5099 results about "Neuron" patented technology

The present invention relates to a magnetism generation coil that provides non-invasive treatment by applying a magnetic field to nerve cells or muscle cells of the body. The magnetism generation coil of the present invention comprises: a coil bundle consisting of a collection of a plurality of unit coil strands of a copper material; and a hollow tube surrounding the coil bundle and formed of a flexible material. The unit coil strands forming the magnetism generation coil of the present invention may be formed of an oxygen-free copper (OFC) coil having a low ratio of oxygen to copper.

The present invention relates to a helmet-type magnetic stimulation device comprising an inner sheath in contact with a skull part when the device is worn. The helmet-type magnetic stimulation device of the present invention is a helmet-type magnetic stimulation device for applying a magnetic field to nerve cells in the skull part or scalp cells so as to provide a non-invasive treatment, and includes a dome part providing a space in which the skull part is accommodated when the helmet-type stimulation device is worn; and an inner sheath inserted into the dome part to adjust the inner diameter of the dome part, wherein the inner sheath includes: a circumferential part disposed in an area which annularly surrounds the skull part in order to adjust the inner diameter of the dome part; and a connection part connected to one end and the other end of the circumferential part so as to connect the one end and the other end while crossing the upper side of an opening formed by the circumferential part.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a mechanical fault diagnosis method and system based on a pulse neural network. The method comprises the following steps: constructing a fault diagnosis network, wherein the fault diagnosis network comprises an improved gating attention coding module and a pulse residual network; the method comprises the following steps: replacing a common convolution layer of a gating attention coding module with a one-dimensional sparse convolution layer, and replacing a two-dimensional batch normalization layer with a one-dimensional batch normalization layer to obtain an improved gating attention coding module; setting a pulse neuron in the fault diagnosis network as a PSN neuron, and embedding a time step shrinkage layer in the pulse residual network; and inputting the mechanical vibration signal of the current sampling period into a fault diagnosis network, and outputting a predicted fault category label of the current sampling period. According to the invention, high-efficiency and high-precision mechanical fault intelligent diagnosis is realized.

The invention relates to the technical field of data protection, and discloses a privacy protection type data joint modeling method based on federated learning, which comprises the following steps: acquiring local data to perform meta-feature extraction, calculating key statistics to characterize data characteristics, collecting meta-features, grouping the meta-features into similar feature clusters through spectral clusters, and carrying out feature clustering on the similar feature clusters; dynamically allocating and calculating resource weights according to the similar characteristic cluster scale and the equipment computing power; distributing a basic privacy budget according to the client type, calculating a local model accuracy rate and an intra-cluster level difference, dynamically adjusting the privacy budget, adding adaptive Gaussian noise based on the privacy budget, and adjusting gradient sensitivity of gradient calculation; verifying gradient compliance through zero knowledge, carrying out safe aggregation on gradients passing verification, optimizing a meta-model through a knowledge distillation loss function, and generating confrontation sample analysis to obtain a leakage risk value to identify knowledge leakage risks; sensitive neurons in the neuron sensitivity positioning element model are analyzed and calculated, directional noise is injected, and initial parameters are adjusted for initialization training.

The invention relates to a neuromorphic computing system and computing method based on a spiking neural network, and the system comprises a neuromorphic processor which is used for constructing a hardware-based spiking neural network through employing a plurality of physical neurons, and carrying out the pulse coding and target recognition of a real-time input image; the central processing unit is realized on the basis of an RISC-V instruction architecture and is used for executing a control instruction required for constructing the pulse neural network; the neuromorphic processor is also used for carrying out online learning on the pulse neural network under the control of the control instruction; and the memory is used for storing program instructions required by the operation of the central processing unit and data transmitted between the central processing unit and the neuromorphic processor. The invention further provides a neuromorphic calculation method. According to the technical scheme of neural morphology calculation, high balance among precision, calculation speed and energy efficiency is achieved, and dynamic and complex task data can be processed accurately and efficiently with low power consumption.

Various aspects of this disclosure relate to a prosthetic cover comprising an array of sensors, which transmit signals to an array of electrodes in a liner that fits over a residual limb of an amputee. Different interactions with the prosthetic cover cause different activation of the electrodes to transmit electrical current through different areas of the residual limb and modulate neurons differently within the residual limb. Various aspects of this disclosure also relate to a prosthetic liner with a substrate that includes electrodes for transcutaneous electrical nerve stimulation and / or electronic muscle stimulation for a residual limb.

The invention provides an end-side multi-modal large model accelerated reasoning method and system, and the method comprises the steps: carrying out the two-stage screening and rearrangement of visual tokens based on the CLS attention and text-to-visual attention in a visual encoder and pre-filling stage, and constructing a sparse attention and sparse key value cache; in a decoding stage, an important neuron set is judged according to activation gating or historical statistics, only a corresponding feedforward network weight is pulled and calculated, missed weights are loaded on demand through asynchronous I / O, and hot neurons are maintained in a high-speed memory to utilize model sparsity, so that video memory / memory occupancy and calculation overhead are remarkably reduced on an end side; throughput and time delay performance are improved. According to the method, the internal memory and computing resources required by reasoning of the multi-modal large language model are reduced from two dimensions by utilizing the endogenous sparsity of the end-side large language model in input and the model, so that a higher reasoning speed is achieved by utilizing fewer resources on the premise of keeping the size of the model unchanged, and the performance of the whole system is improved.

The invention discloses a rural power grid user power load prediction method and system based on a liquid neural network. The method comprises the following steps: firstly, collecting rural power grid user power load historical data including multi-dimensional features such as weather and agricultural modes, and carrying out data preprocessing; then constructing liquid neurons based on biological neuron dynamics, and modeling the state of the liquid neurons through a differential equation; thirdly, constructing a liquid neural network based on liquid neurons, improving the characterization capability of multi-scale time sequence data through multi-level time constant setting and time gating residual connection, and supplementing network initial information in combination with a multi-layer perceptron architecture; completing model training by using the time sequence data set; the actual application performance of the model is tested based on the test data and the actual application scene; and finally, deploying the model to practical application, and carrying out power load prediction on rural power grid users. According to the method, the expression capability of the model for the multi-scale time sequence data is improved, and high-precision rural power grid user power load prediction is realized.

The invention discloses a federated learning backdoor defense method based on pruning and fine tuning in the technical field of artificial intelligence and network security, the method realizes defense through two core mechanisms of dynamic pruning and gradient constraint fine tuning, and the method comprises the following steps: firstly, calculating a sensitivity score based on a neuron activation frequency and a weight outlier degree; dynamically identifying and cutting redundant neurons utilized by a backdoor, and blocking an abnormal activation path; secondly, gradient direction consistency detection and amplitude constraint are introduced in the fine tuning stage, and a malicious client is inhibited from reconstructing a back door through an abnormal gradient; the server continuously purifies model parameters and enhances robustness by cyclically executing pruning, fine tuning and aggregation operations; the method does not need to depend on an extra clean data set, strictly follows a federated learning privacy protection principle, reduces communication overhead through lightweight pruning, maintains main task performance in combination with gradient constraint, is suitable for a federated learning scene in which edge equipment participates, and effectively balances a defense effect and model stability.

The embodiment of the invention provides a self-adaptive retail behavior recognition model optimization method for edge calculation, and the method comprises the steps: building a weight evaluation matrix based on an initial retail behavior recognition model which comprises three behavior recognition tasks: commodity interaction, settlement operation and customer moving line; calculating the importance score of the weight of each layer of neurons by using an L2 norm, generating a multi-dimensional evaluation index in combination with the importance score and the interlayer connection density, and setting a self-adaptive pruning threshold according to the evaluation index; collecting real-time retail scene data at an edge device, extracting commodity operation, customer movement track and shopping behavior time sequence features to construct a sample cache pool, calculating feature importance based on the sample cache pool, and performing selective online updating on a quantitative model; according to the method and the device, the operation efficiency and the recognition accuracy of the system on the edge equipment can be improved by constructing an intelligent model optimization and online updating framework.

The invention relates to the technical field of neural mimicry computing chips, and discloses a distributed processing method and device for a neural mimicry computing chip and a storage medium. The method comprises the following steps: carrying out characteristic decomposition on a calculation task of a neural mimicry calculation chip to obtain a task coding vector; performing working state acquisition on the neuron core to obtain a core state matrix; inputting the task coding vector and the core state matrix into an initial deep reinforcement learning network to generate a resource allocation strategy; performing sequence reconstruction on the resource allocation strategy, and outputting a mapping scheme of a neuron core and a calculation task; a synaptic connection weight is configured according to a mapping scheme, a calculation task is converted into a pulse signal sequence, and the pulse signal sequence is transmitted and executed among neuron cores through an asynchronous event triggering mechanism; and calculating a synaptic weight update gradient to carry out parameter optimization to obtain a target deep reinforcement learning network. The resource utilization rate and the processing efficiency of the neural mimicry computing chip are improved, and the energy consumption is reduced.

An automated system for the detection of pests and diseases in crops using image processing based on deep learning, which includes the following: ◯ an image acquisition unit (101) configured to capture high-resolution images of crops; ◯ a preprocessing unit (102) for improving image quality by applying noise reduction, contrast enhancement and segmentation techniques; ◯ a convolutional neural network (CNN) model(103) trained on a dataset of annotated images of pests and diseases and configured to extract features and classify detected anomalies; ◯ a decision unit (104) that, based on the detected pests or diseases, generates diagnostic reports and provides treatment recommendations; ◯ an integrated mobile (105) and web-based application interface that provides real-time analytics, historical data tracking and recommendations for farmers and agricultural experts.

A method, program and system for training a neural network to perform a machine learning task is provided. The method comprises receiving input data for the neural network. The method further comprises determining values for a plurality of hyperparameters of the neural network. The method further comprises building the neural network according to the hyper parameter values, wherein the neural network comprises a plurality of neurons. Each neuron includes a probability distribution for a plurality of logic operators, such that the neuron includes a corresponding probability for each of the logic operators. The method further comprises training the neural network according to the hyper parameter values and the input data by learning the probability distribution of each neuron. The method further comprises determining a logic operator of the plurality of logic operators for each neuron by selecting a value in the probability distribution.

The present invention relates to a novel association or combination of active ingredients useful in the prevention and repair of neuronal damage, both centrally and peripherally. In greater detail, the present invention relates to an association or combination of i) an extract of Gastrodia elata and ii) at least one ingredient selected from the group comprising or, alternatively, consisting of citicoline, L-acetylcarnitine and, preferably, at least one vitamin of group D, and / or mixtures thereof, and / or at least one vitamin of group B, and / or mixtures thereof. Furthermore, the invention also relates to the pharmaceutical compositions and / or compositions for medical devices (Regulation (EU) 2017 / 745) and / or nutraceutical and / or food compositions comprising that association and to their use in therapy, in particular in the protection of the functionality of the nervous system and in the prevention and / or treatment of central and peripheral neuropathies.

A system and method for real-time time series forecasting using a compound large codeword model with integrated supervisory neurons. The system processes diverse inputs through adaptive codebook generation and codeword allocation. A projection network fuses different data types, creating unified representations for a latent transformer-based machine learning core. The core contains local neural network regions of interconnected operational neurons, monitored by supervisory neurons. These supervisory neurons receive activation data from operational neurons, perform real-time statistical analysis, determine necessary structural modifications, and initiate their implementation during operation. This architecture enables efficient handling of multi-modal data, capturing complex relationships between different input types. The combination of adaptive codebook generation and the supervisory neuron system ensures responsiveness to evolving data patterns and task requirements. This approach provides more accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure to maintain optimal performance.

The invention discloses a pulse neural network training method based on adaptive threshold integer activation neurons, and the method specifically comprises the following steps: inputting a neuromorphic data stream or a static image, generating a pulse sequence through time coding, and taking the pulse sequence as the input of a model; in the backbone network, performing down-sampling and extracting multi-scale features by using an SNN module based on LIF neurons activated by an adaptive threshold integer; the visual tasks are divided into image recognition, image detection and image segmentation, and are respectively accessed to different processing modules for processing and outputting; according to the invention, on the basis of a Meta-SpikeFormer element module, the spiking neural network based on LIF neurons activated by an adaptive threshold integer value is used, so that the memory overhead during training is reduced, the network performance of direct training is improved, the application range of the spiking neural network is expanded, and the network is better applied to image classification, recognition and segmentation tasks.

A computing device and an operating method of the computing device based on a spiking neural network (SNN) are disclosed. The computing device includes a pulse generator configured to generate a pulse corresponding to an input spike signal; an SNN including layers of spiking neurons each generating an output spike signal by applying the pulse to a neuron model; and a loss circuit module configured to calculate a loss value based on a potential value accumulated by the output spike signal generated for each of the layers and backpropagate the loss value to the SNN.

The invention provides a transcranial stimulation magnetic therapy closed-loop control method and system, and the method comprises the steps: determining a multi-modal physiological signal of a user, the multi-modal physiological signal at least comprising an electroencephalogram signal, an autonomic nerve physiological signal and a subjective feedback signal; constructing an individual dynamic baseline spectrum based on user multi-state data collected before treatment; the method comprises the following steps: preprocessing a multi-modal physiological signal collected in a treatment process, and extracting a real-time feature vector; calculating a comprehensive deviation index of the real-time feature vector and the individual dynamic baseline spectrum, and determining a deviation type based on the comprehensive deviation index; the adjustment amount of transcranial magnetic stimulation parameters is dynamically calculated according to the deviation type, the individual response characteristics and the historical treatment data, stimulation operation is conducted according to the adjustment amount of the transcranial magnetic stimulation parameters, and the transcranial magnetic stimulation parameters at least comprise the stimulation frequency, the intensity, the pulse width and the stimulation duration. Parameters of transcranial magnetic stimulation are adaptively changed according to changes of neuron oscillation of a patient in the treatment process.

The invention discloses a memristor neural network circuit for generating a multi-direction multi-double-scroll chaotic attractor, which comprises a first integral channel circuit, a second integral channel circuit, a third integral channel circuit, a first memristor, a second memristor, a third memristor, a first switch, a second switch and a third switch, and is characterized in that the first integral channel circuit comprises three input ends and one output end; the second integral channel circuit comprises three input ends and one output end; the third integral channel circuit comprises two input ends and one output end. The memristive neural network circuit can generate a multi-direction multi-scroll chaotic attractor by controlling the on-off state of the memristive channels connected with the input ends of the integral channel circuits, and the circuit has a discharge characteristic closer to an interactive neuron in a real nervous system; the method can provide new possibility for physically constructing an intelligent neuromorphic system, and has important value in the fields of chaotic secret communication, image encryption, complex signal processing and the like.

The invention discloses brain-targeted stem cell membrane bionic cationic liposome as well as a preparation method and application thereof. The preparation method comprises the following steps: extracting and purifying to obtain a stem cell membrane; preparing a cationic liposome; the stem cell membrane and the cationic liposome are fused to obtain the stem cell membrane bionic cationic liposome, and the relative dosage of the stem cell membrane and the cationic liposome is that the mass ratio of the total protein mass of the stem cell membrane to the total fat mass of the cationic liposome is 1: (2-24). The stem cell membrane bionic cationic liposome constructed by using the purified stem cell membrane has high brain targeting ability, low immune irritation and efficient nucleic acid drug carrying ability, can break through the blood brain barrier, realizes efficient targeting delivery of nucleic acid drugs to cells of the brain including neurons, and has good application prospects. Particularly, the polypeptide can be used as a targeted delivery platform of nucleic acid drugs for targeted therapy of cerebral diseases, especially cerebral arterial thrombosis.

The invention belongs to the technical field of cerebral apoplexy rehabilitation medicine, and relates to evaluation of myoelectric medicine, in particular to an HD-sEMG-based evaluation method and evaluation system for hand dyskinesia of a cerebral apoplexy hemiplegia patient. According to the invention, the high-density surface myoelectricity data of the front arms of the healthy side and the affected side can be respectively collected when the stroke hemiplegia patient executes different hand actions, and the microscopic neuron characteristics are analyzed, so that the degree and progress of dyskinesia of the stroke hemiplegia patient can be accurately evaluated. Through statistical analysis of the number of motion units, the average discharge rate and the discharge mode, changes and rules of neurons in the rehabilitation process are revealed, the difference between the healthy side and the affected side is compared, and the motion function condition of the neurons is evaluated.

The invention discloses a closed-loop noninvasive spinal cord electrical stimulation regulation and control method and system based on motion data, and the system is provided with a flexible electrode array patch which is attached to the skin surface of a to-be-tested human body; the sensing module is used for collecting electromyographic signals or acceleration signals of a user; the signals are processed, and characteristic values related to actions and motions are extracted; the closed-loop feedback module compares the characteristic value with a model set characteristic / threshold value, and judges whether to start stimulation site and stimulation parameter adjustment or not; automatically selecting an optimal electrode unit and a corresponding stimulation parameter based on a judgment result; the dynamic regulation and control module controls the optimal electrode unit to output stimulation current, and regulates the activity of corresponding spinal cord neurons; and continuously performing closed-loop adjustment on the electrode unit and the stimulation parameters according to the motion feedback data. According to the method and system, precise stimulation and closed-loop regulation and control based on the motion response of the patient are achieved, and the problems that stimulation target positioning depends on artificial experience, parameter adjustment is not timely, and individualized and closed-loop feedback capacity is lacked are solved.

The invention is applied to the field of mechanical fault diagnosis signal processing, and particularly provides a pulse neural network mechanical fault diagnosis method based on STFT dimension transformation, and the method comprises the steps: collecting a one-dimensional mechanical vibration signal, carrying out the wavelet decomposition, carrying out the wavelet reconstruction of a low-frequency component and a denoised high-frequency component, and carrying out the wavelet reconstruction of the low-frequency component and the denoised high-frequency component; obtaining a denoised one-dimensional vibration signal; performing short-time Fourier transform, and converting the time-frequency two-dimensional matrix into a time-frequency two-dimensional matrix; inputting the time-frequency two-dimensional matrix into an improved HH threshold neuron model, carrying out Poisson sparse coding on the time-frequency two-dimensional matrix, and only carrying out pulse response on signal significant features; constructing a suprathreshold coding convolutional network with residual connection, inputting a sparse coding matrix, training by adopting an unsupervised learning rule based on STDP, and adaptively adjusting a network synaptic weight; and inputting to a trained above-threshold coding convolutional network, and obtaining pulse emission activity of neurons of an output layer through network forward propagation to determine a fault diagnosis result.

The invention relates to the technical field of image optimization, and discloses an image lossless fusion method and system based on depth feature extraction, and the method comprises the following steps: collecting multi-modal input data from different sensors, carrying out the preprocessing of each modal in the multi-modal input data, and carrying out the preprocessing of each modal in the multi-modal input data; the multi-modal input data comprises a visible light image, an infrared image and a depth map, a multi-layer pulse neural network is used for simulating pulse emission characteristics of biological neurons, time sequence feature extraction is performed on the multi-modal input data, and feature representations of different levels are captured. According to the method, deep feature extraction is carried out through the SNN algorithm to carry out image fusion, the problems of feature mutual exclusion and modal conflict when infrared and visible light images are processed through a traditional method can be effectively solved, a concrete image can be converted into an abstract feature, and then the abstract feature is converted into an end-to-end conversion process of the fused image. And the real-time and fidelity requirements in a dynamic scene are met.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that train and utilize machine learning models to generate perturbation embeddings from phenomic images of cells, including neuronal cell images. Indeed, in one or more implementations, the disclosed systems generate a perturbation embedding using an adapter model or a mixture of experts model. In some implementations, the disclosed systems utilize a mixture of experts model that combines phenomic embeddings from different embedding models to generate a mixture of experts phenomap that contains information from multiple embedding models.

The present disclosure provides methods of differentiating pluripotent stem cells, including induced pluripotent stem cells, into lineage-specific floor plate midbrain progenitor cells, determined dopaminergic neuronal progenitor cells, committed dopaminergic neuronal progenitor cells and / or dopaminergic neuronal cells. Also provided are compositions uses thereof, such as for treating neurodegenerative diseases and conditions, including Parkinson's disease, and articles of manufacture and kits for use thereof.

The invention provides an intracranial neuron electrical stimulation system for treating epilepsy and other brain diseases involving epileptic seizure, which can immediately apply intracranial neuron electrical stimulation to the interseizure epilepsy activity in a personalized manner after detecting the interseizure epilepsy pattern each time. And the stimulation is activated by intracranial electroencephalogram (EEG) detection. The intracranial stimulation for epileptic activities in the epileptic seizure period continuously acts along with time, the forming process of an epileptic network can be blocked, the neuronal super-synchronization risk is reduced, and therefore the control effect on epileptic seizure is improved. The invention also includes the use of nerve adjustment biomarkers, thereby dynamically optimizing the detectability and providing more accurate overall assessment for the patient's treatment progress. Finally, according to the scheme, by detecting the intracranial EEG mode and the high-risk state mode (such as the epileptic state and the sudden accidental death risk in epilepsy) in the attack period, an instant alarm is given to the patient / nursing personnel, and it is ensured that safe treatment measures are taken for the patient in time.

The invention discloses a biological neural network bionic method and system and related equipment. The method comprises the steps of digital unit design simulating biological neuron characteristics, network dynamic adjustment based on connection plasticity, network structure generation of task adaptability, optimal allocation of hardware resources, real-time optimization of multi-dimensional performance, enhancement of safety and robustness, cross-platform hardware adaptation and the like. The problems of poor adaptability, insufficient robustness, low resource utilization rate and the like when a current neural network model processes complex tasks are solved. The brain-like computing system is efficient, safe and flexible, can be widely applied to the fields of intelligent sensing, autonomous decision making, man-machine interaction and the like, and provides important support for development of a new-generation artificial intelligence technology. Meanwhile, the invention further provides an Internet of Things device integrated with the bionic neural network and a chip implementation scheme, and the Internet of Things device and the chip implementation scheme have the advantages of being high in energy efficiency ratio, high in adaptability, wide in application range and the like.

The invention provides an agricultural product supply chain traceability data integrity verification method based on machine learning, and belongs to the technical field of agricultural product supply chains. An agricultural product supply chain data acquisition system is established, layering is carried out according to timeliness, a data classification processing framework is constructed, and original data is classified according to complexity; establishing an agricultural product damage prediction model to identify a vulnerable link, and designing a double-layer game optimization model consisting of an upper layer model taking data integrity maximization as a target and a lower layer model taking calculation efficiency optimization as a target, a liquid neural network is adopted to construct a data fusion verification model, liquid neuron state parameters are dynamically adjusted according to the number of supply chain nodes, data timeliness and other parameters, the parameter range of each evaluation matrix is determined through a small-scale verification experiment, and finally integrity verification is executed to calculate and output a comprehensive score result. The technical problems that the agricultural product supply chain traceability data integrity verification accuracy is not high and the calculation efficiency is low are solved.

The disclosure provides technologies that permit detection and / or characterization of brain biomarkers (e.g., disease-associated biomarkers) in non-CNS samples, including by liquid biopsy (e.g., blood, serum, etc.). Among other things, the present disclosure demonstrates that ultrasound opening of the blood brain barrier can achieve detectable increases in brain-derived materials (e.g., brain-derived proteins, neuron-derived extracellular vesicles, and / or cell-free DNA) in readily-sampled systemic liquids.