated design method for the controller of soft actuators based on Neuroevolution is proposed. Specifically, the suggested techniques employ Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT (HyperNEAT) to generate the synchronization profile of the components of a simulated soft actuator by employing Compositional Pattern Producing Networks (CPPNs). As a baseline methodology, a Standard Genetic Algorithm (SGA) was used. Moreover, to test the robustness of the proposed methodologies, both high- and low-performing morphologies of soft actuators were utilized as testbeds. Moreover, the use of an affluent and a more limited set of activation functions for the Neuroevolution targets was tested throughout the experiments. The results support the hypothesis that Neuroevolution based methodologies are more appropriate for designing controllers that align with both types of morphologies. In specific, NEAT performed better for all different scenarios tested and produced more simplistic networks that are easier to implement in real life applications.

cs.NE↗

arXiv2025

Designing morphologies of soft medical devices using cooperative neuro coevolution

Soft robots have proven to outperform traditional robots in applications related to propagation in geometrically constrained environments. Designing these robots and their controllers is an intricate task, since their building materials exhibit non-linear properties. Human designs may be biased; hence, alternative designing processes should be considered. We present a cooperative neuro coevolution approach to designing the morphologies of soft actuators and their controllers for applications in drug delivery apparatus. Morphologies and controllers are encoded as compositional pattern-producing networks evolved by Neuroevolution of Augmented Topologies (NEAT) and in cooperative coevolution methodology, taking into account different collaboration methods. Four collaboration methods are studied: n best individuals, n worst individuals, n best and worst individuals, and n random individuals. As a performance baseline, the results from the implementation of Age-Fitness Pareto Optimisation (AFPO) are considered. The metrics used are the maximum displacement in upward bending and the robustness of the devices in terms of applying to the same evolved morphology a diverse set of controllers. Results suggest that the cooperative neuro coevolution approach can produce more suitable morphologies for the intended devices than AFPO.

cs.NE↗

arXiv2025

Graph-based Complexity and Computational Capabilites of Proteinoid Spike Systems

Proteinoids, as soft matter fluidic systems, are computational substrates that have been recently proposed for their analog computing capabilities. Such systems exhibit oscillatory electrical activity because of cationic and anionic exchange inside and outside such gels. It has also been recently shown that this (analog) electrical activity, when sampled at fixed time intervals, can be used to reveal their underlying information-theoretic, computational code. This code, for instance, can be expressed in the (digital) language of Boolean gates and QR codes. Though, this might seem as a good evidence that proteinoid substrates have computing abilities when subjected to analog-to-digital transition, the leap from their underlying computational code to computing abilities is not well demonstrated. In this work, we analyse the electrical activity patterns of proteinoid substrates using computational methods including deep ReLU networks. Our findings suggest intriguing parallels between these patterns and certain computational frameworks, hinting at the possibility that such chemical systems might possess inherent information-processing and computational capabilities. To demonstrate the computational ability, we construct a prediction algorithm which acts as a binary classification model and extract 16-dimensional vector data from the proteinoid spike, in order to perform predictions with 70.41% accuracy. This model in its core has a unique transformation modality, inspired from number-theoretic sieve theory, and is combination of two functions: spiral sampling F1 and significant digit extraction F2 functions. The complexity of the transformed data is measured using eight distinct metrics, and effectively, using a single meta-metric.

physics.flu-dyn↗

arXiv2025

Phase transitions in swarm optimization algorithms

physics.comp-ph↗

arXiv2025

Perturbations and Phase Transitions in Swarm Optimization Algorithms

Natural systems often exhibit chaotic behavior in their space-time evolution. Systems transiting between chaos and order manifest a potential to compute, as shown with cellular automata and artificial neural networks. We demonstrate that swarm optimization algorithms also exhibit transitions from chaos, analogous to a motion of gas molecules, when particles explore solution space disorderly, to order, when particles follow a leader, similar to molecules propagating along diffusion gradients in liquid solutions of reagents. We analyze these `phase-like' transitions in swarm optimization algorithms using recurrence quantification analysis and Lempel-Ziv complexity estimation. We demonstrate that converging iterations of the optimization algorithms are statistically different from non-converging ones in a view of applied chaos, complexity and predictability estimating indicators. An identification of a key factor responsible for the intensity of their phase transition is the main contribution of this paper. We examined an optimization as a process with three variable factors -- an algorithm, number generator and optimization function. More than 9.000 executions of the optimization algorithm revealed that the nature of an applied algorithm itself is the main source of the phase transitions. Some of the algorithms exhibit larger transition-shifting behavior while others perform rather transition-steady computing. These findings might be important for future extensions of these algorithms.

physics.comp-ph↗

arXiv2024

Control of Biohybrid Actuators using NeuroEvolution

In medical-related tasks, soft robots can perform better than conventional robots because of their compliant building materials and the movements they are able perform. However, designing soft robot controllers is not an easy task, due to the non-linear properties of their materials. Since human expertise to design such controllers is yet not sufficiently effective, a formal design process is needed. The present research proposes neuroevolution-based algorithms as the core mechanism to automatically generate controllers for biohybrid actuators that can be used on future medical devices, such as a catheter that will deliver drugs. The controllers generated by methodologies based on Neuroevolution of Augmenting Topologies (NEAT) and Hypercube-based NEAT (HyperNEAT) are compared against the ones generated by a standard genetic algorithm (SGA). In specific, the metrics considered are the maximum displacement in upward bending movement and the robustness to control different biohybrid actuator morphologies without redesigning the control strategy. Results indicate that the neuroevolution-based algorithms produce better suited controllers than the SGA. In particular, NEAT designed the best controllers, achieving up to 25% higher displacement when compared with SGA-produced specialised controllers trained over a single morphology and 23% when compared with general purpose controllers trained over a set of morphologies.

cs.RO↗

arXiv2024

Complexity and nonlinearity of colloid electrical transducers

This work explores the complexity and nonlinearity of seven different colloidal suspensions-Au, ferrofluid, TiO2}, ZnO, g-C3N4, MXene, and PEDOT:PSS-when electrically stimulated with fractal, chaotic, and random binary signals. The recorded electrical responses were analyzed using entropy, file compression, fractal dimension, and Fisher information measures to quantify complexity. The nonlinearity introduced by each colloid was evaluated by the deviation of the output from the best-fit hyperplane of the input-output mapping. The results showed that TiO2 was the most complex colloid across all inputs, exhibiting high entropy, poor compressibility, and an unpredictable response pattern. The colloids also exhibited significant nonlinearity, making them promising candidates for reservoir computation, where the mapping of inputs into high-dimensional nonlinear states is advantageous. This study provides insight into the dynamics of colloids and their potential for unconventional computational applications that exploit their inherent complexity and nonlinearity, and it provides a rapid method for assessing the suitability of a particular material for use as a computational substrate before others.

cond-mat.soft↗

arXiv2024

Using neuroevolution for designing soft medical devices

Soft robots can exhibit better performance in specific tasks compared to conventional robots, particularly in healthcare-related tasks. However, the field of soft robotics is still young, and designing them often involves mimicking natural organisms or relying heavily on human experts' creativity. A formal automated design process is required. We propose the use of neuroevolution-based algorithms to automatically design initial sketches of soft actuators that can enable the movement of future medical devices, such as drug-delivering catheters. The actuator morphologies discovered by algorithms like Age-Fitness Pareto Optimization, NeuroEvolution of Augmenting Topologies (NEAT), and Hypercube-based NEAT (HyperNEAT) were compared based on the maximum displacement reached and their robustness against various control methods. Analyzing the results granted the insight that neuroevolution-based algorithms produce better-performing and more robust actuators under different control methods. Moreover, the best-performing morphologies were discovered by the NEAT algorithm. As a future work aspect, we propose using the morphologies discovered here as test beds to optimize specialized controllers, enabling more effective functionality towards the desired deflections of the suggested soft catheters.

cs.RO↗

arXiv2024

NeuroEvolution algorithms applied in the designing process of biohybrid actuators

Soft robots diverge from traditional rigid robotics, offering unique advantages in adaptability, safety, and human-robot interaction. In some cases, soft robots can be powered by biohybrid actuators and the design process of these systems is far from straightforward. We analyse here two algorithms that may assist the design of these systems, namely, NEAT (NeuroEvolution of Augmented Topologies) and HyperNEAT (Hypercube-based NeuroEvolution of Augmented Topologies). These algorithms exploit the evolution of the structure of actuators encoded through neural networks. To evaluate these algorithms, we compare them with a similar approach using the Age Fitness Pareto Optimization (AFPO) algorithm, with a focus on assessing the maximum displacement achieved by the discovered biohybrid morphologies. Additionally, we investigate the effects of optimization against both the volume of these morphologies and the distance they can cover. To further accelerate the computational process, the proposed methodology is implemented in a client-server setting; so, the most demanding calculations can be executed on specialized and efficient hardware. The results indicate that the HyperNEAT-based approach excels in identifying morphologies with minimal volumes that still achieve satisfactory displacement targets.

cs.RO↗

arXiv2024

On complexity of colloid cellular automata

The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from the colloids. We analyse the space-time complexity of Boolean circuits derived from the electrical responses of colloids: ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the mineral zincite), proteinoids (microspheres and crystals of thermal abiotic proteins), and combinations thereof to electrical stimulation. To extract Boolean circuits from colloids, we send all possible configurations of two-, four-, and eight-bit binary strings, encoded as electrical potential values, to the colloids, record their responses, and thereby infer the Boolean functions they implement. We map the discovered functions onto the cell-state transition rules of cellular automata (arrays of binary state machines that update their states synchronously according to the same rule) -- the colloid cellular automata. We then analyse the phenomenology of the space-time configurations of the automata and evaluate their complexity using measures such as compressibility, Shannon entropy, Simpson diversity, and expressivity. A hierarchy of phenomenological and measurable space-time complexity is constructed.

nlin.CG↗

arXiv2024

Information-theoretic language of proteinoid gels: Boolean gates and QR codes

With an aim to build analog computers out of soft matter fluidic systems in future, this work attempts to invent a new information-theoretic language, in the form of two-dimensional Quick Response (QR) codes. This language is, effectively, a digital representation of the analog signals shown by the proteinoids. We use two different experimental techniques: (i) a voltage-sensitive dye and (ii) a pair of differential electrodes, to record the analog signals. The analog signals are digitally approximatied (synthesised) by sampling the analog signals into a series of discrete values, which are then converted into binary representations. We have shown the AND-OR-NOT-XOR-NOR-NAND-XNOR gate representation of the digitally sampled signal of proteinoids. Additional encoding schemes are applied to convert the binary code identified above to a two-dimensional QR code. As a result, the QR code becomes a digital, unique marker of a given proteinoid network. We show that it is possible to retrieve the analog signal from the QR code by scanning the QR code using a mobile phone. Our work shows that soft matter fluidic systems, such as proteinoids, can have a fundamental informatiom-theoretic language, unique to their internal information transmission properties (electrical activity in this case) - such a language can be made universal and accessible to everyone using 2D QR codes, which can digitally encode their internal properties and give an option to recover the original signal when required. On a more fundamental note, this study identifies the techniques of approximating continuum properties of soft matter fluidic systems using a series representation of gates and QR codes, which are a piece-wise digital representation, and thus one step closer to programming the fluids using information-theoretic methods, as suggested almost a decade ago by Tao's fluid program.

cs.ET↗