Bio-inspired Systems Research Articles

Decoding complex state space trajectories for neural computing.

In biological neural circuits as well as in bio-inspired information processing systems, trajectories in high-dimensional state-space encode the solutions to computational tasks performed by complex dynamical systems. Due to the high state-space dimensionality and the number of possible encoding trajectories rapidly growing with input signal dimension, decoding these trajectories constitutes a major challenge on its own, in particular, as exponentially growing (space or time) requirements for decoding would render the original computational paradigm inefficient. Here, we suggest an approach to overcome this problem. We propose an efficient decoding scheme for trajectories emerging in spiking neural circuits that exhibit linear scaling with input signal dimensionality. We focus on the dynamics near a sequence of unstable saddle states that naturally emerge in a range of physical systems and provide a novel paradigm for analog computing, for instance, in the form of heteroclinic computing. Identifying simple measures of coordinated activity (synchrony) that are commonly applicable to all trajectories representing the same percept, we design robust readouts whose sizes and time requirements increase only linearly with the system size. These results move the conceptual boundary so far hindering the implementation of heteroclinic computing in hardware and may also catalyze efficient decoding strategies in spiking neural networks in general.

Computational model of grid cells based on back‐propagation neural network

To simulate the firing pattern of biological Grid cells (GCs), an efficient computational model based on back-propagation neural network (BPNN), which allows for the generation of regular hexagonal grids, is described. For each GC, it is associated with the output of a learned BPNN. The firing characteristics of GCs can be controlled by the adjustment factors (AFs) flexibly. During the vehicle's space exploration, GCs can exhibit hexagonal firing patterns with different characteristics according to the settings of the AFs. Simulation results validate the proposed model's accuracy as they are in agreement with GCs' biological experimental electroencephalogram results. Meanwhile, the application of the three-layer BPNN can greatly reduce the computational complexity, which lay a foundation for its application in realistic bio-inspired autonomous navigation systems.

Bio-inspired robotics enabled schemes in blockchain-fog-cloud assisted IoMT environment

Due to emerging developments in sports games, the usage of bio-ankle sensors has been growing progressively. Whereas, Internet of Medical Things (IoMT) is an emerging network that boosts bio-inspired sensors’ performances onto the fog-cloud network. However, a sequence of processes is required to complete the healthcare process for one sportsman. Therefore, workflow-enabled bio-inspired sensors tasks scheduled in IoMT postures different challenges. For instance, cost-efficient scheduling, security, and data validation in distributed hospitals to share their data. In this paper, we devise bio-inspired robotics-enabled schemes in the blockchain-fog-cloud-assisted IoMT environment. The goal is to minimize execution cost and blockchain of applications. Based on the proposed system, the study devises bio-inspired robotics function blockchain task scheduling (BIR-FBTS) schemes, determining the optimal assignment of tasks to the available nodes. The simulation results show that the proposed methods minimized 50% of the service cost and 40% of mined cost in the system compared to all existing bio-inspired healthcare systems.

Highly Flexible Freestanding BaTiO3 -CoFe2 O4 Heteroepitaxial Nanostructure Self-Assembled with Room-Temperature Multiferroicity.

Multiferroics with simultaneous electric and magnetic orderings are highly desirable for sensing, actuation, data storage, and bio-inspired systems, yet developing flexible materials with robust multiferroic properties at room temperature is a long-term challenge. Utilizing water-soluble Sr3 Al2 O6 as a sacrificial layer, the authors have successfully self-assembled a freestanding BaTiO3 -CoFe2 O4 heteroepitaxial nanostructure via pulse laser deposition, and confirmed its epitaxial growth in both out-of-plane and in-plane directions, with highly ordered CoFe2 O4 nanopillars embedded in a single crystalline BaTiO3 matrix free of substrate constraint. The freestanding nanostructure enjoys super flexibility and mechanical integrity, not only capable of spontaneously curving into a roll, but can also be bent with a radius as small as 4.23µm. Moreover, piezoelectricity and ferromagnetism are demonstrated at both microscopic and macroscopic scales, confirming its robust multiferroicity at room temperature. This work establishes an effective route for flexible multiferroic materials, which have the potential for various practical applications.

Electrolyte-gated neuromorphic transistors for brain-like dynamic computing

In recent years, the rapid increase in the data volume to be processed has led to urgent requirements for highly efficient computing paradigms. Brain-like computing that mimics the way the biological brain processes information has attracted growing interest due to extremely high energy efficiency. Particularly, dynamics play an essential role in neural spike information processing. Here, we offer a brief review and perspective in the field of electrolyte-gated neuromorphic transistors for brain-like dynamic computing. We first introduce the biological foundation of dynamic neural functions. Then dynamic synaptic plasticity, dynamic dendritic integration, dynamic neural functions, and bio-inspired somatosensory systems realized based on the electrolyte-gated neuromorphic transistors are presented. At last, conclusions and perspectives are given.

Mathematical modelling, nonlinear dynamics, bifurcation, synchronization and control of mechanisms driven by power supply

The special issue entitled “Mathematical modelling, nonlinear dynamics, bifurcation, synchronization and control of mechanisms driven by power supply,” published by The European Physical Journal: Special Topics (EPJST), aims to bring into discussion works involving nonlinear and dynamic and chaotic behaviors, which are frequently found in mechanical and electromechanical systems, nonlinear circuits, and biologically inspired systems, stimulating the integration of findings on the nonlinear dynamics that can be observed in various systems in the physical sciences and engineering. This special issue presents 27 papers concerning the dynamics and control of flexible structure systems, nonideal systems, energy harvesting problems, and vibration absorbers. In addition, the papers address problems involving chaos control of nonlinear oscillations, phase-locked loops, synchronization of nonlinear oscillators, dynamics of bio-inspired and biostructural systems, nonlinear methods, and nonlinear circuits.

Reviewing Bioinspired Technologies for Future Trends: A Complex Systems Point of View

In this contribution, the main guidelines that, in the opinion of the authors, will address bioinspired technologies in the next future are discussed. The topics are related to some specific subjects. The presented perspectives could be useful to remark how bioinspired technologies can be applied to solve every day problems in a low cost and sustainable way. Moreover, all the considerations reported hallmark the need of changing the paradigm to design innovative bionspired systems. Efficient and alternative bioinspired systems cannot be designed by only looking at macroscopic scale as observed in nature. The efforts of this paper are oriented towards providing a wide perspective on bioinspired technologies as complex systems where nonlinear phenomena are fundamental elements.

Development of a Material Design Space for 4D-Printed Bio-Inspired Hygroscopically Actuated Bilayer Structures with Unequal Effective Layer Widths.

(1) Significance of geometry for bio-inspired hygroscopically actuated bilayer structures is well studied and can be used to fine-tune curvatures in many existent material systems. We developed a material design space to find new material combinations that takes into account unequal effective widths of the layers, as commonly used in fused filament fabrication, and deflections under self-weight. (2) For this purpose, we adapted Timoshenko’s model for the curvature of bilayer strips and used an established hygromorphic 4D-printed bilayer system to validate its ability to predict curvatures in various experiments. (3) The combination of curvature evaluation with simple, linear beam deflection calculations leads to an analytical solution space to study influences of Young’s moduli, swelling strains and densities on deflection under self-weight and curvature under hygroscopic swelling. It shows that the choice of the ratio of Young’s moduli can be crucial for achieving a solution that is stable against production errors. (4) Under the assumption of linear material behavior, the presented development of a material design space allows selection or design of a suited material combination for application-specific, bio-inspired bilayer systems with unequal layer widths.

A Skin-Inspired Artificial Mechanoreceptor for Tactile Enhancement and Integration.

Mechanoreceptors endow humans with the sense of touch by translating the external stimuli into coded spikes, inspiring the rise of artificial mechanoreceptor systems. However, to incorporate slow adaptive receptors-like pressure sensors with artificial neurons remains a challenge. Here we demonstrate an artificial mechanoreceptor by rationally integrating a polypyrrole-based resistive pressure sensor with a volatile NbOx memristor, to mimic the tactile sensation and perception in natural skin, respectively. The artificial mechanoreceptor enables the tactile sensory coding by converting the external mechanical stimuli into strength-modulated electrical spikes. Also, tactile sensation enhancement is achieved by processing the spike frequency characteristics with the pulse coupled neural network. Furthermore, the artificial mechanoreceptor can integrate signals from parallel sensor channels and encode them into unified electrical spikes, resembling the coding of intensity in tactile neural processing. These results provide simple and efficient strategies for constructing future bio-inspired electronic systems.

Advances in Drug Delivery and Theranostics

Advances in Drug Delivery and Theranostics

Bio-inspired life-like motile materials systems: Changing the boundaries between living and technical systems in the Anthropocene

A current trend observed in the Anthropocene is the search for bioinspired solutions. Since it became possible to change the quality of the boundary between living and technical systems, more and more life-like technical products have been developed in recent years. Using five plant-inspired developments of motile technical systems for architecture and soft-robotics, we show how the boundary between living and technical systems undulates, shifts, perforates, blurs, or dissolves with increasing life-likeness. We discuss what causes theses changes in the boundary and how this contributes to the overall aim to achieve higher resilience, robustness, and improved esthetics of plant-inspired products. Inspiration from living systems that make efficient and economic use of materials and energy and are fully recyclable after “service time” may additionally contribute to sustainable material use, one of the major challenges in the Anthropocene.

Flow Characteristics and Novel Applications of Synthetic Jets: A Review

Abstract Synthetic jets, often abbreviated as “SJs” or “Synjets,” are a type of zero net mass flux (ZNMF) jets, which have gained popularity due to their ability to transfer momentum without transferring mass. This property distinguishes SJs from their counterpart traditional continuous jets. Over a stretch of time, SJs have been employed extensively in applications such as active flow control, impingement heat transfer, and miniature electronics cooling. However, researchers are now slowly extending the frontiers of SJ research and making efforts to utilize excellent properties of SJs in novel applications such as autonomous underwater vehicles (AUVs), marine systems, and bio-inspired propulsion systems. This paper strives to identify the gaps in the current research and the areas that have remained unexplored yet. The article begins with a brief discussion of the origin of SJs, i.e., “acoustic streaming.” Resulting segments talk about the formation criteria and the recent works employing various configurations of orifices and cavities. The paper discusses the critical concepts such as jet impact factor, volumetric efficiency, and propulsive efficiency of synthetic jets. It is followed by introduction of novel flow control systems employing SJs such as gurney flaps, bumps, and electro-active synthetic jets. Finally, the most innovative applications of SJs such as AUVs, unmanned air vehicles, and jetting cavities are also brought out. Pulsatile jet flows found in jellyfish, cephalopod, squid, and salp have been discussed in great detail. The exhaustive discussion on future prospects strives to not only provide the reader with the comprehensive insights into SJs, but also to motivate future researchers to overcome the gaps identified in this paper.

Spatio-Temporal Encoding Improves Neuromorphic Tactile Texture Classification

With the increase in interest in the deployment of robots in unstructured environments to work alongside humans, the development of human-like sense of touch for robots becomes important. In this work, we implement a multi-channel neuromorphic tactile system that encodes contact episodes as discrete spike events that mimic the behavior of slow adapting mechanoreceptors. We study the impact of information pooling across artificial mechanoreceptors on classification performance of spatially non-uniform naturalistic textures. We encoded the spatio-temporal activation patterns of mechanoreceptors through gray-level co-occurrence matrix computed from time-varying mean spiking rate-based tactile response volume. We found that this approach greatly improved texture classification in comparison to the use of individual mechanoreceptor response alone. In addition, the performance was also more robust to changes in sliding velocity. The importance of exploiting precise spatial and temporal correlations between sensory channels is evident from the fact that on either removal of precise temporal information or altering of spatial structure of response pattern, a significant performance drop was observed. This study thus demonstrates the superiority of population coding approaches that can exploit the precise spatio-temporal information encoded in activation patterns of mechanoreceptor populations. It, therefore, makes an advance in the direction of development of bio-inspired tactile systems required for realistic touch applications in robotics and prostheses.

Functional Materials and Devices for XR (VR/AR/MR) Applications

Functional Materials and Devices for XR (VR/AR/MR) Applications

Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic.

Collision prevention sets a major research and development obstacle for intelligent robots and vehicles. This paper investigates the robustness of two state-of-the-art neural network models inspired by the locust’s LGMD-1 and LGMD-2 visual pathways as fast and low-energy collision alert systems in critical scenarios. Although both the neural circuits have been studied and modelled intensively, their capability and robustness against real-time critical traffic scenarios where real-physical crashes will happen have never been systematically investigated due to difficulty and high price in replicating risky traffic with many crash occurrences. To close this gap, we apply a recently published robotic platform to test the LGMDs inspired visual systems in physical implementation of critical traffic scenarios at low cost and high flexibility. The proposed visual systems are applied as the only collision sensing modality in each micro-mobile robot to conduct avoidance by abrupt braking. The simulated traffic resembles on-road sections including the intersection and highway scenes wherein the roadmaps are rendered by coloured, artificial pheromones upon a wide LCD screen acting as the ground of an arena. The robots with light sensors at bottom can recognise the lanes and signals, tightly follow paths. The emphasis herein is laid on corroborating the robustness of LGMDs neural systems model in different dynamic robot scenes to timely alert potential crashes. This study well complements previous experimentation on such bio-inspired computations for collision prediction in more critical physical scenarios, and for the first time demonstrates the robustness of LGMDs inspired visual systems in critical traffic towards a reliable collision alert system under constrained computation power. This paper also exhibits a novel, tractable, and affordable robotic approach to evaluate online visual systems in dynamic scenes.

A design strategy of bio-inspired defensive structures with stiffness programmability for reusable impact-resistance protection

As a natural defensive structure, insect cuticle exhibits fascinating impact resistance properties in their body protection due to their spatial hierarchical architecture and the stiffness distribution along their thickness designed by nature. In this study, inspired by the pattern of insect cuticle, we present a novel design strategy of graded elastic ring systems with programmable stiffness gradients to enhance the protective properties. By studying the influence of the elastic modulus, radius and thickness of rings on the impact response of the bio-inspired graded ring systems, the optimal solution of programing the stiffness gradient can be obtained. The mechanical analyses of the single-column ring systems and the bio-inspired ring array architectures with different stiffness gradients under impact loading show that both the distributions of stiffness gradients along the impact direction and the concavity and convexity of the stiffness profiles have a great influence on preventing stress wave propagation and improving impact-tolerance. Compared with convex stiffness gradients, the concave stiffness gradients significantly improve the protective properties as well as reduce the peak value of collision force applied to inner structures. In concave stiffness gradients, the results show that the bio-inspired ring array architectures with exponential (EXP) stiffness gradient have been identified to result in the minimum values of stress, collision force and impulse under impact loading. The strategy proposed in this paper greatly improves the impact resistance property and defensive effect compared with that of the case without protection. The knowledge gained from this work will inspire the designs of novel advanced reusable and lightweight protective structures with improved impact resistance capability by programming reasonable stiffness distributions.

Reprint of: Gentelligent processes in biologically inspired manufacturing

Production systems have to meet quality requirements despite increasing product individuality, varying batch sizes and a scarcity of resources. The transfer of experience-based knowledge in a flexible and self-optimizing production and process planning offers the potential to meet these challenges. Biological systems solve conceptually similar challenges pertaining to the transfer of knowledge, flexibility of individual reactions and adaptation over time. Thus, in the context of digital transformation, mechanisms derived from biology are interpreted and applied to the knowledge domain of production technology. To be able to exploit the potential of bio-inspired production systems, genetic and intelligent properties of technical components and machines were identified and brought together under the concept of “Gentelligence”. Expanding upon this concept with the new idea of process-DNA and biologically inspired optimization algorithms facilitates a more flexible, learning and self-optimizing production, which is shown in three different applications. By using the new concept of gentelligent process planning it is possible to determine machine-specific process parameters in turning processes in order to ensure appropriate roughness within the requirements. Furthermore, the combination of the concept with a material removal simulation allows the determination of the resulting process force in tool grinding for subsequent unknown workpiece geometries. As a result of using the process-DNA, a workpiece-independent knowledge transfer and thus process adaptation for shape error compensation becomes possible. Gentelligent production scheduling enables a process-parallel, holistically optimized machine allocation, and as a result, a significantly reduced lead time.

A Systems Approach of Topology Optimization for Bioinspired Material Structures Design Using Additive Manufacturing

Bioinspired design has been applied in sustainable design (e.g., lightweight structures) to learn from nature and support material structure functionalities. Natural structures usually require modification in practice because they were evolved in natural environmental conditions that can be different from industrial applications. Topology optimization is a method to find the optimal design solution by considering the material external physical environment. Therefore, integrating topology optimization into bioinspired design can benefit sustainable material structure designers in meeting the purpose of using bioinspired concepts to find the optimal solution in the material functional environment. Current research in both sustainable design and materials science, however, has not led to a method to assist material structure designers to design structures with bioinspired concepts and use topology optimization to find the optimal solution. Systems thinking can seamlessly fill this gap and provide a systemic methodology to achieve this goal. The objective of this research is to develop a systems approach that incorporates topology optimization into bioinspired design, and simultaneously takes into consideration additive manufacturing processing conditions to ensure the material structure functionality. The method is demonstrated with three lightweight material structure designs: spiderweb, turtle shell, and maze. Environmental impact assessment and finite element analysis were conducted to evaluate the functionality and emissions of the designs. This research contributes to the sustainable design knowledge by providing an innovative systems thinking-based bioinspired design of material structures. In addition, the research results enhance materials knowledge with an understanding of mechanical properties of three material structures: turtle shell, spiderweb, and maze. This research systemically connects four disciplines, including bioinspired design, manufacturing, systems thinking, and lightweight structure materials.

Biomimetic Nanostructure Platform for Cancer Diagnosis Based on Tumor Biomarkers.

Biomarker discovery and its clinical use have attracted considerable attention since early cancer diagnosis can significantly decrease mortality. Cancer biomarkers include a wide range of biomolecules, such as nucleic acids, proteins, metabolites, sugars, and cytogenetic substances present in human biofluids. Except for free-circulating biomarkers, tumor-extracellular vesicles (tEVs) and circulating tumor cells (CTCs) can serve as biomarkers for the diagnosis and prognosis of various cancers. Considering the potential of tumor biomarkers in clinical settings, several bioinspired detection systems based on nanotechnologies are in the spotlight for detection. However, tremendous challenges remain in detection because of massive contamination, unstable signal-to-noise ratios due to heterogeneity, nonspecific bindings, or a lack of efficient amplification. To date, many approaches are under development to improve the sensitivity and specificity of tumor biomarker isolation and detection. Particularly, the exploration of natural materials in biological frames has encouraged researchers to develop new bioinspired and biomimetic nanostructures, which can mimic the natural processes to facilitate biomarker capture and detection in clinical settings. These platforms have substantial influence in biomedical applications, owing to their capture ability, significant contrast increase, high sensitivity, and specificity. In this review, we first describe the potential of tumor biomarkers in a liquid biopsy and then provide an overview of the progress of biomimetic nanostructure platforms to isolate and detect tumor biomarkers, including in vitro and in vivo studies. Capture efficiency, scale, amplification, sensitivity, and specificity are the criteria that will be further discussed for evaluating the capability of platforms. Bioinspired and biomimetic systems appear to have a bright future to settle obstacles encountered in tumor biomarker detection, thus enhancing effective cancer diagnosis.

DESIGN AND SIMULATION OF NEURON-EQUIVALENTORS ARRAY FOR CREATION OF SELF-LEARNING EQUIVALENT-CONVOLUTIONAL NEURAL STRUCTURES (SLECNS)

In the paper, we consider the urgent need to create highly efficient hardware accelerators for machine learning algorithms, including convolutional and deep neural networks (CNN and DNNS), for associative memory models, clustering, and pattern recognition. We show a brief overview of our related works the advantages of the equivalent models (EM) for describing and designing bio-inspired systems. The capacity of NN on the basis of EM and of its modifications is in several times quantity of neurons. Such neural paradigms are very perspective for processing, clustering, recognition, storing large size, strongly correlated, highly noised images and creating of uncontrolled learning machine. And since the basic operational functional nodes of EM are such vector-matrix or matrix-tensor procedures with continuous-logical operations as: normalized vector operations “equivalence”, “nonequivalence”, and etc. , we consider in this paper new conceptual approaches to the design of full-scale arrays of such neuron-equivalentors (NEs) with extended functionality, including different activation functions. Our approach is based on the use of analog and mixed (with special coding) methods for implementing the required operations, building NEs (with number of synapsis from 8 up to 128 and more) and their base cells, nodes based on photosensitive elements and CMOS current mirrors. Simulation results show that the efficiency of NEs relative to the energy intensity is estimated at a value of not less than 1012 an. op. / sec on W and can be increased. The results confirm the correctness of the concept and the possibility of creating NE and MIMO structures on their basis.