DNA strand displacement (DSD) is an experimental technology based on DNA molecules, which has a wide range of prospects for application in the fields of molecular biology, nanotechnology and biomedicine. In this paper, classical conditioned reflexes and operant conditioning based on DSD are researched and applied to path planning of unmanned aerial vehicles (UAVs). Firstly, classical conditioned reflexes and four kinds of operant conditioning are studied. Secondly, the process of using the integral system principle to train dogs is modeled based on DSD. Thirdly, the combination of classical conditioned reflexes and operant conditioning is investigated. Finally, the application of conditioned reflexes in path planning of UAVs is proposed. All experiments in this paper are validated using Visual DSD software. The combination of DSD technology and conditioned reflexes provides a research idea for intelligent IoT systems.

Temporal memory is an important component of the memory system, which has the ability to remember a series of events in chronological order. Although simple temporal memory and recall processes are implemented by some memristor based neural networks. However, previous work has not considered the interaction between temporal stimuli and the effect of emotions on the memory and recall processes of temporal stimuli. A memristor based temporal memory neural network circuit influenced by emotional arousal and memory interaction is designed in this paper. The circuit is composed of hippocampus module, memory interaction module, memory threshold module, emotional arousal module and temporal recall module. Firstly, when there is correlation between temporal stimuli, the interaction effect function between memories is realized through hippocampus module and memory interaction module. Secondly, the memory and recall processes of emotional arousal to temporal stimuli in different emotional states are realized through the emotional arousal module, hippocampus module and temporal recall module. Finally, the functions of memory fatigue, memory saturation and time-distance effect are realized. The simulation results of PSPICE verify the above functions. This circuit provides more reference for the further development of brain-like system.

With the long-term exposure to the subjectively recognized reward value, the sensitivity gradually weakens, which leads to the generation of incentive sensitization. A memristor-based neural network circuit is developed on the basis of memory reconsolidation theory, motivational sensitization theory, and emotional adaptability mechanisms. Its purpose is to inhibit memory retrieval, thereby extinguishing the incentive sensitization. In addition, this article considers the influence of inhibiting memory retrieval on the incentive sensitization under various emotional states. The designed circuit is primarily composed of a memory module, prefrontal cortex module, emotion module, amygdala module, inhibition module, and nucleus accumbens module. This circuit not only achieves the extinction of incentive sensitization induced by the inhibition of memory retrieval, but also exhibits inhibition effects under different emotional states, which had been verified through personal simulation program with integrated circuit emphasis (PSPICE) simulations. Finally, the proposed circuit provides a reference for modeling biological intelligence and industrial health applications, particularly the development of accurate health monitoring systems.

Most memristor-based nonassociative learning circuits only focus on habituation, while neglecting the research of sensitization and secondary sensitization. Therefore, this article designs a memristor-based sensitization learning circuit, which mainly consists of a habituation module, sensitization module, voltage control module, and secondary sensitization module. After receiving the signal from the voltage module, the habituation module and the sensitization module implement their functions, respectively. Second, this study investigates the influence of voltage stimuli on the formation rates of habituation and sensitization. Moreover, the functions of secondary sensitization and long/short-term sensitization processes have been successfully realized through the synergistic effect of the sensitization module and the secondary sensitization module. Finally, PSPICE software is used to verify the correctness of the design. The outcomes of simulations demonstrate that the circuit is capable of simulating the complex biological mechanisms mentioned earlier. Meanwhile, the circuit also realizes the application simulation of overheating protection for industrial equipment.

Memory homeostasis in the human brain's memory system is interactively influenced by circadian rhythms via neuroplasticity regulation and emotional states. In the current research field, there are still few hardware implementations that can couple rhythm-emotional dynamics similar to those in the brain. A memristor-based brain-inspired memory regulation circuit is proposed in this article. A synaptic weight dynamic balance model is constructed. The circadian rhythm mechanism is simulated under the circuit. In a waking state, short-term memory (STM) pathways should be established first. Memory integration is strengthened during sleep. The state-dependent regulation of neural plasticity is achieved. A 2-D emotional valence–arousal module and a cortisol concentration synaptic decay model are integrated into the circuit to simulate the mechanisms of long-term memory and STM suppression and decreased emotional stability caused by sleep deprivation. A time varying forgetting rate circuit combined with learning intensity monitoring is also designed. The memory regulation characteristics under different emotional states are monitored, such as forgetting compensation in negative high arousal states and memory weakening in low arousal states. Finally, the circuit is applied to intelligent industrial robots. The circuit provides a hardware implementation approach for the memory–emotion coupling mechanism in brain-inspired intelligent systems.

With the rapid development of intelligent information technology, it is significant to construct neural network models that conform to biological characteristics. In this paper, a memoristor model with tunable multistable properties is proposed. By changing the memory parameters, the number of multistable states can be adjusted. Based on the memoristor, an asymmetric memristive FN-HNN neural network (MFNHNN) containing five neurons is constructed. The fundamental dynamical theories, such as equilibrium points, bifurcation diagrams and Lyapunov exponents, are used to reveal the complex dynamic behaviours of MFNHNN. The different dynamic behaviors with coupling intensity control, the tunable coexistence of infinite chaotic attractors and the coexistence of initially controlled chaos and periodic attractors are observed. Furthermore, the equivalent circuit of MFNHNN is implemented. On the basis of random chaotic sequences, an image encryption scheme combining Arnold mapping and diagonal diffusion algorithm is proposed. The findings indicate that the proposed scheme exhibits superior encryption performance, rendering it suitable for application in remote sensing information security.

Biological circuits can not only be applied to ultra sensitive biomedical testing, but also provide new ideas for research in fields such as biomolecular information control, secure communication, and biological computers. In recent years, the synchronization of biological chaotic circuit (BCC) based on DNA strand displacement (DSD) has been widely studied and applied in the field of biological information secure communication. Therefore, this paper proposes a Q-S synchronization scheme of BCC based on DSD, and applies it to biological information secure communication. First, through the research and analysis of DNA molecular reaction dynamics and dual-rail representation, the ideal chemical reaction networks (CRNs) are realized, and the BCC is achieved through the cascade of CRNs. Second, the CRNs of synchronization controller are constructed according to the construction method of Q-S controller, and the combined synchronization between different variables of two BCC with different orders is realized. Finally, CRNs of biological information are designed, and secure communication and decryption of biological information are realized under Q-S synchronization scheme. The effectiveness and robustness of the scheme are proved by numerical simulation in software Visual DSD and MATLAB. Our work provides a new reference for the synchronization of BCC and the secure communication of biological information.

Current biological behavior models only take the external environment information as the basis for decision-making, ignoring the internal emotional state information. A memristor-based cerebellar model articulation controller (CMAC) neural network circuit of artificial fish behavioral decision is designed, and fuzzy emotion is taken into account. The designed circuit is mainly composed of voltage selection modules, fuzzy processing modules, synaptic neuron modules, eigen quantity modules and feedback modules. CMAC neural network is used as learning criteria and the learning subspace voltage with emotional generalization properties outputs to synaptic neural module. By utilizing the nonvolatility and thresholding properties of the memristor, the weights in the neural network are changed to enable the artificial fish to perform primary and secondary learning under specific emotional voltages. The feasibility of the above circuit is verified by PSpice simulation software. The artificial life and biological intelligence behavior are integrated by the memristor-based CMAC neural network circuit. It provides a reliable theory and basis for the emotional behavior of bionic robots.

Nowadays, memcapacitor-based associative memory neural networks are focusing on classical conditioning roles and ignoring operant conditioning roles. In this article, a biomimetic model of operant conditioning neural network based on memcapacitor is designed. The designed circuit includes neuron module, time delay module, hunger output module, experience module, and decision making based on experience module. The novel neural network based on memcapacitors implements learning, forgetting, immediate and delayed reinforcement learning, blocking, generalization, and decision making. In addition, the effects of hunger and satiety on operant conditioning are discussed and implemented using memcapacitors to represent states of deprivation. PSPICE simulation results show that the circuit can be used to simulate real-world conditioned reflexes and complex applications. The proposed circuit can be applied to an intelligent inspection robot for power distribution rooms, enabling autonomous learning and equipment detection.

Current memristive circuits for biological decision-making only consider simple situations and do not take into account how the organisms themselves learn these behaviors. In this article, a memristor-based generalized fuzzy min–max (GFMM) neural network circuit of biology with multiobjective decision is designed, imprinting learning is taken into account. The designed circuit is mainly composed of imprinting learning module, generalization and differentiation learning module, multimodal learning module, behavioral decision module, graded response and feedback module. Behavioral signals are converted into high-intensity and low-intensity learning signals by imprinting learning module, which are output to multimodal learning module for multiple processes. The generalization and differentiation learning module is designed to better analyze the learning signals. Multiple factors are processed by behavior decision module, different behaviors are output based on GFMM neural network. The feasibility of the circuit is verified by PSpice, which provides a reference for biomimetic robots in learning, decision-making, and industrial firefighting.

The Internet of Things has shown great advantages in intelligent healthcare applications. In this article, based on the idea of Internet of Things application in intelligent healthcare, the channel of connection between thyroid gland and DNA strands is established so that the information can be delivered. Tracking and control of the thyroid feedback network (TFN) can be achieved through variations of DNA strand concentration. First, DNA strand displacement chemical reaction networks (DSD CRNs) of the thyroid three-compartment network model [thyrotropin releasing hormones (TRHs), thyrotropin stimulating hormones (TSHs), and thyroid hormones (THs)] are constructed based on DSD. Second, the variations in biochemical indicators of thyroid-related hormones (TRH, TSH, and TH) are analyzed by studying the dynamic behavior of the TFN. The changes in thyroid-related hormones reflect to the symptoms of thyroid-related diseases (TRDs). Third, the CRNs of the active tracking controller are constructed to achieve dynamic equilibrium of biochemical indicators in the TFN. Therefore, the regulation of the TFN is implemented. The Visual DSD and MATLAB software are used to verify the robustness and effectiveness of the TFN. The TFN can better predict biochemical indicators in patients with TRDs.

IoT technology is a key driver for many applications in different fields, such as digital health, smart city, industrial automation, and supply chain. The information security of the Internet of Things is one of the important requirements. Due to the extreme sensitivity to initial values, high randomness and unpredictability of chaotic information, chaotic synchronization is widely used in the field of IoT information encryption. In this article, chaotic systems are used as information security transmission systems for the IoT, and a IoT information encryption scheme for chaotic synchronization under reduced-order observer is proposed. First, through the study of DNA molecular reaction dynamics, the IoT information encryption of chaotic synchronization is extended to the field of DNA strand displacement (DSD). Second, according to dual-rail theory, the chemical reaction networks (CRNs) of IoT information security transmission systems and IoT transmission information are constructed by multiple DSD reaction modules. Finally, CRNs are cascaded to realize the encryption and decryption of IoT information. Through numerical simulation in Visual DSD and MATLAB, the phase diagram, sequence diagram, Lyapunov exponent diagram and bifurcation diagram are given, and their dynamic characteristics are analyzed. Simulation results verify the effectiveness and feasibility of the scheme. Our work will provide a new reference scheme for chaotic synchronization and IoT information encryption using DSD.

With the continuous development of computers, communication technology, and regional medical collaboration services, the security and confidentiality of information are becoming more and more important. In order to prevent the illegal leakage of sensitive patient information, it is of great significance to study medical image encryption. In this article, a flux-controlled hyperbolic memristor model with locally active characteristics is proposed, which has rich nonlinear characteristics. The memristor parameters affect the local activity of the memristor, which is explained by mathematical analysis. Based on the traditional hopfield neural network (HNN), a memristive fully connect neural network (MFNN) containing four neurons is constructed with more complex coupling relationships between individual neurons. The memristor can be used to characterize the effect of external electromagnetic radiation on neurons. The complex dynamical behaviors of MFNN are found by numerical simulations. An equivalent circuit for the neural network is constructed to verify the accuracy of the numerical simulation. In addition, a medical image encryption scheme based on MFNN is proposed. The encryption scheme performs a bit-level permutation of the original image using a chaotic sequence randomly generated by the chaotic system. Fibonacci$Q$-matrix and central diffusion algorithm are used to diffuse the permutation image. Through numerical analysis, the maximum entropy of this encryption algorithm reaches 7.99, and the correlation is close to zero, which proves the resistance of the algorithm to statistical attacks. The algorithm takes only 3.9 s to encrypt an 8-bit medical image of 320 × 320 size on Windows 10 operating system. Experimental results show that the proposed encryption scheme is very secure and has good applications in medical image encryption.

The complete synchronization of two chaotic systems and its secure communication have been considered based on DNA chemical reaction networks (DNA CRNs). There are concerns about the security of the single-level chaotic secure communication system. Thereby a multilevel secure communication scheme is put forward via DNA CRNs in this work. First, a three-variable chaotic system that consists of catalysis, double, fasciation, and annihilation modules is constructed using DNA CRNs. Second, according to the stability principle of nonlinear systems, DNA CRNs are adopted to realize the coupling projection synchronization of three chaotic systems, and the synchronization results are discussed. Finally, sine and cosine signals are designed using DNA CRNs. These signals are added into three systems to realize multilevel encryption and decryption. Furthermore, the interference terms are taken into consideration to test the robustness of the system. The results show that the proposed scheme has high security, and can efficiently encrypt and decrypt biological signals through multilevel transmission in the presence of interference.