While Federated learning (FL) is considered privacy-preserving by nature, it remains vulnerable to many attacks, such as data and model poisoning, that compromise data integrity and model accuracy. Conventional privacy-preserving federated learning (PPFL) mechanisms, including homomorphic encryption (HE), secure aggregation, and secure multiparty computation (SMPC) demonstrate several limitations, such as high computational complexity, significant communication overhead, and scalability challenges. To overcome the aforementioned issues, we propose an end-to-end secure FL architecture that integrates differential privacy (DP), zero-knowledge proof (ZKP), and median aggregation. DP prevents data leakage during model updates by introducing Laplacian noise for privacy preservation. ZKP is implemented through Schnorr’s protocol, which enables lightweight and efficient client authentication without revealing sensitive information. Finally, median aggregation is incorporated to mitigate the impact of outliers and adversarial updates, ensuring robust prediction aggregation. The experimental results indicate that the proposed approach outperforms other well-known PPFL methods including partially homomorphic encryption (PHE), fully homomorphic encryption (FHE) and SMPC. It delivers substantial improvements in global accuracy, especially for larger client counts, with gains of 10%-30% over the other methods. The client training time is significantly reduced by 70%-90%, ensuring faster processing. The approach also excels at reducing average round latency by 80%-95%, enhancing the overall efficiency of the system. Communication overhead is significantly reduced by 65%-85%, lowering data transfer costs per round. Furthermore, the size of the model is minimized by 60%-85%, making it more resource efficient and scalable for larger deployments.

This paper presents the applicability of a mycelium-inspired reservoir network through an innovative extension of the Memristive Oscillating Cellular Automata (MOCA) circuit-based network. Drawing on the adaptive, self-organizing properties of mycelium, this MOCA grid employs SiNx-based RRAMs to form reconfigurable, dynamic connections that replicate mycelial network behaviors. Using the Stanford-PKU RRAM model, the non-linear properties of the fabricated devices have been characterized, establishing a flexible reservoir network capable of transforming and encoding input signals. The network has been evaluated, demonstrating small-world characteristics, including high clustering and short average path lengths, critical for effective information propagation and complex local dynamics. The resulting adaptable circuit offers a scalable foundation for future applications in bioinspired reservoir computing.

Mycelium, the vegetative structure of fungi, exhibits complex electrical signaling patterns when stimulated that resemble neural-like activity, which can be leveraged for bio-inspired computing and sensing applications. To replicate this activity, a 100x100 grid-based circuit has been designed capable of spiking behavior and adaptable configuration thanks to memristive technology aligned with fabricated devices, mimicking the dynamics seen in mycelial networks. Simulations have been carried out to demonstrate that the memristive grid successfully replicates key aspects of mycelium’s electrical activity recorded from experimental setups, including response to environmental stimuli, spiking signal propagation, and eradication.

ABSTRACT The characteristics of soft robots make them better candidates for applications such as healthcare, due to their enhanced safety, adaptability, and more natural human‐robot interaction compared to traditional counterparts. Different actuating systems have been proposed for soft robotics. On the other hand, since this technology is fairly young, the design process of soft actuators is not yet well formalized. In an attempt to enhance the applicability of this type of actuator, the utilization of a NeuroEvolution algorithm to automatically design them is proposed here. More specifically, Hypercube‐based NeuroEvolution of Augmented Topologies (HyperNEAT) is investigated for different substrate architectures. These substrates are Artificial Neural Networks that encode the three‐dimensional representation of the soft actuators. The produced three‐dimensional sketches are tested within a simulated environment under two different targets (the maximum displacement and the combination of maximum displacement and minimum actuator volume) to identify the suitability of HyperNEAT as an efficient designing methodology. Since the evaluation of candidate solutions under a physics simulator is the most computationally demanding process, the proposed methodology was realized under a client‐server setting, with the aim of accelerating the evolutionary optimization of actuator sketches. The evaluation part of the algorithm was outsourced to the server side, which can be a specialized and high‐performing computational entity. The resulting soft actuators of this study proved to be of higher competence when compared with actuators derived under previously published evolutionary methodologies.

Abstract Mycelium, the intricate vegetative network of fungi, has emerged as a promising candidate within the realm of engineered living materials (ELMs). While its intriguing structural and electrical properties highlight its potential, mycelium growth is highly sensitive to environmental conditions. To bridge this gap, a robust framework was developed to both model mycelium growth and explore its computational capabilities. This framework uses a cellular automata (CA) approach, enhanced with reaction-diffusion (RD) processes, to simulate mycelium growth under diverse environmental conditions. This configuration, combined with tunable parameters, enables the identification and validation of optimal growth patterns, supported by an algorithm designed to extract key features of hyphae–the fundamental building blocks of the mycelial network. Subsequently, the small-world properties of the modeled mycelium networks were investigated, revealing high clustering coefficients and short path lengths, characteristics that make them well-suited for reservoir computing (RC). To demonstrate their computational capabilities, mycelium-inspired RC architectures were evaluated on the MNIST dataset classification task, achieving an accuracy of up to 97.09%, highlighting the effectiveness of biologically inspired models. As a result, this framework establishes a comprehensive test-bench for mycelium modeling, growth, and computational exploration, paving the way for innovative applications in bio-inspired computing.

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. A formal design process is needed since human expertise to design such controllers is not sufficiently effective. 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.

Neural action potentials (APs) are difficult to interpret as signal encoders and/or computational primitives. Their relationships with stimuli and behaviors are obscured by the staggering complexity of nervous systems themselves. We can reduce this complexity by observing that "simpler" neuron-less organisms also transduce stimuli into transient electrical pulses that affect their behaviors. Without a complicated nervous system, APs are often easier to understand as signal/response mechanisms. We review examples of nonneural stimulus transductions in domains of life largely neglected by theoretical neuroscience: bacteria, protozoans, plants, fungi, and neuron-less animals. We report properties of those electrical signals-for example, amplitudes, durations, ionic bases, refractory periods, and particularly their ecological purposes. We compare those properties with those of neurons to infer the tasks and selection pressures that neurons satisfy. Throughout the tree of life, nonneural stimulus transductions time behavioral responses to environmental changes. Nonneural organisms represent the presence or absence of a stimulus with the presence or absence of an electrical signal. Their transductions usually exhibit high sensitivity and specificity to a stimulus, but are often slow compared to neurons. Neurons appear to be sacrificing the specificity of their stimulus transductions for sensitivity and speed. We interpret cellular stimulus transductions as a cell's assertion that it detected something important at that moment in time. In particular, we consider neural APs as fast but noisy detection assertions. We infer that a principal goal of nervous systems is to detect extremely weak signals from noisy sensory spikes under enormous time pressure. We discuss neural computation proposals that address this goal by casting neurons as devices that implement online, analog, probabilistic computations with their membrane potentials. Those proposals imply a measurable relationship between afferent neural spiking statistics and efferent neural membrane electrophysiology.

Colloids subjected to electrical stimuli exhibit a reconfiguration that might be used to store information and potentially compute. In a colloidal suspension of ZnO nanoparticles in DMSO, we investigated the learning, memorization, time and stimulation’s voltage dependence of conductive network formation. The relationships between the critical resistance and stimulation time have been reconstructed.The critical voltage (i.e., the stimulation voltage required to drop the resistance) was found to decrease as stimulation time increased. We characterised a dispersion of conductive ZnO nanoparticles in the DMSO polymeric matrix using FESEM and UV–visible absorption spectrum.

There is a continuous urge for higher efficiency in conventional computing systems, driven by an ever-growing demand for these systems’ complexity to be able to match the one of convoluted and challenging problems. However, this type of problems has formulated the benchmarks for unconventional computing systems to validate their emerging applicability and prove their effectiveness. Towards this path, Cellular Automata (CAs) have been established as a promising mathematical tool for simulating physical processes and demonstrated a favourable methodology for effectively implementing computations in hardware by taking advantage of their inherent parallelism. Representing CAs with oscillating memristive networks could further enhance the performance of these systems, by incorporating the rich dynamics evident in memristors and their strong memory and computing features. In this work, a wave generator circuit has been designed with low-voltage fabricated CBRAM devices, that is able to act as a Wave Cellular Automaton (WCA). These wave generation units are located on a grid with adjusting multi-directional interconnections between neighbors. In addition to that, the ability to reconFigure the amount of such units that influence each other, facilitates the propagation of voltage signals through the grid following wave propagation features. An example of this computational domain is presented with the realization of complex logic gates on the grid of WCAs.

The threshold switching effect is critical in memristor devices for a range of applications, from crossbar design reliability to simulating neuromorphic features using artificial neural networks. The rich inherit dynamics of a metallic conductive filament (CF) formation are thought to be linked to this characteristic. Simulating these dynamics is necessary to develop an accurate memristor model. In this work we present a compact memristor model that utilizes the drift, diffusion and thermo-diffusion effects. These three effects are taken into consideration to derive the switching behavior of a memristor. The resistance of a memristor is calculated based on the evolution of a truncated cone shaped filament. The objective of this model is to achieve a realistic integration of switching mechanisms of the memristor device, while minimizing the overhead on computing resources and being compatible with circuit design tools. The model incorporates the effect of thermo-diffusion on the switching pattern, providing a different perception of the ionic transport processes, which enable the unipolar switching. SPICE simulation results provide an exact match with experimental results of Metal-Insulator-Metal (MIM) memristive devices of Ag/Si2/SiO2.07/Pt nanoparticles (NPs) configuration.

Synchronization transition in neuromorphic networks has attracted much attention recently as a fundamental property of biological neural networks, which relies on network connectivity along with different synaptic features. In this work, an area-optimized FPGA implementation of an Asynchronous Cellular Automata Neuron model that exhibits discrete-state neuron dynamics is introduced. The proposed neuron model is capable of reproducing various neuromorphic oscillations observed in biological neurons using less hardware resources than previous implementations. We investigate synchronization transitions with a focus on the emergence of chimera states in a ring-based network consisting of hardware-based neurons with electrical synaptic coupling. In particular, we study the effects on the network’s phase synchronization through changing two control parameters: the coupling range and the coupling strength. We indicate that via proper configuration of the coupling parameters, we influence the synchronization transition and reveal chimera states which have been associated with neurological disorders.

Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication. To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of nanoelectronic devices is now reaching the scale of atoms or molecules - a technical goal undoubtedly demanding for novel devices. Following the trend, we explore an unconventional route of implementing reservoir computing on a single protein molecule and introduce neuromorphic connectivity with a small-world networking property. We have chosen Izhikevich spiking neurons as elementary processors, corresponding to the atoms of verotoxin protein, and its molecule as a `hardware' architecture of the communication networks connecting the processors. We apply on a single readout layer, various training methods in a supervised fashion to investigate whether the molecular structured Reservoir Computing (RC) system is capable to deal with machine learning benchmarks. We start with the Remote Supervised Method, based on Spike-Timing-Dependent-Plasticity, and carry on with linear regression and scaled conjugate gradient back-propagation training methods. The RC network is evaluated as a proof-of-concept on the handwritten digit images from the standard MNIST and the extended MNIST datasets and demonstrates acceptable classification accuracies in comparison with other similar approaches.

Since CMOS technology approaches its physical limits, the spotlight of computing technologies and architectures shifts to unconventional computing approaches. In this area, novel computing systems, inspired by natural and mostly nonelectronic approaches, provide also new ways of performing a wide range of computations, from simple logic gates to solving computationally hard problems. Reaction-diffusion processes constitute an information processing method, occurs in nature and are capable of massive parallel and low-power computing, such as chemical computing through Belousov-Zhabotinsky reaction. In this paper, inspired by these chemical processes and based on the wave-propagation information processing taking place in the reaction-diffusion media, the novel characteristics of the nanoelectronic element memristor are utilized to design innovative circuits of electronic excitable medium to perform both classical (Boolean) calculations and to model neuromorphic computations in the same Memristor-RLC (M-RLC) reconfigurable network.

An accurate modelling of bio-electrochemical processes that govern Microbial Fuel Cells (MFCs) and mapping their behavior according to several parameters will enhance the development of MFC technology and enable their successful implementation in well defined applications. The geometry of the electrodes is among key parameters determining efficiency of MFCs due to the formation of a biofilm of anodophilic bacteria on the anode electrode, which is a decisive factor for the functionality of the device. We simulate the bio-electrochemical processes in an MFC while taking into account the geometry of the electrodes. Namely, lattice Boltzmann methods are used to simulate the fluid dynamics and the advection-diffusion phenomena in the anode compartment. The model is verified on voltage and current outputs of a single MFC derived from laboratory experiments under continuous flow. Conclusions can be obtained from a parametric analysis of the model concerning the design of the geometry of the anode compartment, the positioning and microstructure of the anode electrode, in order to achieve more efficient overall performance of the system. An example of such a parametric analysis is presented here, taking into account the positioning of the electrode in the anode compartment.

Unconventional computing has been studied intensively, even after the appearance of CMOS technology. Currently, it has returned to the spotlight because CMOS is about to reach its physical limits, given that the constant demand for more computational power requires for novel unconventional computing solutions. In this area, the oscillatory internal motion mechanism of slime mould, namelyPhysarum Polycephalum, could serve as an alternative concept for the design and development of electronic circuits that exploit the memristive dynamics and simple LC contours to deliver solutions for computationally hard to be solved problems. In this direction, this work presents how bio-inspired memristive LC oscillators with a coupling capacitor can be synchronized to perform the functionalities of a biological neuron, also able to execute more complex computations, aiming to model biological neural systems much more advanced than the neuron-less slime mould biological organism. This work proposes a connection between the function mechanism of a simple biological organism and that of complex biological systems, made in a plausible and sufficient manner, towards unconventional computation with memristors.

The slime mold Physarum polycephalum is a huge single cell that has proved to be a fruitful material for designing novel computing architectures. The slime mold is capable of sensing tactile, chemical, and optical stimuli and converting them to characteristic patterns of its electrical potential oscillations. The electrical responses to stimuli may propagate along protoplasmic tubes for distances exceeding tens of centimeters, as impulses in neural pathways do. A slime mold makes decisions about its propagation direction based on information fusion from thousands of spatially extended protoplasmic loci, similarly to a neuron collecting information from its dendritic tree. The analogy is distant yet inspiring. We speculate on whether alternative-would-be-nervous systems can be developed and practically implemented from the slime mold. We uncover analogies between the slime mold and neurons, and demonstrate that the slime mold can play the roles of primitive mechanoreceptors, photoreceptors, and chemoreceptors; we also show how the Physarum neural pathways develop. The results constituted the first step towards experimental laboratory studies of nervous system implementation in slime molds.

Elementary cellular automata (ECA) are linear arrays of finite-state machines (cells) which take binary states, and update their states simultaneously depending on states of their closest neighbours. We design and study ECA with memory (ECAM), where every cell remembers its states during some fixed period of evolution. We characterize complexity of ECAM in a case study of rule 126, and then provide detailed behavioural classification of ECAM. We show that by enriching ECA with memory we can achieve transitions between the classes of behavioural complexity. We also show that memory helps to ‘discover’ hidden information and behaviour on trivial (uniform, periodic), and non-trivial (chaotic, complex) dynamical systems.

Man-made transport networks and their design are closely related to the shortest path problem and considered amongst the most debated problems of computational intelligence. Apart from using conventional or bio-inspired computer algorithms, many researchers tried to solve this kind of problem using biological computing substrates, gas-discharge solvers, prototypes of a mobile droplet, and hot ice computers. In this aspect, another example of biological computer is the plasmodium of acellular slime mould Physarum polycephalum (P. polycephalum), which is a large single cell visible by an unaided eye and has been proven as a reliable living substrate for implementing biological computing devices for computational geometry, graph-theoretical problems, and optimization and imitation of transport networks. Although P. polycephalum is easy to experiment with, computing devices built with the living slime mould are extremely slow; it takes slime mould days to execute a computation. Consequently, mapping key computing mechanisms of the slime mould onto silicon would allow us to produce efficient bio-inspired computing devices to tackle with hard to solve computational intelligence problems like the aforementioned. Toward this direction, a cellular automaton (CA)-based, Physarum-inspired, network designing model is proposed. This novel CA-based model is inspired by the propagating strategy, the formation of tubular networks, and the computing abilities of the plasmodium of P. polycephalum. The results delivered by the CA model demonstrate a good match with several previously published results of experimental laboratory studies on imitation of man-made transport networks with P. polycephalum. Consequently, the proposed CA model can be used as a virtual, easy-to-access, and biomimicking laboratory emulator that will economize large time periods needed for biological experiments while producing networks almost identical to the tubular networks of the real-slime mould.

A memristor is often identified by showing its distinctive pinched hysteresis curve and testing for the effect of frequency. The hysteresis size should relate to frequency and shrink to zero as the frequency approaches infinity. Although mathematically understood, the material causes for this are not well known. The d.c. response of the memristor is a decaying curve with its own timescale. We show via mathematical reasoning that this decaying curve when transformed to a.c. leads to the frequency effect by considering a descretized curve. We then demonstrate the validity of this approach with experimental data from two different types of memristors.

Neuromorphic computing is a brainlike information processing paradigm that requires adaptive learning mechanisms. A spiking neuro-evolutionary system is used for this purpose; plastic resistive memories are implemented as synapses in spiking neural networks. The evolutionary design process exploits parameter self-adaptation and allows the topology and synaptic weights to be evolved for each network in an autonomous manner. Variable resistive memories are the focus of this research; each synapse has its own conductance profile which modifies the plastic behaviour of the device and may be altered during evolution. These variable resistive networks are evaluated on a noisy robotic dynamic-reward scenario against two static resistive memories and a system containing standard connections only. The results indicate that the extra behavioural degrees of freedom available to the networks incorporating variable resistive memories enable them to outperform the comparative synapse types.

A cellular slime mould Physarum polycephalum is a monstrously large single cell visible by an unaided eye. The slime mold explores space in parallel, is guided by gradients of chemoattractants, and propagates toward sources of nutrients along nearly shortest paths. The slime mold is a living prototype of amorphous biological computers and robotic devices capable of solving a range of tasks of graph optimization and computational geometry. When presented with a distribution of nutrients, the slime mold spans the sources of nutrients with a network of protoplasmic tubes. This protoplasmic network matches a network of major transport routes of a country when configuration of major urban areas is represented by nutrients. A transport route connecting two cities should ideally be a shortest path, and this is usually the case in computer simulations and laboratory experiments with flat substrates. What searching strategies does the slime mold adopt when exploring 3-D terrains? How are optimal and transport routes approximated by protoplasmic tubes? Do the routes built by the slime mold on 3-D terrain match real-world transport routes? To answer these questions, we conducted pioneer laboratory experiments with Nylon terrains of USA and Germany. We used the slime mold to approximate route 20, the longest road in USA, and autobahn 7, the longest national motorway in Europe. We found that slime mold builds longer transport routes on 3-D terrains, compared to flat substrates yet sufficiently approximates man-made transport routes studied. We demonstrate that nutrients placed in destination sites affect performance of slime mold, and show how the mold navigates around elevations. In cellular automaton models of the slime mold, we have shown variability of the protoplasmic routes might depends on physiological states of the slime mold. Results presented will contribute toward development of novel algorithms for sensorial fusion, information processing, and decision making, and will provide inspirations in design of bioinspired amorphous robotic devices.

This paper presents a spiking neuroevolutionary system which implements memristors as plastic connections, i.e., whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and variable topologies, allowing the number of neurons, connection weights, and interneural connectivity pattern to emerge. By comparing two phenomenological real-world memristor implementations with networks comprised of: 1) linear resistors, and 2) constant-valued connections, we demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We extend this approach to allow for heterogeneous mixtures of memristors within the networks; our approach provides an in-depth analysis of network structure. Our networks are evaluated on simulated robotic navigation tasks; results demonstrate that memristive plasticity enables higher performance than constant-weighted connections in both static and dynamic reward scenarios, and that mixtures of memristive elements provide performance advantages when compared to homogeneous memristive networks.

This paper presents a spiking neuro-evolutionary system which implements memristors as neuromodulatory connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and a constructionist approach, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to be evolved for each network. Additionally, each memristor has its own conductance profile, which alters the neuromodulatory behaviour of the memristor and may be altered during the application of the GA. We demonstrate that this approach allows the evolutionary process to discover beneficial memristive behaviours at specific points in the networks. We evaluate our approach against two phenomenological real-world memristive implementations, a theoretical "linear memristor", and a system containing standard connections only. Performance is evaluated on a simulated robotic navigation task.

This paper presents a spiking neuro-evolutionary system which implements memristors as neuromodulatory connections, i.e. whose weights can vary during a trial. The evolutionary design process exploits parameter self-adaptation and a constructionist approach, allowing the number of neurons, connection weights, and inter-neural connectivity pattern to be evolved for each network. We demonstrate that this approach allows the evolution of networks of appropriate complexity to emerge whilst exploiting the memristive properties of the connections to reduce learning time. We evaluate two phenomenological real-world memristive implementations against a theoretical “linear memristor”, and a system containing standard connections only. Our networks are evaluated on a simulated robotic navigation task.

We propose that the behavior of nonlinear media can be controlled automatically through evolutionary learning. By extension, forms of unconventional computing (viz., massively parallel nonlinear computers) can be realized by such an approach. In this initial study a light-sensitive subexcitable Belousov-Zhabotinsky reaction in which a checkerboard image, composed of cells of varying light intensity projected onto the surface of a thin silica gel impregnated with a catalyst and indicator, is controlled using a learning classifier system. Pulses of wave fragments are injected into the checkerboard grid, resulting in rich spatiotemporal behavior, and a learning classifier system is shown to be able to direct the fragments to an arbitrary position through dynamic control of the light intensity within each cell in both simulated and real chemical systems. Similarly, a learning classifier system is shown to be able to control the electrical stimulation of cultured neuronal networks so that they display elementary learning. Results indicate that the learned stimulation protocols identify seemingly fundamental properties of in vitro neuronal networks. Use of another learning scheme presented in the literature confirms that such fundamental behavioral characteristics of a given network must be considered in training experiments.

We aim to search for cellular automata candidate to an automatic system for the demonstration of collision-based universality and that can be able to simulate Turing machines in their space-time dynamics using gliders and glider guns. In this paper, we demonstrate a variety of novel glider guns discovered by genetic algorithms.

We propose that the behaviour of non-linear media can be controlled automatically through coevolutionary systems. By extension, forms of unconventional computing, i.e., massively parallel non-linear computers, can be realised by such an approach. In this study a light-sensitive sub-excitable Belousov-Zhabotinsky reaction in which a checkerboard image comprised of varying light intensity cells projected onto the surface of a catalyst loaded gel is controlled using a heterogeneous cellular automaton. Pulses of wave fragments are injected onto the gel resulting in rich spatio-temporal behaviour and a coevolved cellular automaton is shown able to either increase or decrease the chemical activity through dynamic control of the light intensity within each cell in both simulated and real chemical systems.

This paper presents a novel approach to the programming of automata-based simulation and computation using a machine learning technique. The identification of lattice-based automata for real-world applications is cast as a data mining problem. Our approach to achieving this is to use evolutionary computing and reinforcement learning with performance fed back indicating the predictive accuracy of future behaviour of the given system. The purpose of this work is to develop an approach to identifying automata rules that can achieve good performance using data from a variety of kinds of complex systems.

We discuss the experimental implementation of a chemical controller for a robotic hand. In the present case study, we design a closed system in which a Belousov-Zhabotinsky (BZ) thin-layer chemical reactor linked to a robotic hand via an array of photo-sensors and fingers of the hand stimulates the excitation dynamics in a BZ medium. The principal working circle of chemo-robotic systems is that oxidation wave fronts traveling in the medium are detected by photo-sensors and cause (via a microcontroller) fingers to bend. When a finger bends, it applies a small quantity of colloid silver to the reaction and, thus, evokes an excitation wave. The traveling and interacting waves stimulate further movements of the fingers. In this paper, we offer an experimental set-up, including algorithms and interfacing, for an experimental chemical robotic hand controller, which contributes to the fields of non-classical computation, non-linear physics, and unconventional robotics.