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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.

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.

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.

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.

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., Comment: 7 pages, 5 figures

Liquid computers use incompressible fluids for computational processes. Here we present experimental laboratory prototypes of liquid computers using colloids composed of zinc oxide (ZnO) nanoparticles and microspheres containing thermal proteins (proteinoids). The choice of proteinoids is based on their distinctive neuron-like electrical behaviour and their similarity to protocells. In addition, ZnO nanoparticles are chosen for their non-trivial electrical properties. Our research demonstrates the successful extraction of 2-, 4- and 8-bit logic functions in ZnO proteinoid colloids. Our analysis shows that each material has a distinct set of logic functions, and that the complexity of the expressions is directly related to each material present in a mixture. These findings provide a basis for the development of future hybris liquid devices capable of general purpose computing.

Colloid-based computing devices offer remarkable fault tolerance and adaptability to varying environmental conditions due to their amorphous structure. An intriguing observation is that a colloidal suspension of ZnO nanoparticles in DMSO exhibits reconfiguration when exposed to electrical stimulation and produces spikes of electrical potential in response. This study presents a novel laboratory prototype of a ZnO colloidal computer, showcasing its capability to implement various Boolean functions featuring two, four, and eight inputs. During our experiments, we input binary strings into the colloid mixture, where a logical ``True" state is represented by an impulse of an electrical potential. In contrast, the absence of the electrical impulse denotes a logical ``False" state. The electrical responses of the colloid mixture are recorded, allowing us to extract truth tables from the recordings. Through this methodological approach, we demonstrate the successful implementation of a wide range of logical functions using colloidal mixtures. We provide detailed distributions of the logical functions discovered and offer speculation on the potential impacts of our findings on future and emerging unconventional computing technologies. This research highlights the exciting possibilities of colloid-based computing and paves the way for further advancements.

Proteinoids are thermal proteins which form microspheres in water in presence of salt. Ensembles of proteinoid microspheres exhibit passive non-linear electrical properties and active neuron-like spiking of electrical potential. We propose that various neuromorphic computing architectures can be prototyped from the proteinoid microspheres. A key feature of a neuromorphic system is a learning. Through the use of optical and resistance measurements, we study mechanisms of learning in ensembles of proteinoid microspheres. We anlyse 16 types of proteinoids, study their intrinsic morphology and electrical properties. We demonstrate that proteinoids can learn, memorize, and habituate, making them a promising candidate for novel computing.

Proteinoids are solidified gels made from poly(amino acids) based polymers that exhibit oscillatory electrical activity. It has been proposed that proteinoids are capable of performing analog computing as their electrical activity can be converted into a series of Boolean gates. The current article focuses on decrypting the morphological and functional complexity of the ensembles of proteinoid microspheres prepared in the laboratory. We identify two different protocols (one with and one without SEM) to prepare and visualize proteinoid microspheres. To quantify the complexity of proteinoid ensembles, we measure nine complexity metrics (to name a few: average degrees $\textrm{Deg}_{av}$, maximum number of independent cycles $u$, average connections per node $\textrm{Conn}_{av}$, resistance $\textrm{res}_{\textrm{eff}}$, percolation threshold $\textrm{perc}_{\textrm{t}}$) which shine light on the morphological, functional complexity of the proteinoids, and the information transmission that happens across the undirected graph abstraction of the proteinoid microspheres ensembles. We identify the complexity metrics that can distinguish two different protocols of preparation and also the most dense, complex, and less power consuming proteinoid network among all tested. With this work, we hope to provide a complexity toolkit for hardware designers of analog computers to design their systems with the right set of complexity ingredients guided one-to-one by the protocol chosen at the first place. On a more fundamental note, this study also sets forth the need to treat gels, microspheres, and fluidic systems as fundamentally information-theoretic in nature, rather than continuum mechanical, a perspective emerging out from recent program by Tao to treat fluids as potentially Turing-complete and thus, programmable., Comment: 11 pages, 8 figures

Proteinoids, also known as thermal proteins, possess a fascinating ability to generate microspheres that exhibit electrical spikes resembling the action potentials of neurons. These spiking microspheres, referred to as protoneurons, hold the potential to assemble into proto-nano-brains. In our study, we investigate the feasibility of utilizing a promising electrochemical technique called differential pulse voltammetry (DPV) to interface with proteinoid nano-brains. We evaluate DPV's suitability by examining critical parameters such as selectivity, sensitivity, and linearity of the electrochemical responses. The research systematically explores the influence of various operational factors, including pulse width, pulse amplitude, scan rate, and scan time. Encouragingly, our findings indicate that DPV exhibits significant potential as an efficient electrochemical interface for proteinoid nano-brains. This technology opens up new avenues for developing artificial neural networks with broad applications across diverse fields of research.

This paper examines the modulation of proteinoid spiking frequency in response to light. Proteinoids are proteins formed through thermal condensation of amino acids and have been found to exhibit spiking behaviour in response to various stimuli. It has been demonstrated that their properties can be modulated by light, with the frequency of spikes changing in response to varying light intensity and wavelength. This paper explores the underlying mechanisms of this phenomenon, including how light affects the proteinoid's structure and its effect on the spiking frequency. We also discuss the potential implications of this modulation for future research and applications. Our research findings suggest that light could be used as a tool to regulate the spiking frequency of proteinoids, opening up a new range of possibilities for unconventional computing research.

Living fungal mycelium networks are proven to have properties of memristors, capacitors and various sensors. To further progress our designs in fungal electronics we need to evaluate how electrical signals can be propagated through mycelium networks. We investigate the ability of mycelium-bound composites to convey electrical signals, thereby enabling the transmission of frequency-modulated information through mycelium networks. Mycelia were found to reliably transfer signals with a recoverable frequency comparable to the input, in the \SIrange{100}{10000} {\hertz} frequency range. Mycelial adaptive responses, such as tissue repair, may result in fragile connections, however. While the mean amplitude of output signals was not reproducible among replicate experiments exposed to the same input frequency, the variance across groups was highly consistent. Our work is supported by NARX modelling through which an approximate transfer function was derived. These findings advance the state of the art of using mycelium-bound composites in analogue electronics and unconventional computing.

Proteinoids, or thermal proteins, are produced by heating amino acids to their melting point and initiation of polymerisation to produce polymeric chains. In aqueous solutions proteinoids swell into hollow microspheres. These microspheres produce endogenous burst of electrical potential spikes and change patterns of their electrical activity in response to illumination. We report results of detailed investigation on the effects of white cold light on the spiking of proteinoids. We study how different types and intensities of light determine proteinoids' spiking amplitude, period, and pattern. The results of this study will be utilised to evaluate proteinoids for their potential as optical sensors and their application in unconventional computing.

Proteinoids, or thermal proteins, are inorganic entities formed by heating amino acids to their melting point and commencing polymerisation to form polymeric chains. Typically, their diameters range from 10 to 100 micron. Some amino acids incorporated into proteinoid chains are more hydrophobic than others, leading proteinoids to cluster together when they are present in aqueous solutions at specific concentrations, allowing them to grow into microspheres. The peculiar structure of proteinoids composed of linked amino acids endows them with unique properties, including action-potential like spiking of electrical potential. These unique properties make ensembles of proteinoid microspheres a promising substrate for designing future artificial brains and unconventional computing devices. To evaluate a potential of proteinoid microspheres for unconventional electronic devices we measure and analyse the data-transfer capacities of proteinoid microspheres. In experimental laboratory conditions we demonstrate that the transfer function of proteinoids microspheres is a nontrivial phenomenon, which might be due to the wide range of proteinoid shapes, sizes, and structures.

A kombucha is a tea and sugar fermented by over sixty kinds of yeasts and bacteria. This symbiotic community produces kombucha mats, which are cellulose-based hydrogels. The kombucha mats can be used as an alternative to animal leather in industry and fashion once they have been dried and cured. Prior to this study, we demonstrated that living kombucha mats display dynamic electrical activity and distinct stimulating responses. For use in organic textiles, cured mats of kombucha are inert. To make kombucha wearables functional, it is necessary to incorporate electrical circuits. We demonstrate that creating electrical conductors on kombucha mats is possible. After repeated bending and stretching, the circuits maintain their functionality. In addition, the abilities and electronic properties of the proposed kombucha, such as being lighter, less expensive, and more flexible than conventional electronic systems, pave the way for their use in a diverse range of applications.

Proteinoids (thermal proteins) are produced by heating amino acids to their melting point and initiation of polymerisation to produce polymeric chains. Amino acid-like molecules, or proteinoids, can condense at high temperatures to create aggregation structures called proteinoid microspheres, which have been reported to exhibit strong electrical oscillations. When the amino acids L-Glutamic acid (L-Glu) and L-Aspartic acid (L-Asp) were combined with electric fields of varying frequencies and intensities, electrical activity resulted. We recorded electrical activity of the proteinoid microspheres' ensembles via a pair of differential electrodes. This is analogous to extracellular recording in physiology or EEG in neuroscience but at micro-level. We discovered that the ensembles produce spikes of electrical potential, an average duration of each spike is 26 min and average amplitude is 1 mV. The spikes are typically grouped in trains of two spikes. The electrical activity of the ensembles can be tuned by external stimulation because ensembles of proteinoid microspheres can generate and propagate electrical activity when exposed to electric fields.

Protocells are a type of synthetic cells, which, if engineered to have properties similar to a natural cell, can have immense applications in synthetic biology and bioengineering communities. Proteinoids are one of the leading contenders for protocells discovered by Sidney H. Fox in 1950s as the protein-like molecules which are made out of amino acids. Proteinoids, if made in a right way, can show electrical excitability patterns on its surface with inflow and outflow of acidic and basic ions giving rise to a oscillatory charge behaviour similar to a neuronal spiking potential. The right protocol for the preparation of proteinoids can be hard to find, given that the literature for proteinoid is widely distributed across different scientific subdisciplines - origin of life, synthetic cell engineering, application of proteinoid NPs etc. This review attempts to enlist most of the relevant protocols published in the literature, each catering to different application, whether motivated by fundamental sciences or basic sciences perspective. The article also suggests the best set of protocol that could be followed by the readers to synthesise proteinoid powder as a potential experimental system for proto-cognitive, cosmetic, biomedical, or synthetic biology applications. An overarching picture of proteinoid as a potential system to study the chemical evolution during the transition from abiotic to prebiotic life, in the history of Earth, is also presented in the end., Comment: 1 figure

Magnetic fluids are excellent candidates for important research fields including energy harvesting, biomedical applications, soft robotics and exploration. However, notwithstanding relevant advancements such as shape reconfigurability, that have been demonstrated, there is no evidence for their computation capability, including the emulation of synaptic functions. Here, we experimentally demonstrate that a Fe3O4 water-based Ferrofluid (FF) can perform electrical analog computing and be programmed using quasi DC signals and read at Radio Frequency (RF) mode. We have observed features in all respects attributable to a memristive behavior, featuring both short and long-term information storage capacity and plasticity. The colloid was capable of classifying digits of a 8x8 pixel dataset using a custom in-memory signal processing scheme, and through Physical Reservoir Computing (PRC) by training a readout layer.

Pavlovian reflex is an essential mechanism of nervous systems of living beings which allows them to learn. Liquid colloid computing devices offer a high degree of fault-tolerance, reconfigurability and plasticity. As a first step towards designing and prototyping colloidal neuromorphic computing systems we decided to evaluate if it is possible to implement Pavlovian reflexes. We equate an increase of a synaptic weight with decreased resistance. In laboratory experiments we demonstrated that it is possible to implement Pavlovian learning using just two volumes of colloid liquid., Comment: arXiv admin note: text overlap with arXiv:2211.00419

Colloids submitted to electrical stimuli exhibit a reconfiguration that could be used to store information and, potentially, compute. We investigated learnign, memorization, and time and stimulation's voltage dependence of conductive network formation in a colloidal suspension of ZnO nanoparticles in DMSO. Relations between critical resistance and stimulation time were reconstructed. The critical voltage, i.e. the stimulation voltage necessary for dropping the resistance, was shown to decrease in response to an increase in stimulation time.