260 results about "Human brain" patented technology

The invention relates to a multi-agent cooperation system based on a large model and a bionic human brain structure, and the method comprises the steps: an interaction layer is used for obtaining a natural language instruction inputted by a user side, and analyzing the natural language instruction into a structured task request; the application layer is used for generating a sub-task list according to the structured task request and determining a target resource demand; the support layer is used for dynamically allocating computing resources according to the sub-task list and target resource requirements and generating a resource allocation result; the cognitive layer is used for generating an environment state and a recommendation strategy according to the external environment data and the context of the natural language instruction; the decision-making layer generates a co-situation response and a task execution instruction according to the environment state, the recommendation strategy and the long-term portrait of the user; the execution layer is used for performing task execution according to the task execution instruction and generating a task execution result; and the model layer is used for determining a target model according to the task request and the model load state, and executing a task to generate a model processing result. According to the invention, the agent collaboration efficiency in a complex scene is improved.

The invention relates to the technical field of artificial intelligence, and discloses a brain-like multi-mode emotion recognition network, a brain-like multi-mode emotion recognition method and an emotion robot, and the brain-like multi-mode emotion recognition method comprises the steps: converting multi-mode emotion information into a pulse sequence; on the basis of the obtained pulse sequence, constructing a continuous pedigree emotion representation space, and realizing continuous representation of an emotional state; a continuous pedigree emotion representation space is utilized to generate pulse codes of mixed emotions, and a complex emotional state is represented; according to the pulse codes of the mixed emotions, establishing an emotional state conversion probability model, and describing a conversion relation between the emotions; based on an emotional state transition probability model, realizing prediction and processing of an emotional gradual change process, and capturing subtle emotional changes; according to the method, the working principle of human brain neurons can be used for reference, fine representation and accurate prediction of the human emotional state are achieved, and meanwhile high calculation efficiency and biological interpretability are achieved.

Belongs to the technical field of biomedical engineering, and discloses a method, a device, equipment, a product and a storage medium for identifying skull defects by constructing a characteristic spectrum through a nuclear magnetic image to solve the problem that a defect area is mistakenly divided into skull areas in existing automatic skull segmentation. Converting the human brain nuclear magnetic image to an anatomical space based on the plurality of anatomical points, and implementing preliminary segmentation to obtain a primary segmentation region of the skull; calculating coordinates of voxel points in the primary segmentation region in an anatomical point coordinate system, establishing a matrix, calculating a plurality of nuclear magnetic gray statistical characteristics of the primary segmentation region, and drawing a characteristic spectrum; and performing multi-condition feature threshold judgment on the feature map to obtain a logic binary map, and extracting a region of which a connected domain is greater than a specified threshold as a skull defect region. According to the method provided by the invention, a plurality of gray features are utilized, the misrecognition of the skull in the prior art is effectively corrected, the method can be further used for judging the filling tissue of the skull defect area, and the skull defect recognition capability can be improved.

The invention discloses a schizophrenia early warning and evaluation system based on human brain multi-region signals, and relates to the technical field of early warning and evaluation, and the system comprises a neural connection analysis unit which is used for recognizing neural connection characteristics of a brain network based on biomarker characteristics, comparing the neural connection characteristics with a database, and determining the neural connection characteristics of the brain network; clustering individuals with similar neural connection features according to a comparison result to generate a risk group; the emotion perception analysis unit is used for acquiring facial emotion states of the risk group in a preset time period and predicting evolution trends of neural connection features in different facial emotion states; and the early warning evaluation unit is used for inputting the evolution trend of the neural connection characteristics into a pre-constructed evaluation model, outputting a risk level evaluation result and formulating an early warning measure based on the risk level evaluation result. According to the method, neural connection features and emotion perception analysis are combined, so that early-stage neural connection abnormity and emotion turning intervals of schizophrenia can be accurately identified and positioned.

The invention provides a voice recognition method and device based on a brain-like model, electronic equipment and a storage medium, and the method comprises the steps: obtaining a whole-brain network topological structure according to a brain function network generated by human brain image data, and carrying out the recognition of a whole-brain network through employing a multi-class neuron model as a node and a synaptic plasticity model as an edge, constructing a multi-brain-region pulse neural network as a brain-like model; constructing a speech recognition framework of the brain-like model; electromagnetic intervention is applied to different brain areas of the brain-like model, optimal electromagnetic intervention parameters are determined by analyzing the voice recognition accuracy of the brain-like model before and after electromagnetic intervention, and brain-like model voice recognition is carried out according to the optimal electromagnetic intervention parameters. According to the invention, the speech recognition performance of the brain-like model can be effectively improved, the biological interpretability and neural information processing capability of the brain-like model are further improved, and the development of brain-like intelligence in the application of a mode recognition task is promoted.

The invention discloses a method for detecting interpersonal nerve synchronization under audio-visual stimulation, which comprises the following steps of: designing a double-person super-scanning experiment normal form, collecting double-person electroencephalogram signals, and constructing a database of visual stimulation and auditory stimulation; preprocessing the data in the database to obtain electroencephalogram signals of four different frequency bands delta, theta, alpha and beta, extracting the electroencephalogram signals of the alpha frequency band, segmenting the electroencephalogram signals, and calculating a correlation ISC value between subjects of each segment; an intra-brain network and an inter-brain network are constructed by constructing a functional connection matrix, and the similarity of the intra-brain network and the global efficiency of the inter-brain network are calculated, so that the cooperation and synchronization degree between neural activities of subjects under visual stimulation and auditory stimulation is effectively evaluated. The invention further discloses a system for detecting interpersonal nerve synchronization under audiovisual stimulation. According to the method, the difference of subjects is reduced by standardizing experimental conditions, and the influence of single sensory stimulation on an intracerebral network activation mode is studied.

The basis of calculating memory capacity of modern computing is bit. Thus, the number of bits is the unit of information quantity of modern communication. The number of neurons (node number) in the neural networks in human brain is not the unit of the memory capacity of the human being. The complexity of neural network is much greater than the bit capacity. Hence, the current AI, which tries to imitate the human brain using computing with the basis on bits, performs inherently different processing of information from the human brain. In addition, computing based on bit number is always facing the limitation of integration. The present disclosure provides a system of information memory without relying on bits using three-dimensional neural networks. By replacing the electrical connection of non-volatile memory cells, which are distributed in a three-dimensional array, the mechanism of the information processing of the human brain can be imitated.

The determination of the probability of gas hydrate formation using artificial neural network (ANN) and multilayer perceptrons (MLPs) models. ANN generally refers to a network of interconnected neurons (also referred to as “nodes”) that model the neurons in a human brain. An MLP refers to a feed-forward network having a specific arrangement of neurons and includes an input layer, one or more hidden layers, and an output layer. Input data such as temperature, pressure, gas mixture composition, and indicators of gas hydrate formation may be obtained and preprocessed for use in training and testing. The ANN and MLPs may be trained using a training set of the input to output a probability of gas hydrate formation. The trained ANN and MLP models may then be used to determine a gas hydrate formation probability for new data associated with a pipeline transporting a gas mixture.

The invention discloses a compound-target binding affinity prediction method based on multi-modal feature fusion, and relates to the technical field of compound activity prediction, and the technical key point is that the method comprises the steps of data acquisition, multi-modal feature characterization, multi-modal feature extraction and fusion, and affinity prediction to predict the compound activity. The problems of long time consumption, high cost, low efficiency and the like of compound activity prediction are solved through assistance of an artificial intelligence algorithm, and information can be processed in parallel in a self-adaptive and self-learning manner by referring to a multi-layered structure of a human brain and a layer-by-layer analysis processing mechanism of neuron information interaction. The intensity information of the interaction between the compound-target pair is provided by combining the affinity, the affinity between the compound and the target is predicted through a deep learning method, and the specific biological activity and key action target of the compound are analyzed.

The invention relates to a human brain extraction method and system for enhancing anatomical structure and surface information perception, electronic equipment and a storage medium, and belongs to the technical field of image processing. The anatomical structure sensing module aims to enhance understanding of global brain tissues, and the surface information sensing module emphasizes a local mode of a brain surface so as to solve challenges brought by low-level inter-class similarity of voxel intensity between brain tissues and non-brain tissues; furthermore, a novel local-global feature enhancement module is employed to enhance the association between local surface patterns and global brain tissue, providing a robust local-global representation that facilitates precise brain extraction. According to the method, the anatomical structure and surface information perception of the human brain is enhanced, so that accurate brain extraction is realized.

The invention discloses a result verification method for a transcranial magnetic stimulator calibration device, and relates to the technical field of medical equipment detection, and the method comprises the following steps: collecting pulse magnetic field parameters outputted by a TMS therapeutic instrument in real time through constructing a multipath parallel magnetic field distribution perception array composed of a plurality of coil type magnetic field sensors; designing a three-dimensional calibration platform to perform multi-dimensional dynamic calibration on the magnetic field distribution sensing array, calibrating the relationship between the induced electromotive force and the magnetic induction intensity of the sensor, and verifying whether the measurement error is less than + / -0.1% or not; and comparing the actually measured magnetic field data with the human brain magnetic field distribution data simulated by the finite element simulation model, analyzing the difference value between the simulation data and the actually measured data, and correcting the system according to the data deviation degree. According to the transcranial magnetic stimulator calibration device and the result verification method, real-time acquisition and high-precision calibration of dynamic parameters are realized through a multi-path synchronous magnetic field sensing array, and the calibration reliability is verified based on finite element simulation and actually measured data comparison.

An integrated EEG-fNIRS neurofeedback system for interacting with participant's brain activity includes EEG electrodes, fNIRS detectors, at least one information receiver, a computation module, and a report generator. The EEG electrodes collect EEG signals. The fNIRS detectors collect fNIRS signals. The at least one information receiver receives and processes the collected EEG and fNIRS signals. The computation module executes an EEG and fNIRS signal processing pipeline with the EEG electrodes, the fNIRS detectors, and the information receiver. The computation module is further configured to: calculate score information based on received EEG and fNIRS signals; select a minimum score from the calculated score information; and discard an alternative score that is not selected as the minimum score, so as to enable the computation module to choose a single representative score for the shared target objective from both EEG and fNIRS signals. The report generator provides a report of the selection.

The invention discloses a human brain specific fluid flow direction identification method comprising the following steps: S1, multi-input network model construction: using a decoder to segment a task, and introducing a boundary detection task; high-density lesions and surrounding edema in the medical image are accurately segmented according to the Vi < T > and an attention mechanism; s2, fluid mechanics model construction and parameter setting, focus classification and risk quantification: optimizing time windows of two times of CT scanning; constructing a three-dimensional geometric model of high-density lesions, peripheral edema and normal brain tissue RO I; constructing initial physical field distribution; setting boundary conditions corresponding to a vascular rupture source and a focus drainage area; and dynamically adjusting the time step length based on the flow velocity and the diffusion coefficient. The invention provides a human brain specific fluid flow direction identification method, which combines image high-dimensional features with a multi-physics field coupling model, and reveals the heterogeneity of human brain specific fluid-blood conversion and contrast agent leakage in absorption and peripheral edema expansion modes.

Artificial neural networks (ANNs) are computing systems inspired by the human brain by learning to perform tasks by considering examples. These ANNs are typically created by connecting several layers of artificial neurons using connections, where each artificial neuron is connected to every other artificial neuron either directly or indirectly to create fully connected layers within the ANN. By substituting ternary matrices for one or more fully connected layers within the ANN, a complexity and resource usage of the ANN may be reduced, while improving the performance of the ANN.

The invention discloses an artificial intelligence model optimization method based on a brain-like artificial neural network, and the method comprises the steps: inputting a task data set into an artificial intelligence model, carrying out the forward propagation, and discriminating a corresponding dormancy neuron or an activation neuron of the artificial intelligence model in a process of executing an artificial intelligence task; a neuron dormancy matrix or activation matrix is generated as a mask matrix of the artificial intelligence model network weight, and a forward propagation path of the artificial intelligence model is reconstructed, so that dormancy neurons do not participate in the training and testing process of the artificial intelligence model network structure; or only enabling the activated neurons to participate in the training and testing process of the artificial intelligence model network structure; and loading the reconstructed artificial intelligence model at the computing device to execute the artificial intelligence task. According to the method, the working mode that only 0.5%-2.5% of neurons of the human brain are activated and most neurons are in a dormant state is effectively simulated, the calculation cost of the model is greatly reduced while the calculation performance of the model is not reduced, and the calculation efficiency of the model is improved.

A computer-implemented method for decoding speech, language and related semantic neural activity includes: collecting neural signals from an array of electrodes implanted in or on a brain; extracting features from the neural signals to detect distributed signatures of linguistic encoding using non-contiguous coverage of the electrode array; and decoding linguistic units, including phonemes and semantic embeddings from the extracted features. The decoding can utilize a custom neural language model for a limited or impaired brain adapted from a generalized neural language model trained on other human brains with intact speech, linguistic and cognitive regions.

The invention belongs to the technical field of human body attention evaluation in a high-temperature environment, and provides a human body attention level evaluation method and system in a high-temperature environment, and the method comprises the steps: dividing an environment temperature range into a plurality of temperature ranges according to a value sequence of temperatures from low to high; performing feature extraction on the near-infrared fNIRS data in the plurality of temperature ranges to obtain feature data in each temperature range; according to the method, range division is carried out on the environment temperature, feature extraction and screening are carried out on the corresponding near-infrared fNIRS data in different environment temperature ranges on the basis, the expression of the near-infrared fNIRS data features in different high-temperature ranges can be reflected, and the near-infrared fNIRS data features in different temperature sections in the high-temperature environment can be reflected. According to the method, the attention performance value is calculated through different attention performance value calculation models, so that the influence caused by obvious change of human brain metabolic rate difference in a high-temperature range is avoided, the coupling relationship between the blood oxygen signal of the fNIRS and the neural activity can be better expressed, and the evaluation accuracy is improved.

The invention discloses a multi-modal feature fusion prediction method and system for early screening of Alzheimer's disease, and belongs to the technical field of brain disease prediction. The method comprises the following steps: acquiring multi-dimensional data containing cognitive test scores, brain image scanning results and biomarker concentration levels from a patient record database, and performing standardization processing to obtain a multi-dimensional data set in a unified format; key feature vectors are extracted through a dimensionality reduction analysis method to capture the covariant relation between cognitive test scores and brain image changes; when the reduction range of the cognitive test score exceeds a preset threshold value and the brain image displays an atrophy sign, a classification model is constructed through an integrated learning method to preliminarily classify abnormal signals; and fusing the biomarker concentration level to obtain an abnormal signal vector. According to the method, the accuracy and efficiency of early screening of brain diseases such as Alzheimer's disease are remarkably improved through accurate evaluation of key parts of the human brain, such as hippocampus.

Provided are Aβ375 related oligomers / aggregates in human brains and anti-Aβ3175 related oligomers / aggregates antibodies thereof. In addition, the using of anti-Aβ3175 related oligomers / aggregates antibodies for the treatment of Alzheimer's disease (AD) is also provided.

The purpose of the present invention is to provide a novel method for increasing the number of newly generated neurons in the adult brain, and to provide a polynucleotide, a vector, and a pharmaceutical composition for use in the method. The present invention provides: a polynucleotide characterized by including (A) the nucleic acid sequence of the Plagl2 gene, (B) an miR-shRNA (microRNA adapted short hairpin RNA) nucleic acid sequence for the Dyrk1a gene, and (C) a promoter sequence operatively connected to the nucleic acid sequences; a vector including the polynucleotide; and a pharmaceutical composition including the polynucleotide and the vector.

Artificial neural networks (ANNs) are computing systems inspired by the human brain by learning to perform tasks by considering examples. These ANNs are typically created by connecting several layers of artificial neurons using connections, where each artificial neuron is connected to every other artificial neuron either directly or indirectly to create fully connected layers within the ANN. By substituting ternary matrices for one or more fully connected layers within the ANN, a complexity and resource usage of the ANN may be reduced, while improving the performance of the ANN.

This invention relates to the field of organoid disease models, and discloses a method for constructing a human brain organoid model of early-onset Alzheimer's disease (AD) and its applications. This invention introduces [a specific technology] into human embryonic stem cell lines through single-base editing and lead editing techniques. PSEN1 ΔE9、 PSEN1 M146V and APP Human embryonic stem cell lines carrying four familial pathogenic gene mutations (K670N and M671L) were constructed and induced to differentiate into Alzheimer's disease (AD) brain organoids. The AD brain organoid model established by this invention exhibited tau phosphorylation pathological phenotypes as early as 20 days and Aβ-related phenotypes as early as 40 days, with increased total Aβ, decreased Aβ42 / Aβ40 ratio, and simultaneous aggravation of Aβ-Tau pathology. This represents a complex neurodegenerative pathological model where multiple mutations synergistically regulate Aβ production and tau phosphorylation, which is highly valuable for understanding and studying early-onset familial AD. This invention provides a human brain organoid model for in vitro AD studies and offers a tool for studying the pathological mechanisms of AD and screening drugs.

1. The name of the product under this design: electroencephalograph. 2. Purpose of this design product: used to monitor the electrophysiological signals of the human brain. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The invention provides an automatic driving decision generation method and device, equipment and a storage medium. The automatic driving decision generation method comprises the steps that multiple pieces of current environment information collected by multiple pieces of sensing equipment at the current moment are acquired; judging whether target historical data matched with the multiple pieces of current environment information exists or not; if the target historical data exists, determining a virtual neurotransmitter concentration corresponding to each sensing device according to a historical weight, an original weight and a reliability coefficient corresponding to each sensing device in the target historical data; and generating an automatic driving decision according to the weights of the plurality of sensing devices. According to the method, the adjustment process of the neurotransmitters in the human brain is simulated, the weights of different sensing devices are adjusted through the virtual neurotransmitters with different concentrations, and the automatic driving decision is generated according to the weights of the multiple sensing devices. By simulating the flexibility of information processing of the human brain in a complex environment, the robustness and response speed of the automatic driving system in a variable environment and under the condition that sensing equipment is more and more diverse can be ensured.

The invention relates to the technical field of biological medicines, and provides a method for reducing acetonitrile residues in a recombinant human brain natriuretic peptide stock solution, which comprises the following steps: sequentially carrying out purification chromatography and ultrafiltration treatment on the human brain natriuretic peptide stock solution; during purification chromatography, a mobile phase comprises a phase A and a phase B; the phase A is a mixed solution of citric acid and sodium citrate; and the phase B is a mixed solution of citric acid, sodium citrate and sodium chloride. A chromatographic system is utilized, the recombinant human brain natriuretic peptide is adsorbed on a chromatographic medium under a specific mobile phase condition, then the chromatographic medium is switched to a proper B-phase buffer solution for elution, and in the process, acetonitrile is effectively separated and removed due to the adsorption characteristic difference between the acetonitrile and the recombinant human brain natriuretic peptide on the chromatographic medium. According to the technical scheme, the problem that the residual amount of acetonitrile in the recombinant human brain natriuretic peptide stock solution in related technologies is large is solved.

The present invention relates to a forebrain assembloid that exhibits a maturity similar to that of the human brain, and a schizophrenia biomarker identified using the forebrain assembloid. More specifically, the present invention relates to: a method for preparing a forebrain assembloid; a forebrain assembloid having a single rosette structure that includes six cortical layers and cavities, the cortical layers including glial cells; a composition for diagnosing schizophrenia, comprising an agent for measuring the expression level of UCN, an agent for measuring the expression level of PTPRF, an agent for measuring the expression level of WNT11, and an agent for measuring the expression level of THBS4; and a kit comprising the composition for diagnosing schizophrenia.

A system may comprise hardware and software configured to perform computing functions that mimic at least one computing function of a human brain, wherein the hardware and software comprises: analog circuitry configured to perform signal processing of neural signals obtained from living brain tissue using at least one sensor, a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor digital circuitry and software configured to process the obtained neural signals to generate a representation of a brain function from the obtained neural signals, and to generate parameters for use by analog and digital circuitry to perform computing functions that mimic at least one computing function of a human brain, and the analog and digital circuitry configured to use the generated parameters to perform computing functions that mimic at least one computing function of a human brain.

The invention relates to a structural network-genetic map biological network model for predicting ischemic stroke and a construction method thereof, and the method comprises the steps: extracting and calculating seven multi-scale morphological features and pairwise Pearson correlation coefficients among the features from T1 weighted imaging data and diffusion tensor imaging data; constructing a 308 * 308 morphological similarity network matrix and a brain network module for identifying ischemic stroke neural dysfunction; 1782 sampling points are extracted from the Airy human brain map, and each sampling point comprises expression data of 10185 genes; the method comprises the following steps: mapping space coordinates of AHBA sampling points to a cortex package of a Desikan-Killiany map, carrying out normalization processing to output 308 * 10185 brain region gene-by-gene expression matrixes, and constructing a structural network-gene map biological network model for predicting ischemic stroke by adopting a partial least square regression method and a bootstrap method. Compared with the prior art, the model determines the specific molecular mechanism related to the phenotypic structure change of ischemic stroke injury, and the stroke occurrence probability is predicted according to the specific molecular mechanism.

The application provides application of circular RNA_hsa_circ_0001681 in preparation of a marker for diagnosing malignant progression prognosis of a brain glioma patient, and the sequence of the circular RNA_hsa_circ_0001681 is shown as SEQ ID NO. 1. The application further provides a vector containing the sequence of the circular RNA_hsa_circ_0001681. The application further provides application of the above-mentioned vector in preparation of a drug for treating a brain glioma patient. The application further provides application of a reagent for detecting the circular RNA_hsa_circ_0001681 in preparation of a kit for diagnosing malignant progression prognosis of a brain glioma patient. Compared with normal brain glioma cells, the circular RNA is significantly decreased in common glioma cell lines, and cell experiments reveal that overexpression of the circular RNA_hsa_circ_0001681 can reduce malignant proliferation of glioma cells.

The invention of a device and system for reduction of tinnitus defects and hearing disorders in the ear and to correct the defects along with real-time feedback with the help of brain output signal recording technology to determine the amount of corrections is related to a device and system to control and reduce hyperacusis, tinnitus and hearing loss and a flexible wearable device that can be placed on a patient's head and also is a portable wearable device that can be connected to a mobile phone and a pair of true wireless earbuds wirelessly. This invention is a system of applying a magnetic field to the auditory cortex of the human brain to reduce hearing defects. This hat is able to simultaneously treat the patient with rTMS and monitor brain activity using fNIRS technology so that it provides accuracy in magnetic stimulation and monitoring of brain reactions.