A remote control training system for rat navigation in complicated environment

A remote control system has been developed to deliver stimuli into the rat brain through a wireless micro-stimulator for animal behavior training. The system consists of the following main components： an integrated PC control program, a transmitter and a receiver based on Bluetooth （BT） modules, a stimulator controlled by C8051 microprocessor, as well as an operant chamber and an eight-arm radial maze. The micro-stimulator is featured with its changeable amplitude of pulse output for both constant-voltage and constant-current mode, which provides an easy way to set the proper suitable stimulation intensity for different training. The system has been used in behavior experiments for monitoring and recording bar-pressing in the operant chamber, controlling rat roaming in the eight-arm maze, as well as navigating rats through a 3D obstacle route. The results indicated that the system worked stably and that the stimulation was effective for different types of rat behavior controls. In addition, the results showed that stimulation in the whisker barrel region of rat primary somatosensory cortex （SI） acted like a cue. The animals can be trained to take different desired turns upon the association between the SI cue stimulation and the reward stimulation in the medial forehrain bundle （MFB）.

Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices

Millimeter-scale animals such as Caenorhabditis elegans,Drosophila larvae,zebrafish,and bees serve as powerful model organisms in the fields of neurobiology and neuroethology.Various methods exist for recording large-scale electrophysiological signals from these animals.Existing approaches often lack,however,real-time,uninterrupted investigations due to their rigid constructs,geometric constraints,and mechanical mismatch in integration with soft organisms.The recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability,offering unique capabilities for chronic,stable interrogation and stimulation of millimeter-scale animals and miniature tissue constructs.This review summarizes the most advanced technologies for electrophysiological studies,based on methods of 3-dimensional flexible bioelectronics.A concluding section addresses the challenges of these devices in achieving freestanding,robust,and multifunctional biointerfaces.

A Graded Speed Control Method for Cyborg Rats Based on Electrical Stimulation of the Cuneiform Nucleus

The precise movement speed regulation is a key factor to improve the control effect and efficiency of the cyborg rats.However,the current stimulation techniques cannot realize the graded control of the speed.In this study,we achieved the multi-level speed regulation of cyborg rats in the large open field and treadmill by specifically targeting the Cuneiform Nucleus(CnF)of the Mesencephalic Locomotor Region(MLR).Detailed,we measured the influence of each stimulation parameter on the speed control process which included the real-time speed,accelerated speed,response time,and acceleration period.We concluded that the pulse period and the pulse width were the main determinants influencing the accelerated speed of cyborg rats.Whereas the amplitude of stimulation was found to affect the response time exhibited by the cyborg rats.Our study provides valuable insights into the regulation of rat locomotion speed and highlights the potential for utilizing this approach in various experimental settings.

ODOR DISCRIMINATION BY MITRAL CELLS IN RAT OLFACTORY BULB USING MICROWIRE ARRAY RECORDING

Response features of mitral cells in the olfactory bulb were examined using principal component analysis to determine whether they contain information about odorant stimuli.Using microwire electrode array to record from the olfactory bulb in freely breathing anesthetized rats,we recorded responses of different mitral cells to saturated vapor of anisole(1 M),carvone(1 M),isobutanol(1 M),citral(1 M)and isoamyl actate(1 M).The responses of single mitral cells to the same odorant varied over time.The response profiles showed similarity during certain amount of period,which indicated that the response was not only depended on odor itself but also associated with context.Furthermore,the responses of single mitral cell to different odorants were observed with difference in firing rate.In order to recognize different odorant stimuli,we apply four cells as a sensing group for classification using principal component analysis.Features of each cell’s response describing both temporal and frequency characteristics were selected.The results showed that five different single molecular odorants can be distinguished from each other.These data suggest that action potentials of mitral cells may play a role in odor coding.

Path guided motion synthesis for Drosophila larvae

The deformability and high degree of freedom of mollusks bring challenges in mathematical modeling and synthesis of motions.Traditional analytical and statistical models are limited by either rigid skeleton assumptions or model capacity,and have difficulty in generating realistic and multi-pattern mollusk motions.In this work,we present a large-scale dynamic pose dataset of Drosophila larvae and propose a motion synthesis model named Path2Pose to generate a pose sequence given the initial poses and the subsequent guiding path.The Path2Pose model is further used to synthesize long pose sequences of various motion patterns through a recursive generation method.Evaluation analysis results demonstrate that our novel model synthesizes highly realistic mollusk motions and achieves state-of-the-art performance.Our work proves high performance of deep neural networks for mollusk motion synthesis and the feasibility of long pose sequence synthesis based on the customized body shape and guiding path.

Reshaping the Cortical Connectivity Gradient by Long-Term Cognitive Training During Development

The organization of the brain follows a topologi-cal hierarchy that changes dynamically during development.However,it remains unknown whether and how cognitive training administered over multiple years during develop-ment can modify this hierarchical topology.By measuring the brain and behavior of school children who had carried out abacus-based mental calculation(AMC)training for five years(starting from 7 years to 12 years old)in pre-training and post-training,we revealed the reshaping effect of long-term AMC intervention during development on the brain hierarchical topology.We observed the development-induced emergence of the default network,AMC training-promoted shifting,and regional changes in cortical gradi-ents.Moreover,the training-induced gradient changes were located in visual and somatomotor areas in association with the visuospatial/motor-imagery strategy.We found that gradient-based features can predict the math ability within groups.Our findings provide novel insights into the dynamic nature of network recruitment impacted by long-term cognitive training during development.

Decoding grasp movement from monkey premotor cortex for real-time prosthetic hand control

Brain machine interfaces (BMIs) have demonstrated lots of successful arm-related reach decoding in past decades, which provide a new hope for restoring the lost motor functions for the disabled. On the other hand, the more sophisticated hand grasp movement, which is more fundamental and crucial for daily life, was less referred. Current state of arts has specified some grasp related brain areas and offline decoding results; however, online decoding grasp movement and real-time neuroprosthetic control have not been systematically investigated. In this study, we obtained neural data from the dorsal premotor cortex (PMd) when monkey reaching and grasping one of four differently shaped objects following visual cues. The four grasp gesture types with an additional resting state were classified asynchronously using a fuzzy k-nearest neighbor model, and an artificial hand was controlled online using a shared control strategy. The results showed that most of the neurons in PMd are tuned by reach and grasp movement, us- ing which we get a high average offline decoding accuracy of 97.1%. In the online demonstration, the instantaneous status of monkey grasping could be extracted successfully to control the artificial hand, with an event-wise accuracy of 85.1%. Overall, our results inspect the neural firing along the time course of grasp and for the first time enables asynchronous neural control of a prosthetic hand, which underline a feasible hand neural prosthesis in BMIs.

Automatic Navigation for Rat-Robots with Modeling of the Human Guidance

A bio-robot system refers to an animal equipped with Brain-Computer Interface （BCI）, through which the outer stimulation is delivered directly into the animal＇s brain to control its behaviors. The development ofbio-robots suffers from the dependency on real-time guidance by human operators. Because of its inherent difficulties, there is no feasible method for automatic con- trolling of bio-robots yet. In this paper, we propose a new method to realize the automatic navigation for bio-robots. A General Regression Neural Network （GRNN） is adopted to analyze and model the controlling procedure of human operations. Com- paring to the traditional approaches with explicit controlling rules, our algorithm learns the controlling process and imitates the decision-making of human-beings to steer the rat-robot automatically. In real-time navigation experiments, our method suc- cessfully controls bio-robots to follow given paths automatically and precisely. This work would be significant for future ap- plications of bio-robots and provide a new way to realize hybrid intelligent systems with artificial intelligence and natural biological intelligence combined together.

A P300 based online brain-computer interface system for virtual hand control

Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands.The present work provides a design for a virtual reality (VR) based BCI system that allows human participants to control a virtual hand to make gestures by P300 signals,with a positive peak of potential about 300 ms posterior to the onset of target stimulus.In this virtual environment,the participants can obtain a more immersed experience with the BCI system,such as controlling a virtual hand or walking around in the virtual world.Methods of modeling the virtual hand and analyzing the P300 signals are also described in detail.Template matching and support vector machine were used as the P300 classifier and the experiment results showed that both algorithms perform well in the system.After a short time of practice,most participants could learn to control the virtual hand during the online experiment with greater than 70% accuracy.

A bidirectional brain-computer interface for effective epilepsy control

Brain-computer interfaces(BCIs) can provide direct bidirectional communication between the brain and a machine. Recently, the BCI technique has been used in seizure control. Usually, a closed-loop system based on BCI is set up which delivers a therapic electrical stimulus only in response to seizure onsets. In this way, the side effects of neurostimulation can be greatly reduced. In this paper, a new BCI-based responsive stimulation system is proposed. With an efficient morphology-based seizure detector, seizure events can be identified in the early stages which trigger electrical stimulations to be sent to the cortex of the brain. The proposed system was tested on rats with penicillin-induced epileptic seizures. Online experiments show that 83% of the seizures could be detected successfully with a short average time delay of 3.11 s. With the therapy of the BCI-based seizure control system, most seizures were suppressed within 10 s. Compared with the control group, the average seizure duration was reduced by 30.7%. Therefore, the proposed system can control epileptic seizures effectively and has potential in clinical applications.

Brain＇s neural switch for social dominance in animals

Animals perform social behaviors during their lives to survive and reproduce. As one of the most robust and fundamental social behaviors, social dominance determines individual＇s behavioral displays, priority access to food, mate,territory or other resources, and impacts on its physical and mental health. In human society, socioeconomic status （SES）is a major predictor of physical and mental outcomes, even when the participants have equal access to health care. A Norwegian scientist Thorleif Schjelderup-Ebbe first scientifically described dominance behaviors when he observed

Nanomodification of living organisms by biomimetic mineralization

In nature, a few living organisms such as diatoms, magnetotactic bacteria, and eggs have developed specific mineral structures, which can provide extensive protection or unique functions. However, most organisms do not have such structured materials due to their lack of biomineralization ability. The artificial introduction of biomimetic-constructed nanominerals is challenging but holds great promise. In this overview, we highlight two typical types of mineral- living complex systems. One involves biological surface-induced nanomaterials, which produces artificial living-mineral core-shell structures such as the mineral- encapsulated yeast, cyanobacteria, bacteria and viruses. The other involves internal nanominerals that could endow organisms with unique structures and properties. The applications of these biomimetic generated nanominerals are further discussed, mainly in four potential areas： storage, protection, ＂stealth＂ and delivery. Since biomineralization combines chemical, nano and biological technologies, we suggest that nanobiomimetic mineralization may open up another window for interdisciplinary research. Specifically, this is a novel material-based biological regulation strategy and the integration of living organisms with functional nanomaterials can create ＂super＂ or intelligent nanoscale living complexes for biotechnological practices.

Development of an invasive brain machine interface with a monkey model

Brain-machine interfaces (BMIs) translate neural activities of the brain into specific instructions that can be carried out by external devices. BMIs have the potential to restore or augment motor functions of paralyzed patients suffering from spinal cord damage. The neural activities have been used to predict the 2D or 3D movement trajectory of monkey's arm or hand in many studies. However, there are few studies on decoding the wrist movement from neural activities in center-out paradigm. The present study developed an invasive BMI system with a monkey model using a 10×10-microelectrode array in the primary motor cortex. The monkey was trained to perform a two-dimensional forelimb wrist movement paradigm where neural activities and movement signals were simultaneous recorded. Results showed that neuronal firing rates highly correlated with forelimb wrist movement; > 70% (105/149) neurons exhibited specific firing changes during movement and > 36% (54/149) neurons were used to discriminate directional pairs. The neuronal firing rates were also used to predict the wrist moving directions and continuous trajectories of the forelimb wrist. The four directions could be classified with 96% accuracy using a support vector machine, and the correlation coefficients of trajectory prediction using a general regression neural network were above 0.8 for both horizontal and vertical directions. Results showed that this BMI system could predict monkey wrist movements in high accuracy through the use of neuronal firing information.

Illuminating the Activated Brain: Emerging Activity-Dependent Tools to Capture and Control Functional Neural Circuits

Immediate-early genes(IEGs) have long been used to visualize neural activations induced by sensory and behavioral stimuli. Recent advances in imaging techniques have made it possible to use endogenous IEG signals to visualize and discriminate neural ensembles activated by multiple stimuli, and to map whole-brain-scale neural activation at single-neuron resolution. In addition, a collection of IEG-dependent molecular tools has been developed that can be used to complement the labeling of endogenous IEG genes and, especially, to manipulate activated neural ensembles in order to reveal the circuits and mechanisms underlying different behaviors. Here, we review these techniques and tools in terms of their utility in studying functional neural circuits. In addition, we provide an experimental strategy to measure the signal-to-noise ratio of IEG-dependent molecular tools, for evaluating their suitability for investigating relevant circuits and behaviors.

A Neuronal Pathway that Commands Deceleration in Drosophila Larval Light-Avoidance

When facing a sudden danger or aversive condition while engaged in on-going forward motion,animals transiently slow down and make a turn to escape.The neural mechanisms underlying stimulation-induced deceleration in avoidance behavior are largely unknown.Here, we report that in Drosophila larvae, light-induced deceleration was commanded by a continuous neural pathway that included prothoracicotropic hormone neurons, eclosion hormone neurons, and tyrosine decarboxylase 2 motor neurons(the PET pathway). Inhibiting neurons in the PET pathway led to defects in lightavoidance due to insufficient deceleration and head casting.On the other hand, activation of PET pathway neurons specifically caused immediate deceleration in larval locomotion. Our findings reveal a neural substrate for the emergent deceleration response and provide a new understanding of the relationship between behavioral modules in animal avoidance responses.

Biomineralization: Biomimetic Synthesis of Materials and Biomimetic Regulation of Organisms

What is the most favorite and original chemistry developed in your research group?The discovery of ionic oligomers and their crosslinking following a new concept of inorganic ionic polymerization.How do you get into this specific field?Could you please share some experiences with our readers?I got into the field of biomineralization when I was a graduate student.A challenge in biomineralization community is how we can achieve a moldable construction of inorganic materials(biominerals).

Neural decoding based on probabilistic neural network

Brain-machine interface (BMI) has been developed due to its possibility to cure severe body paralysis. This technology has been used to realize the direct control of prosthetic devices,such as robot arms,computer cursors,and paralyzed muscles. A variety of neural decoding algorithms have been designed to explore relationships between neural activities and movements of the limbs. In this paper,two novel neural decoding methods based on probabilistic neural network (PNN) in rats were introduced,the PNN decoder and the modified PNN (MPNN) decoder. In the ex-periment,rats were trained to obtain water by pressing a lever over a pressure threshold. Microelectrode array was implanted in the motor cortex to record neural activity,and pressure was recorded by a pressure sensor synchronously. After training,the pressure values were estimated from the neural signals by PNN and MPNN decoders. Their per-formances were evaluated by a correlation coefficient (CC) and a mean square error (MSE). The results show that the MPNN decoder,with a CC of 0.8657 and an MSE of 0.2563,outperformed the traditionally-used Wiener filter (WF) and Kalman filter (KF) decoders. It was also observed that the discretization level did not affect the MPNN performance,indicating that the MPNN decoder can handle different tasks in BMI system,including the detection of movement states and estimation of continuous kinematic parameters.

A hybrid brain-computer interface control strategy in a virtual environment

This paper presents a hybrid brain-computer interface (BCI) control strategy,the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment.The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world.The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states,and state switch is achieved in a sequential manner.In the current system,imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously,while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm.The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI.Subjects obtained similar performances in the hybrid and single control tasks,which indicates the hybrid control strategy works well in the virtual environment.

Systemic and single cell level responses to 1 nm size biomaterials demonstrate distinct biological effects revealed by multi-omics atlas

Although ultra-small nanoclusters(USNCs,<2 nm)have immense application capabilities in biomedicine,the investigation on body-wide organ responses towards USNCs is scant.Here,applying a novel strategy of single-cell mass cytometry combined with Nano Genome Atlas of multi-tissues,we systematically evaluate the interactions between the host and calcium phosphate(CaP)USNCs at the organism level.Combining single-cell mass cytometry,and magnetic luminex assay results,we identify dynamic immune responses to CaP USNCs at the single cell resolution.The innate immune is initially activated and followed by adaptive immune activation,as evidenced by dynamic immune cells proportions.Furthermore,using Nano Genome Atlas of multi-tissues,we uncover CaP USNCs induce stronger activation of the immune responses in the cartilage and subchondral bone among the five local tissues while promote metabolic activities in the liver and kidney.Moreover,based on the immunological response profiles,histological evaluation of major organs and local tissue,and a body-wide transcriptomics,we demonstrate that CaP USNCs are not more hazardous than the Food and Drug Administration-approved CaP nanoparticles after 14 days of injection.Our findings provide valuable information on the future clinical applications of USNCs and introduce an innovative strategy to decipher the whole body response to implants.

Expanding from materials to biology inspired by biomineralization

Biomineralization is the intricate process by which living organisms orchestrate the formation of organic–inorganic composites by regulating the nucleation,orientation,growth,and assembly of inorganic minerals.As our comprehension of biomineralization principles deepens,novel strategies for fabricating inorganic materials based on these principles have emerged.Researchers can also harness biomineralization strategies to tackle challenges in both materials'science and biomedical fields,demonstrating a thriving research field.This review begins by introducing the concept of biomineralization and subsequently shifts its focus to a recently discovered chemical concept:inorganic ionic oligomers and their crosslinking.As a novel approach for constructing inorganic materials,the inorganic ionic oligomer-based strategy finds applications in biomimetic regeneration and repair of hard tissues,such as teeth and bones.Aside from innovative methods for material fabrication,biomineralization has emerged as an alternative method for tackling biomedical challenges by integrating materials with biological organisms,facilitating advancements in biomedical fields.Emerging material-biological integrators play a critical role in areas like vaccine improvement,cancer therapy,universal blood transfusion,and arthritis treatment.This review highlights the profound impact of biomineralization in the development and design of highperformance materials that go beyond traditional disciplinary boundaries,potentially promoting breakthroughs in materials science,chemical biology,biomedical,and numerous other domains.