83 results about "Biological neural network" patented technology

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 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.

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 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.

The invention discloses an environment interaction method, system, equipment and product of a humanoid robot. The method comprises the following steps: acquiring multi-modal sensing data of the humanoid robot; performing feature extraction processing on the multi-modal sensing data to obtain global environment features; extracting environment dynamics information from the global environment features based on the time sequence features of the humanoid robot; inputting the environmental dynamics information into a brain-like neural circuit network, and outputting to obtain an interaction control instruction; and analyzing the interaction control instruction into a control signal to drive the humanoid robot to complete an environment interaction action. The working efficiency of the humanoid robot can be improved, and the humanoid robot can be widely applied to the technical field of robots.

The invention provides a social disorder animal model construction method based on a basal lateral amygdaloid nucleus-screen nucleus neural circuit and application thereof, and belongs to the technical field of animal model construction. The method comprises the following steps: injecting a viral vector carrying a photosensitive protein gene or a photosuppressive protein gene into amygdaloid nucleus on the outer side of an animal basal to express neurons of cholecystokinin or neurons of CaMKII alpha; embedding an optical fiber above the screen-shaped core; first light stimulation is applied through an optical fiber to activate nerve projection from amygdaloid nucleus on the outer side of the substrate to the screen nucleus, and a social disorder animal model is obtained; or applying a second photostimulus to inhibit the projection, resulting in a social disorder relief animal model. According to the invention, specific photogenetic regulation and control of the BLACCK +-CLA neural circuit is realized for the first time, social behaviors can be accurately regulated and controlled without interfering with movement and anxiety levels, and a reliable experimental tool is provided for mechanism research and drug screening of social obstacles.

The application discloses an individualized transcranial electrical stimulation system and method based on a multi-scale fusion brain model, which comprises the following modules: a large-scale brain model construction module, which is used for reconstructing an individualized brain function connection matrix by using electroencephalogram (EEG) data, then constructing an individualized large-scale brain model based on an average field model, and equivalent transcranial electrical stimulation to an external current input of the large-scale brain model, and constructing a large-scale brain model under the action of external stimulation; a microscopic neural circuit dynamics model construction module, which is used for fusing single neuron dynamics and synaptic plasticity, adopting a coupled leaky integrate-and-fire model and a spike-timing-dependent plasticity model to establish long-term plasticity between synapses, and then establishing a local neural circuit model through a stimulation target and downstream brain areas; an individualized multi-scale fusion brain model construction module, which is used for replacing corresponding nodes in the large-scale brain model with the local neural circuit model, and constructing an individualized multi-scale fusion brain model; and a stimulation response simulation module, which is used for adjusting transcranial electrical stimulation parameters by using the constructed individualized multi-scale fusion brain model, and obtaining EEG activity and short-term response and long-term effect. The application can more accurately describe the complexity of brain activity by establishing an individualized multi-scale fusion brain model.

This invention provides an all-optical intelligent computing control method and system based on biological neural networks. The method involves determining the position of an obstacle relative to the vehicle and the distance between the obstacle and the vehicle using sensors installed on the vehicle; determining the stimulus position based on the obstacle's position relative to the vehicle and the stimulus intensity based on the distance between the obstacle and the vehicle; applying stimulation to pre-set biological neural samples based on the stimulus position and intensity; constructing a feedback vector based on the responses of the biological neural samples; and inputting the feedback vector into a pre-trained neural network model, which then outputs vehicle commands. This solution first utilizes the application of light stimulation to pre-set biological neural samples, causing neurons to respond by changing their brightness. This approach leverages the realistic reflection of biological neural samples to improve processing efficiency and response accuracy, and combines biological neural networks with artificial intelligence, using biological neural networks to enhance the processing efficiency of artificial intelligence.

Resting-state magnetic resonance imaging (fMRI) technology records brain activity by measuring the global blood oxygen saturation of the subject's brain, providing high spatial resolution data. Although the temporal resolution is lower, in elderly individuals at rest, the introduction of time series data still allows for good observation of the hemodynamic direction of individual pixels, making it one of the important evaluation indicators for intervention and treatment of brain diseases. The neural circuit features constructed based on a single brain region towards the whole brain are also an important evaluation method for fMRI data, namely, the quantification of the physiological interaction of the brain region towards the whole brain in a certain state. This invention constructs a method for quantifying the treatment effect of Alzheimer's disease (AD) based on resting-state magnetic resonance imaging (fMRI) technology. This method mainly addresses: (1) the difficulty in quantifying the rehabilitation effect during the treatment of AD patients; and (2) the quantification of the rehabilitation effect of different treatment modes during the treatment of AD patients.

A transient stability evaluation method based on topology and closed neural network, which utilizes differential equations to establish the nonlinear relationship between the transient power angle stability state and the power dynamic variables, and introduces an adjacency matrix to represent the topology of the power system, solves the long-standing problems of explainable and targeted modeling of complex continuous-time nonlinear dynamics of the power grid. Specifically, the present application combines differential equations, closed continuous-time neural networks and neural circuit strategies, wherein the closed continuous-time neural network greatly simplifies the modeling of continuous-time dynamics and avoids the limitations of numerical solutions of differential equations; and the neural circuit strategy ingeniously combines the adjacency matrix of the power system to simulate the adjacency relationship between buses, so that the model structure is highly sparse, which is conducive to improving the calculation efficiency. At the same time, when the topology of the power system changes, the method can quickly update the model to meet the accuracy requirements under the new topology.

The application is a kind of bionic nerve circuit model based on artificial synapse device. The circuit model is composed of n multi-stage connections of synapse-LIF artificial neuron devices. The composition of the synapse-LIF artificial neuron device includes synapse-like device and cytoplasm-like device. The synapse-like device includes artificial synapse device, voltage dividing resistor and voltage control switch. The cytoplasm-like device includes one comparator. Through different combinations of the four devices, namely excitatory synapse-like device, inhibitory synapse-like device, excitatory cytoplasm-like device and inhibitory cytoplasm-like device, the devices of six circuits, including feedforward excitation circuit, feedforward inhibition circuit, feedback inhibition circuit, lateral inhibition circuit, disinhibition circuit and mutual inhibition circuit, are formed. The application realizes the functions of perception afferent, information integration and motor control in the motor nervous system through the circuit model group, which is helpful for the repair or reconstruction of damaged motor conduction circuit.

The invention relates to a perception and motion control method based on a modularized in-vitro biological neural network, and relates to the technical field of biological neural network calculation, and the method comprises the following steps: 1) coding the image information of an object to be grabbed; 2) encoding the motion information of the multi-joint dexterous hand; 3) constructing a modularized in-vitro biological neural network, training the modularized in-vitro biological neural network by using a repeated stimulation mode, and establishing a neural activity decoding model; and 4) inputting the encoded image and motion information into the trained in-vitro biological neural network, analyzing the activity mode of the biological neural network by using a neural activity decoding model, and mapping the output result of the decoding model into dexterous hand motion control. According to the method, the modularized biological neural network can sense complex and natural environment information, and complex motion control of the dexterous hand is achieved by outputting a control instruction.

A synapse surgical tool for selectively removing or ablating a postsynaptic terminal from a neuron, the method comprising: delivering an expression vector comprising a nucleic acid encoding a fusion protein comprising an N-terminal domain comprising an activated glial receptor binding domain; and a C terminal domain comprising a transmembrane domain of postsynaptic protein; expressing the fusion protein so that the activated glial receptor binding domain is localized to a synaptic cleft of the postsynaptic terminal of the neuron; and contacting the neuron with an activated microglial cell so that the activated microglial binds to the activated glial receptor binding domain and selectively ablates the postsynaptic terminal of the neuron.

Provided herein is a method of targeting a neural circuit of a subject having an impulsivity-related or uncontrollable behavior, the method comprising (a) generating a circuit specific brain connectivity profile using a directionality map of water diffusivity based on MRI of the subject; (b) generating a circuit-specific voxel-wise connectivity matrix with the circuit specific brain connectivity profile to define a region of interest comprising the neural circuit to be targeted; (c) using an algorithm to segment the circuit-specific voxel-wise connectivity matrix to generate a 3D sub-region to identify the neural circuit to be targeted; and (d) targeting the identified neural circuit with a neuromodulation therapy.

The neuron Logic Gate Metal-Oxide-Semiconductor (νLGMOS) circuits to mimic neurons' “integrate-and-fire” behaviors in biological neural network system can be fabricated with industry Complementary Metal-Oxide Semiconductor (CMOS) logic process technology, with which digital computational circuits are fabricated. A processing system having analog νLGMOS circuits, conversion circuitry and digital circuits optimized for power and cost for varieties of applications can be then fabricated with the same CMOS logic process technology for IC chips. Meanwhile analog νLGMOS circuits inspired from biological neural network systems can be simulated, designed, and fabricated for IC chips for the applications of biomedical fields.

The application discloses a kind of nerve circuit, detection circuit, detection system and circuit preparation method, nerve circuit includes first sensor, second sensor, first load resistance, second load resistance, third load resistance, fourth load resistance, first output resistance, second output resistance, first capacitor, second capacitor, first memristor and second memristor, wherein, first sensor and second sensor will be converted into first excitation signal and second excitation signal in turn to the motion state of the object perceived by outside world, first load resistance, second load resistance, third load resistance and fourth load resistance can divide voltage to first excitation signal and second excitation signal, and generate the two response signals of opposite intensity change trend and provide first memristor and second memristor respectively, first memristor and second memristor change or keep the resistance state of itself according to the intensity of received response signal, to output or not output pulse signal.

The invention discloses a tobacco supervision platform access control method based on machine learning, and the method comprises the following steps: collecting access user data, carrying out the alignment through a unified timestamp, and forming an access state data set; constructing a multi-level feature vector, and encoding the multi-level feature vector into a state vector; inputting the state vector into the improved neural circuit strategy model, and outputting an access control decision; carrying out conflict judgment on the access control decision, a static strategy library, a forced closing scale block and a time-space quota account book to form a synthetic access control result; executing an access operation on a gateway layer or a database agent layer of the tobacco supervision platform, and generating an audit log carrying evidence information; and carrying out on-line fine tuning or off-line retraining. According to the invention, the combination of the main decision and the fine-grained control is realized through the improved neural circuit strategy, and the safety, interpretability and adaptivity of the tobacco supervision platform are improved.

The invention discloses a preparation method and application of neural stem cells capable of being used for in-vivo visual neural circuit connection. The method comprises the following steps: constructing a tool virus vector which is driven by a broad-spectrum promoter EF1 alpha, is optimized by a mammal codon and is expressed by a fusion reporter gene of wheat germ lectin and tyrosinase, and transfecting the vector into neural stem cells. TYR catalyzes tyrosine in cells to synthesize melanin through a multi-step reaction, and the in-vivo visualization of a brain region connected with a neural circuit formed by transplanted neural stem cells can be realized by utilizing a nuclide labeling probe of targeted melanin. According to the method, the neural stem cells for in-vivo tracing of neural circuit connection of the neural stem cells are established for the first time, and when and where the neural stem cells are dynamically and visually traced to establish connection with the host neural network in a living body; and a powerful research tool is provided for exploring a specific brain region in which the neural stem cells form loop connection in vivo and an action mechanism of the neural stem cells in neural function repair.