124 results about "Biological neural network" patented technology

The invention discloses a time interference electrical stimulation system for suppressing, controlling and adjusting, which comprises an image acquisition module for acquiring functional magnetic resonance images to acquire functional connection data; the stimulation module is used for respectively generating time interference electrical stimulation waveforms in the plurality of stimulation areas so as to stimulate the corresponding stimulation areas and adjusting stimulation parameters; the evaluation module is electrically connected with the image acquisition module and the stimulation module, acquires cognitive emotion evaluation data, and obtains an emotion and cognitive neural circuit regulation result by combining the emotion evaluation data and the function connection data as an inhibition control capability evaluation index; and the control module is electrically connected with the stimulation module and the evaluation module, and controls the stimulation module to adjust the stimulation parameters based on the emotion and cognitive neural circuit adjustment result. According to the system, effective time interference electrical stimulation is carried out on a plurality of inhibition control intervention brain regions, stimulation parameters are improved by implementing stimulation effect evaluation, and a favorable adjustment means is provided for inhibition control of multi-region and complex network modulation.

The invention relates to the technical field of nerve regulation and control, and provides a closed-loop ultrasonic brain-computer interface regulation and control method for improving post-stroke dysfunction, which comprises the following steps: S1, constructing integrated closed-loop ultrasonic brain-computer interface equipment which comprises a behavior control unit, a signal generator unit, a power amplifier unit and an electrophysiological signal monitoring unit; s2, constructing a mouse model, and embedding an electrode provided for the electrophysiological signal monitoring unit for monitoring; s3, dividing the mouse model into a random stimulation group and a brain-computer interface treatment group, and simultaneously performing closed-loop ultrasonic stimulation for preset time; s4, after closed-loop ultrasonic stimulation for a preset time, performing various behavioral test evaluations including representation of a sensory movement function and representation of an emotional emotion function; and S5, carrying out neural circuit remodeling evaluation, and observing the projection change of the high-order thalamus PO nuclei region of the mouse to the primary cortex. In the aspects of targeting, closed-loop control and integrated design, the method is expected to become a novel and effective post-stroke dysfunction treatment method.

The present invention discloses a memristor pulse neural network system and method, which belongs to the field of artificial intelligence technology. The memristor pulse neural network system provided by the present invention uses a bionic design concept to accurately simulate the synaptic structure of a biological neural network by connecting pairs of memristors of the same level in series; based on the positive and negative Hebbian plasticity rules, an adaptive learning mechanism is implemented to strengthen the computing function of the memristor's storage and computing performance; a cascaded scalable control architecture combining master control and slave control is adopted to give the system a high degree of scalability. According to the needs of specific tasks, users can flexibly adjust the scale of the neural network to achieve unlimited cascade expansion, thereby easily coping with various complex scenarios. In addition, the built-in ADC acquisition module provides a real-time feedback mechanism, which can improve the accuracy of system monitoring, speed up the response speed, and provide valuable data support for system optimization and adjustment.

A tumor classification method, an apparatus, a terminal device and a storage medium. The method comprises: performing feature processing on specific tumor gene data to be classified, so as to obtain a target feature vector; and, by means of a trained biological neural network model, identifying the target feature vector to obtain a classification result of said specific tumor gene data. As the biological neural network model is constructed by introducing biological knowledge, classification results of specific tumor gene data can be predicted on the basis of specific cancer tumor gene attributes combined with hierarchical information in biology, so that the degree of credibility of the classification results is higher, thus improving the accuracy of tumor classification.

The invention relates to the technical field of biological neural networks, in particular to an ammonia escape concentration real-time prediction and control system based on data analysis, which comprises a shared long short-term memory (LSTM) neural network model. The neural network architecture receives as its input a multi-source timing enhancement feature generated via a particular calculation. The neural network not only learns time sequence dependence to predict future physical quantities, but also quantifies the uncertainty of self prediction through an integrated Dropout mechanism. An output layer of the neural network is designed to be of a double-branch structure, and a predicted value and another key system operation state evaluation value are output respectively. The system uses output information of the neural network model to derive technical parameters. Besides, a neural network performance monitoring and self-adaptive calibration unit is integrated, performance degradation of the neural network model can be identified, re-training and re-deployment of the neural network model can be triggered, and the self-adaptive learning ability of the neural network is shown.

The invention relates to a device and a method for distinguishing adaptive behaviors of a mouse musical scale based on discrete frequency acoustic stimulation. The device and the method are composed of an acoustic stimulation generation module, a behavior response acquisition module and a self-adaptive decision control module, an acoustic stimulation sequence of discrete frequency scales is acquired, and a multi-modal parameter coordinated regulation and control system is constructed in combination with an Arduino development environment; on the basis of an embedded signal control circuit, an autonomously designed closed-loop feedback control unit and a multi-channel acoustic signal generation device are integrated, and a musical scale-behavior associated quantitative model based on operational conditioned reflex is established through a two-stage experimental process of'progressive adaptive training-stepped threshold verification '. The method has the function of synchronously detecting auditory cortex frequency coding precision and prefrontal cortex decision ring synaptic plasticity, and a novel experimental tool is provided for quantitative evaluation of neurodegenerative disease central auditory dysfunction and research on a decision-related neural circuit plasticity mechanism.

The invention relates to a closed-loop transcranial acoustic-magnetic stimulation neural circuit regulation and control system and method, and the system comprises an individualized simulation navigation module which is used for carrying out the simulation navigation of ultrasonic stimulation and magnetic stimulation on a brain region of a regulation and control object, and obtaining a stimulation site; the transcranial ultrasonic stimulation module is used for outputting ultrasonic stimulation by adopting the initialized stimulation parameters according to the stimulation sites and carrying out transcranial ultrasonic stimulation on the regulation and control object; the transcranial magnetic stimulation module is used for outputting magnetic stimulation by adopting the initialized stimulation parameters according to the stimulation sites and carrying out transcranial magnetic stimulation on the regulation and control object; and the electroencephalogram collecting, processing and feedback module is used for synchronously collecting and analyzing electroencephalogram signals in the acoustic and magnetic combined stimulation process and optimizing stimulation parameters. According to the method, high spatial resolution of transcranial ultrasonic stimulation, flexibility of transcranial magnetic stimulation and real-time processing of electroencephalogram data are combined, and accurate individualized neural circuit regulation and control are achieved.

The invention discloses a humanoid robot control method based on a neural circuit, and belongs to the technical field of robot control, and the method comprises the steps: S1, constructing a three-layer pulse neural control architecture comprising spinal cord reflex, brainstem balance and cortex decision, and carrying out the weight adaptive distribution and cooperative operation between layers through the dynamic adjustment of neurotransmitter concentration, organic cooperation of prediction and reflection is realized through a three-stage control system; the cerebellar prediction network executes attitude adjustment in advance through a delay compensation algorithm, and compared with pure feedback, the control energy consumption is reduced; the parallel reflex pathway of the spinal cord layer establishes a nerve bypass mechanism, and the emergency response is realized by bypassing high-level processing in an emergency; the multi-layer safety monitoring improves the triggering accuracy of a protection mechanism through error confidence evaluation and contact stability analysis; a weight transfer mechanism of pulse frequency modulation avoids sudden movement change during control mode switching; by means of the design, the stability retention rate of the robot walking on the inclined plane is remarkably improved compared with a traditional method.

Techniques for using a biological and artificial neural network (BANN) system to perform a task. The BANN system comprises a multi-electrode array (MEA); a biological neural network (BNN) comprising neurons arranged on the MEA, a trained statistical model trained using inputs generated using responses of the BNN to training data inputs; and at least one processor. The method comprises using the BANN system to receive an input signal; encode the input signal to generate a stimulation pattern; stimulate the BNN by using the MEA to generate electrical signals in accordance with the stimulation pattern; measure, using the MEA, a response of the BNN responsive to the stimulating; generate, based on the measured response, an input for the ANN; process the input with the trained statistical model to obtain corresponding output; and use the output from the trained statistical model in furtherance of performing the task.

Method for designing a mechatronic component, characterized by the following features: The component is repeatedly simulated by software (10) with certain design parameters (11) that take on variable values, A fixation point (20) of the designer (30) is captured on a screen, while the software (10) displays the component to the designer (30) on the screen. A neural circuit with LTC neurons is trained to infer the resulting fixation point (20) of the designer (30) from the current values ​​of the design parameters (11), and The values ​​of the design parameters (11) are modified by the neural circuit and fed back to the software (10).

The invention relates to the technical field of industrial container detection, and discloses an assembled enamel tank body state detection system, which comprises a three-dimensional sensor group provided with a three-dimensional heterogeneous sensing network, the three-dimensional sensor group is arranged on the surface of an enamel tank body through pasting to form a biological neural network sensing system, and an energy supply unit comprises thermoelectric power generation and wireless charging embedded magnetic attraction power supply. According to inner wall corrosion diagnosis, codes at the inner wall corrosion position are decoded through a code coating protection film, a decoding result is positioned, full-time and full-domain monitoring of the state of the enamel tank is achieved, and the intelligent operation and maintenance level of industrial equipment is remarkably improved.

The present invention provides a neuron logic gate metal oxide semiconductor νLGMOS circuit, which can simulate the "integration and firing" behavior of neurons in a biological neural network system, and can be fabricated together with multiple digital arithmetic circuits by using industrial CMOS logic process technology. The processing system of the present invention includes an analog νLGMOS circuit, a converter circuit, and a digital processing circuit, which can optimize the power and cost of various applications and can be fabricated into an integrated circuit chip by using the same CMOS logic process technology. At the same time, the above-mentioned analog νLGMOS circuit is inspired by the biological neural network system and can be simulated, designed, and fabricated into an integrated circuit chip for applications in the biomedical field.

The invention relates to a touch fluid neuron specific tracing imaging agent, a preparation method and application, and belongs to the technical field of nuclear medicine imaging, the tracing imaging agent is composed of a nerve tracer agent horseradish peroxidase (HRP), zirconium-89 isotope chelated by deferoxamine and a pharmaceutically acceptable carrier, the effective tracking window of the tracer agent is prolonged to 120 hours, and the effective tracking window of the tracer agent is prolonged to 120 hours. The multi-stage synaptic transfer period of the fluid touching neuron can be completely covered; the spatial resolution is improved by 5-8 times compared with that of traditional FDG-PET, and a fluid touching neuron cluster can be accurately presented; the in-vitro serum stability is excellent, the radiochemical purity is still larger than or equal to 80.5% within 120 hours, PET / MRI bimodal imaging can be adapted, drug administration is conducted through lateral ventricle minimally invasive injection, the probe is suitable for various animal models such as mice, rats and non-human primates, in-vivo dynamic tracing of sympathetic fluid neuron trans-synapses can be achieved on the premise that no tissue damage exists, and the probe has a good application prospect. And a key technical tool is provided for neural circuit analysis, neuroscience basic research and preclinical research of neurodegenerative diseases.

The invention discloses an in-vitro biological neural network accurate regulation and control method and system based on a microelectrode array, and the method comprises the steps: S1, culturing and planting neuronal cells on a microelectrode array chip, and forming an in-vitro neural network with spontaneous activity; s2, preprocessing the in-vitro neural network to enable the in-vitro neural network to enter a stable function state capable of being effectively activated; s3, recording an electrophysiological signal, sampling the structure and function connection of the neural network, and dynamically selecting an optimal stimulation site; s4, applying electrical stimulation to the selected stimulation sites according to a preset regulation and control target, collecting neural network response signals in real time, and constructing a closed-loop regulation and control loop; and S5, performing quantitative analysis on the neural network based on the multi-dimensional evaluation index to form a comprehensive evaluation result, and feeding back the comprehensive evaluation result to the step S4 to optimize a subsequent stimulation strategy. According to the method, organic unification of sensing, intelligent decision making, closed-loop intervention and multi-dimensional quantitative evaluation of a neural network structure and a function state can be realized.

The invention discloses a spacecraft attitude stability control method based on a liquid neural network. The method comprises the following steps: S1, establishing a spacecraft attitude dynamic model under multi-source complex disturbance; s2, designing a conventional spacecraft attitude stability controller, and constructing a data set in combination with the attitude dynamics model; s3, training and obtaining a liquid neural network interference observer by adopting a liquid time constant network and a neural circuit strategy architecture; s4, designing a spacecraft attitude stability controller based on a liquid neural network disturbance observer, and proving the stability of a closed-loop system by using a Lyapunov function; s5, verifying the estimation effect of the liquid neural network interference observer and the stability effect of the spacecraft attitude control system through simulation; results show that the method has the advantages that interference of the spacecraft attitude can be effectively estimated, stable control over the spacecraft attitude can be achieved, high real-time performance, low power consumption and high robustness are achieved, and the spacecraft attitude stable control efficiency can be improved.

The purpose of the present invention is to provide a novel method for targeting a neuron population that is a neural circuit element which has more limited functional localization. The purpose of the present invention is also to provide a method for treating a disease or the like by targeting a specific neuron population involved in a disease or the like by said novel method and controlling the neural activity thereof.　The present invention is a vector set used for targeting a specific neuron population in a neural circuit, said vector set being characterized by comprising first to third vectors, wherein: the first vector is an antegrade trans-synaptic virus vector that contains a base sequence which codes for a split-Cre recombinase consisting of a C-terminal-side sequence of a Cre recombinase; the second vector is a retrograde trans-synaptic virus vector that contains a base sequence which codes for a split-Cre recombinase consisting of an N-terminal-side sequence of a Cre recombinase; the third vector is a non-trans-synaptic virus vector that contains an inverted sequence of a gene sequence which codes for a desired protein and that has a loxP sequence and a loxP mutation sequence which face opposite to each other and which are at both ends of the inverted sequence; and a functional Cre recombinase is formed in a cell that has been infected with the first vector and the second vector.

The invention relates to the field of neural network bionics, in particular to a bionic neural network construction method and system, and the method comprises the steps: obtaining a fluctuation threshold value of each neuron cell, and expanding an input dimension for each neuron cell according to the fluctuation threshold value; establishing a corresponding forward network for each neuron cell, and performing network delay dimension expansion on each neuron cell according to the time delay value of each directed edge in the process of establishing each forward network; performing sample recombination on the input sample according to a dimension expansion result; and the weight of the fluctuation threshold input dimension is fixed to train the bionic neural network to be trained. Compared with the prior art, the method has the advantages that the input dimension is expanded for each neuron cell through the fluctuation threshold, and the network delay dimension expansion is carried out on each neuron cell containing the fluctuation threshold; and the trained bionic neural network can better utilize the fluctuation threshold and the network after delay dimension expansion of each neuron cell network to realize quantification and simulation of the biological neural network.

The invention relates to a method for recording and identifying social behaviors of drosophila melanogaster, and belongs to the technical field of neuroscience. The method comprises the steps of building a shooting platform, training an adaptive model through an existing machine learning model, recognizing postures of fruit flies in a video through the adaptive model, and putting a machine learning coordinate file into a patent program to obtain social behavior statistics. Through collaborative design of an adaptive machine learning model and a structured data processing program, full-automatic identification and index quantification of the social behaviors of the drosophila melanogaster are realized, the technical blank that the social behaviors cannot be identified by a drosophila melanogaster mating identification method is filled up, key data support is provided for research of a neural circuit mechanism, gene-behavior association and the like, and the social behaviors of the drosophila melanogaster mating identification method are optimized. Compared with the prior art, the method is obviously improved.

A method and system for differentiating acute and chronic pain based on the functional state of specific neural circuits is proposed. This method utilizes in vivo two-photon / endoscopic calcium imaging and ex vivo brain slice patch-clamp techniques to longitudinally monitor the activity dynamics of neurons in the DRN and ZI brain regions during the acute and chronic phases of a neuropathic pain model. By extracting and analyzing calcium and electrophysiological signal parameter sets, distinguishing criteria are established: if the activity of 5-HTergic neurons in the DRN is specifically enhanced in the acute phase, the indication is acute pain; if the activity of GABAergic neurons in the ZI is specifically enhanced in the chronic phase, the indication is chronic pain. This invention achieves an objective and precise differentiation of the transition from acute to chronic pain at the level of specific neural circuits, providing a new strategy and tool for pain mechanism research and clinical diagnosis and treatment.

This invention provides a method and device for refractive development assessment based on visual development prediction neural circuits, relating to the field of myopia management technology. The method includes: acquiring color retinal optical imaging signals and refractive ground state quantification values ​​of the subject; performing retinal stabilization preprocessing; extracting multi-level morphological features through a reentrant layered retinal choroid encoder; performing spatial feature aggregation gating on the extracted feature maps to obtain feature vectors; concatenating and integrating the feature vectors with the refractive ground state quantification values ​​to obtain fused feature vectors; constructing a fused feature vector sequence and inputting it into a refractive development dynamics internal model inferrer to generate a future multi-time-point refractive state prediction sequence; converting it into an interpretable refractive state numerical sequence; and providing risk warnings based on preset risk thresholds. This invention explicitly embeds physiological mechanisms prior into the model structure and parameter adaptive strategies, thereby maintaining the stability and interpretability of predictions even in a single medical visit scenario.

The invention provides an auxiliary analysis method, device and equipment for a neural circuit injury position and a medium, and relates to the field of medical image processing, and the method comprises the steps: obtaining whole brain sample data, including fMRI data and DTI data of a healthy sample and a tested sample; selecting a seed point based on the fMRI data, establishing whole brain function connection based on the seed point, and extracting a time-varying connection coefficient sequence between the seed point and the region of interest; based on the DTI data, determining a connection fiber between the seed point and the region of interest through probabilistic fiber tracking, obtaining a target liposome representing the connection fiber and a surrounding region thereof, and obtaining a white matter bold signal sequence of the target liposome; and performing difference analysis on the connection coefficient sequence and the white matter bold signal sequence of the healthy sample and the tested sample to obtain the abnormal voxel of the tested sample, and determining the neural circuit injury position according to the coordinate of the abnormal voxel, thereby providing important information for the research of neural activity.

The invention discloses an ADHD child food addiction neural mechanism evaluation method based on fMRI, and the evaluation method mainly comprises the following steps: obtaining the test results of the attention, execution function, intelligence quotient and food addiction of to-be-evaluated children, and dividing the to-be-evaluated children into an ADHD with food addiction group, an ADHD without food addiction group and a health control group according to the results; performing fMRI stimulation normal form execution after grouping, and performing brain function data acquisition; and finally, constructing a mathematical model of neural image data analysis, so that a specific neural circuit of ADHD children food addiction and a mechanism modulated by an emotional state can be revealed non-invasively and objectively, and an important scientific basis and tool are provided for early recognition, pathological mechanism research and targeted intervention strategy development of ADHD co-disease food addiction.

The invention provides a traditional Chinese medicine compound composition with a nerve repairing effect, a cell culture medium, a traditional Chinese medicine-neural stem cell exosome compound and application, and belongs to the technical field of biological medicine. Based on the basic theory of traditional Chinese medicine and the combined theory of traditional Chinese medicine and western medicine, the traditional Chinese medicine compound composition is provided and comprises gastrodia elata exosomes, astragalus membranaceus exosomes and resveratrol, the traditional Chinese medicine compound composition is prepared into a cell culture medium, neural stem cells are cultured through the cell culture medium, and the neural stem cells are obtained. Therefore, the traditional Chinese medicine-neural stem cell exosome compound with an efficient nerve repairing function is prepared. The traditional Chinese medicine-neural stem cell exosome compound achieves the purpose of comprehensively repairing nerve injury by precisely targeting nerve cells, promoting neurogenesis, repairing the function of a neural circuit and reducing the inflammatory effect, and can be used for preparing related medical instruments and drugs for nerve repair.

This invention discloses a method and system for pilot safety risk analysis based on neural computation. The method includes: identifying safety risk factors; establishing a mapping relationship between safety risk factors and unsafe pilot behaviors; establishing a structural model of the cortico-basal ganglia-thalamus neural circuit; establishing AMPA and GABA neural synapse computational models based on the theories of double exponential synapses and α-β synapses, respectively; establishing a computational model of the cortico-basal ganglia-thalamus neural circuit; calculating the thalamic discharge rate under the influence of different safety risk factors, and simulating the regulatory mechanisms of dopamine and acetylcholine during the formation of safety risks. This invention conducts safety analysis from the perspective of pilot neurodynamics, providing key physiological basis and theoretical support for improving risk analysis models and optimizing system design by analyzing the neural regulatory mechanisms of acetylcholine and dopamine during the formation of safety risks.

The present application relates to the technical field of cognitive impairment rehabilitation, in particular to a brain cognitive recovery training scheme analysis system and method for cognitive impairment, comprising five units of basic information acquisition, cognitive test execution, test report generation, norm updating and training scheme analysis, wherein the norm database is modeled in multiple dimensions according to age, educational background and medical history, a regional correction factor is introduced, data freshness is verified by means of blockchain storage, an adaptive sequence is generated for cognitive test, multi-modal data such as correct answer rate, touch screen trajectory and eye movement data are collected, time domain signal separation and denoising are performed, the training scheme analysis unit locates cognitive defects, matches the training module, monitors the brain electrical theta wave and P300 latency through biological feedback, dynamically adjusts the training intensity and frequency, promotes neural circuit reorganization and improves rehabilitation accuracy.

This invention discloses a multi-task control method for a fully soft robot inspired by a fruit fly larva. The method is designed based on the perception-motor-control neural circuit and the motion adaptation neural circuit of a fruit fly larva. Applied to a soft robot inspired by a fruit fly larva, this method enables the robot to possess multiple motion modes such as continuous peristalsis, turning, and obstacle avoidance, while efficiently completing control tasks such as target arrival and navigation. Furthermore, the control method can adaptively optimize the robot's motion rhythm using environmental feedback information and measure the decision uncertainty of the current state based on historical trajectories. In areas requiring precise perception, the soft robot can automatically slow down its motion rhythm to improve localization and detection capabilities, making it suitable for navigation exploration and micro-manipulation in complex environments. This method solves the problems of low efficiency, poor stability, and weak generalization ability of traditional control algorithms driving soft robots, providing an important reference for the design of control methods for fully soft robots in reality.

This associative memory device comprises: a classification vector input data setting unit (1) that sets the same classification vector in a fixed time length as a first input vector; a time-series signal output data setting unit (2) that sets a vector composed of values at each time of a time-series signal in the time length as a first output vector; a first reservoir learning / calculation unit (3) that performs learning by reservoir computing in which a specific classification vector among a plurality of different classification vectors is associated with a specific time-series signal among a plurality of different time-series signals such that the classification vector becomes an input and the time-series signal becomes an output, and calculates a first recursive neural circuit that associates the first input vector with the first output vector; and a first reservoir circuit storage unit (4) that stores the first recursive neural circuit.

The present application relates to a kind of rehabilitation effect evaluation method and system based on electroencephalogram electromyogram signal, belong to electroencephalogram electromyogram signal analysis processing field.Method includes: the multi-channel electroencephalogram signal and multi-channel electromyogram signal of rest period and imagination period are collected and analyzed, obtain the rehabilitation effect evaluation index based on electroencephalogram signal and based on electromyogram signal;The multi-channel electroencephalogram signal and multi-channel electromyogram signal of rest period and imagination period are fused, and brain muscle transmission information effectiveness analysis is carried out, obtain the rehabilitation effect evaluation index based on electroencephalogram electromyogram signal;According to rehabilitation effect evaluation index, the rehabilitation state and effect of patient are evaluated, obtain rehabilitation state and effect evaluation result.The present application method is processed by the multi-channel electroencephalogram electromyogram signal of rest period and imagination period, objective quantification analysis is carried out to the whole closed-loop neural circuit of motor function automatically, so that the evaluation efficiency is higher, and the rehabilitation state and rehabilitation effect evaluated are more comprehensive, accurate.

The invention provides a high-yield rabies virus cell line as well as a preparation method and application thereof. Specifically, the invention provides an engineered cell for producing the recombinant rabies virus, the recombinant rabies virus with effective titer can be produced by using the cell virus through one-time passage, and a neural circuit tracing tool with excellent performance is provided for the application of the defective rabies virus in neural network reverse labeling. The research in the brain science field is promoted.

The application discloses a photosensitive neuron driving electromechanical coupling system, a synchronous control method and application thereof, and electromechanical coupling bionic control technology field, and relates to the technical field of establishing a new type of neural circuit for interacting with external light energy; the neural circuit is used as a driving mechanical arm device of an electrical system to establish an electromechanical coupling circuit system; the response dynamics of the electromechanical coupling system is explored, and it is found that there are periodic bifurcations similar to "arc-shaped curves" and "folding shapes" in a multi-parameter space; capacitor is used as a carrier to study the Hamilton energy balance and complete synchronization of the electromechanical system under the electric field coupling; and resistance is used as a carrier to study the energy evolution of the mechanical arm coupling array when the functional parameters such as light intensity and coupling strength change.