255 results about "Neural system" patented technology

A computer system for adaptive operation of deep learning networks through hierarchical supervision, meta-level pattern tracking, cross-network signal coordination, and selective activation prioritization. The system operates a layered neural network monitored by a hierarchical supervisory system that collects activation data, identifies operational patterns, implements architectural modifications, detects network sparsity, coordinates pruning decisions, and manages resource redistribution. A meta-supervisory system tracks supervisory behavior, stores successful pruning and modification patterns, and extracts generalizable optimization principles. The system manages signal transmission pathways that enable direct communication between non-adjacent network regions, with signal modification and temporal coordination. A greedy neural system selectively processes activation patterns based on utility metrics and includes a competitive bidding manager to allocate limited computational resources to high-value signals. This architecture enables real-time optimization of network behavior and resource usage while maintaining operational stability and responsiveness across diverse applications.

Disclosed are system, method, and computer program product embodiments for determining a digital marker. An embodiment operates by determining one or more oculometric parameters associated with an eye of a user using a video stream. The video stream comprises at least one image of a face of the user. The embodiment then determines a pupillary response function of brightness or a gaze response function by performing a deconvolution process based on at least the one or more oculometric parameters. The pupillary response function of brightness or the gaze response function is indicative of a response of the eye of the user to an external factor. The embodiment determines one or more digital markers of the user based on the pupillary response function of brightness or the gaze response function. The one or more digital markers are indicative of a neurological condition or a mental health condition of the user.

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 a disease early warning method and device, and relates to the technical field of disease early warning, the method utilizes rich target disease related knowledge in clinical examination result data, and through a cross-modal knowledge distillation method, knowledge learned by a multi-modal teacher model is transferred to a student model only accessing a man-machine interaction mode, so that the disease early warning is realized. The disease early warning capability of the man-machine interaction student model is improved. Knowledge highly related to the target disease in the clinical examination result data can be migrated to the man-machine interaction data, so that a more accurate early warning result can be obtained under the condition that only the man-machine interaction data is applicable. A low-cost and low-burden nervous system disease early warning mode based on human-computer interaction data is realized, and early recognition and long-term monitoring can be performed on large-scale crowds with onset risks.

An auditory neural interface device for sound perception by an individual that may be used as a hearing aid. The auditory neural interface device includes a receiver configured to receive sound signals, a processor operably connected to the receiver and configured to encode a received sound signal as a multi-channel neurostimulation signal, and a neurostimulation device operably connected to the processor and configured to apply the multi-channel neurostimulation signal to a neurostimulation electrode of the individual. The neurostimulation signal is configured to directly stimulate afferent sensory neurons of the central nervous system of the individual and thereby to elicit, for each channel of the neurostimulation signal, one or more non-auditory, preferably somatosensory, perceptions in a cortex area of the individual. Each channel of the neurostimulation signal is associated with a different non-auditory perception.

An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with at least one diffusion magnetic resonance imaging dMRI image of a patient's brain. The processing unit is configured to: 1) determine an estimate of an orientation of neurons at each voxel in the dMRI image; 2) determine a plurality of fiber tracts in the at least one dMRI image; 3) select a plurality of voxels along at least one fiber tract of the plurality of fiber tracts; and 4) determine a neurological disease classification.

The invention discloses an intelligent identification method and system for analyzing abnormal node electricity prices based on multi-dimensional backtracking. The method comprises the steps of collecting node real-time electricity prices, node synchronous real-time historical electricity prices and multi-dimensional data indexes influencing the node electricity prices in real time; the node electricity price and the multi-dimensional data are preprocessed, and a multi-dimensional real-time electricity price feature set is constructed. According to historical electricity price data, an electricity price prediction model of a BP nervous system introducing an adaptive fuzzy neural network is established, a multi-dimensional reference electricity price feature set is constructed, and an electricity price normal range is delimited. And comparing the multi-dimensional real-time electricity price feature set with the multi-dimensional reference electricity price feature set in real time, performing multi-dimensional backtracking analysis on the abnormal node electricity price, and analyzing the reason of the electricity price abnormality. And according to the backtracking analysis result, classification is carried out according to abnormal reasons, and an abnormal alarm is sent to technicians. According to the method, the abnormal electricity price and the reason thereof are accurately positioned, the problem response speed and the processing efficiency are improved, and powerful technical support is provided for stable operation of the electricity market.

Systems and methods are described for treatment of neurological conditions in which transcranial illumination using infrared, near-infrared and / or red wavelengths of light are delivered into the brain of a patient using a portable head wearable device. Arrays of light emitters are simultaneously controlled to provide a selected power density and distribution during a therapeutic session to treat a condition of the patient. An external controller such as a touchscreen tablet or laptop computer can be used to select illumination parameters for each patient. The system can be used to provide treatment to children such as those having autism spectrum disorder (ASD).

A building-integrated artificial intelligence system forming a continuous ambient neural field is disclosed. The system includes a distributed multimodal sensor lattice, an on-premise symbolic cognition engine, and an adaptive environmental control kernel operating entirely at the building edge without reliance on external cloud services. Sensor data from optical, thermal, acoustic, airflow, pressure, structural, electrical, and chemical modalities are transformed into non-identifying occupancy vectors, behavioral glyphs, risk indicators, and environmental state descriptors. A privacy-governed policy graph determines sensor permissions, redaction thresholds, consent conditions, emergency overrides, and jurisdiction-specific compliance parameters. The neural field predicts occupancy loads, optimizes HVAC, ventilation, and lighting, detects accidents and structural anomalies, classifies emergent risks, and generates redacted event capsules for audit and emergency dispatch. A federated topology enables multiple buildings to exchange compressed symbolic templates to improve predictive accuracy without transmitting raw data. The system provides a universal, regulation-aligned AI nervous system for autonomous building operations.

A method for classifying neurological disease status is described. The method includes acquiring, by a data preprocessor logic, patient image data. The method further includes generating, by a trained artificial neural network (ANN), a classification output based, at least in part, on the patient image data. The classification output corresponds to a neurological disease status of the patient. The trained ANN is trained based, at least in part, on longitudinal source data.

The embodiment of the invention discloses a multifunctional composite neural electrode system which comprises a supporting structure, a micro-fluidic structure and a flexible electrode structure, the micro-fluidic structure comprises a micro-fluidic channel; a groove is formed in the supporting structure, and at least part of the microfluidic channel is arranged in the groove; the flexible electrode structure is located on the surface of the supporting structure and covers or wraps part of the micro-fluidic channel. By adopting the technical scheme, drug delivery is performed to a target position of a nervous system through the microfluidic structure, electrical recording and chemical regulation and control can be performed on neurons at the same time, and the regulation and control effect of drugs on the neurons can be fed back in real time through electric signals.

The invention relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

The invention provides a time domain parallel WTA circuit and a control method thereof, and relates to the field of integrated circuits. The WTA circuit comprises switch control circuits, a reset latch circuit and a common suppression circuit, wherein the number of the switch control circuits is the same as that of output neurons; a single-channel control signal generated by the reset latch circuit and a common suppression signal generated by the suppression circuit are respectively used for controlling the switch control circuit to realize a WTA mechanism; and when the output signal reaches the output end, the neuron which reaches the output end first keeps normal output, and the other neuron channels are closed until the control signal is reset. According to the method, the correctness of the WTA decision can be ensured under the condition that the time intervals of different output signals are relatively small, and meanwhile, the output of the winning party is not influenced, so that the high speed and the accuracy of time coding information are effectively improved, and the noise robustness and the stability of a nervous system are improved. And meanwhile, a parallel structure and a common suppression circuit mode are adopted, so that the integration density of the chip is improved.

The invention relates to the technical field of nerve probes, and provides a four-mode integrated nerve probe and a preparation method thereof. The four-mode integrated nerve probe provided by the invention comprises a flexible substrate, an electric signal recording module, an ultrasonic stimulation module, a light stimulation module and a microfluidic channel. The electric signal recording module is used for recording electroneurographic signals, the ultrasonic stimulation module is used for ultrasonic stimulation, the optical stimulation module is used for optical stimulation and signal recording, and the microfluidic channel is filled with drugs through a microfluidic channel control port in the probe body and releases the drugs through a microfluidic channel outlet in the needle tip. According to the four-mode integrated nerve probe provided by the invention, multiple functions are integrated into one probe, so that comprehensive detection and regulation of a nervous system are realized, and the technical problems that a traditional nerve probe is single in function, inflammation is caused by a rigid material and multi-mode integration is insufficient are solved.

Methods in accordance with the present disclosure include administering a plurality of instances of a learning task to one or more subjects. The one or more subjects provide responses in accordance with the learning task and the responses are then graphed. Decision boundaries associated with the responses provided in each instance may differ between a first instance of the learning task and a second instance of the learning task. A trend is determined between a first decision boundary and a second decision boundary. Based on this trend, one or more of a neurological state, condition or impairment, a cognitive ability, or a cognitive flexibility may be assessed in connection with one or more subjects.

A system has a digital health avatar can substitute to a neurologist, nurse practitioner, physical assistant or research coordinator (in the context of a clinical trial) to run efficiently a routine neurological examination and document the report. This avatar supports the neurologist in his / her work, and will not replace the neurologist who still need to use his / her expertise to do the final diagnostic and manage the patient disease.

The invention discloses a neural function detection balancing system and method based on artificial intelligence, the system comprises a detection unit, a signal processing unit, an intelligent analysis unit, a treatment unit and a control unit, the detection unit comprises a multi-mode sensor array and is used for collecting neural signals of a human nervous system; the signal processing unit is used for filtering, amplifying and digitalizing the neural signal acquired by the detection module; the intelligent analysis unit is used for establishing a neural function evaluation model and analyzing the abnormal condition of the neural signal to obtain an analysis result; the treatment unit comprises an electromagnetic pulse generator, an ultrasonic generator and a phototherapy device and is used for performing forward stimulation treatment on the ganglion according to an analysis result; and the control unit is used for controlling and adjusting the treatment parameters of the treatment unit. According to the invention, the integration of neural function detection and treatment is realized, and the accuracy and objectivity of neural function evaluation are remarkably improved through multi-modal signal acquisition and artificial intelligence analysis.

The invention discloses a universal medical image auxiliary diagnosis system which comprises an ultra-low radioactivity imaging module which adopts a 3D CycleGAN network as a basic skeleton for PET image reconstruction; the epilepsy focus positioning analysis module is based on a self-adaptive medical image segmentation framework in deep learning; the user interface is used for providing a graphical operation interface and supporting multiple interaction modes so as to meet using habits of different doctors; and the data storage and management module is responsible for storage, backup and management of all medical image data and related information in the system, has data retrieval and query functions, and supports remote access and sharing. According to the universal medical image auxiliary diagnosis system, radiation exposure of a patient can be remarkably reduced, the accuracy and reliability of focus positioning can be improved, and powerful technical support is provided for diagnosis and treatment of nervous system diseases such as epilepsy.

The invention belongs to the technical field of drainage basin runoff prediction, and particularly relates to a drainage basin runoff prediction method based on multi-dimensional hydrological information. Comprising the following steps: step 1, constructing a medium-and-long-term runoff forecasting model: for hydrological data and meteorological data, analyzing correlation between influence factors and runoff by adopting a correlation coefficient method, screening out high-quality forecasting factors, and performing multi-factor forecasting; and step 2, constructing an LSTM network model: simulating an information processing system of a biological nervous system function, processing information through a neural network to complete a training task, and constructing the LSTM network model by referring to a neuron structure. According to the method, a Markov chain correction model is introduced from the angle of error correction, the method is combined with an artificial intelligence deep learning neural network model, an LSTM annual runoff prediction model based on Markov chain correction is established, and it is verified that the model is superior in medium and long term runoff prediction in combination with evaluation indexes.

A system for tracking neurological improvements, the system including a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive user data pertaining to a user, wherein the user data is associated with at least a neurological behavior, generate a habituation program as a function of the user data and a machine learning process, wherein generating the habituation program includes iteratively training a habituation machine learning model by receiving feedback data and adjusting one or more parameter values of the habituation machine learning model as a function of the feedback data, track a user's adaptability to the habituation program and generate an updated habituation program as a function of the user's adaptability to habituation program.

A non-invasive computer-aided system for classification of neurological disorders uses as input neuroimaging data, extracts morphological features from the neuroimaging data characteristic of each of a plurality of behavioral domains associated with the neurological disorder, and uses machine learning models to classify the severity of the disorder for each of the plurality of behavioral domains based on the extracted morphological features.

The invention discloses a heterogeneous electronic synapse device and a preparation method and application thereof, and belongs to the technical field of semiconductor devices. The heterogeneous electronic synapse device comprises a substrate, a connecting layer, a functional layer, a channel layer, a drain electrode, a source electrode, a first grid electrode and a second grid electrode, the connecting layer is arranged on part of the surface of the substrate; the functional layer is stacked on the surface of the connecting layer and covers the residual surface of the substrate; the functional layer comprises a first region serving as a dielectric layer and a second region serving as a ferroelectric layer which are connected, the first region is connected with the first grid electrode, and the second region is connected with the second grid electrode; the channel layer is arranged on the surface of the functional layer and located between the source electrode and the drain electrode. The heterogeneous electronic synaptic device transversely integrates the synergistic interaction of the first grid electrode and the second grid electrode, has a complete electric modulation capability, successfully simulates the heterosynaptic plasticity in a biological nervous system, and effectively meets the requirements of advanced neuromorphic calculation. The preparation method is simple and easy to operate.

A system for analyzing a neurological and / or neurodegenerative disorder (ND) is disclosed. The system include an electrocardiogram (ECG) sensor and an inertial measurement unit (IMU) sensor to a subject; a processor; a memory that stores instructions. When executed by the processor, the instructions cause the processor to: gather ECG and IMU data for the subject; input the integrated ECG and IMU data to a first computational model and a second computational model; and infer a presence of the ND based on the first computational model, or a change in the ND based on the second computational model. A method for analyzing and ND is also disclosed.

The invention discloses a brain deep stimulation parameter recommendation system and method and a storage medium. The method comprises the following steps: determining each recommended electrode combination corresponding to a target stimulation target in the brain of the target object according to the target brain image through the processor, and selecting the target stimulation target in the brain of the target object according to a recommended stimulation parameter set of each recommended electrode combination and a preset screening condition set for the recommended stimulation parameter set, target stimulation parameters for stimulating the target stimulation target are determined, and the preset screening conditions at least comprise a stimulation range screening condition and a side effect screening condition. According to the invention, through systematic analysis of the spatial and functional relationship between the nervous system and the electrode slice, an accurate stimulation treatment window range is provided for electrical stimulation treatment, so that the accuracy and efficiency of electrical stimulation treatment are improved.

The invention discloses a hyperspectral image classification method in the field of hyperspectral image processing, and aims to solve the technical problems that complex spatial relation and texture information exist in hyperspectral images, and a conventional classification algorithm is difficult to fully utilize the information. The method comprises the following steps: a pulse neural network can adaptively extract more effective features and improve the sensitivity to hyperspectral data, and a dynamic threshold adjustment layer avoids manual setting of a fixed threshold and better adapts to the complexity of the hyperspectral data; the pulse neural network can effectively process spatio-temporal data by simulating dynamic processing characteristics of a biological nervous system, and the feature extraction layer highlights key features in binarized pulse signals, so that the classification performance of the key features is enhanced; according to specific information of each pixel, automatic binarization coding is carried out to form a pulse sequence, and better information fidelity is achieved; therefore, key features are fully extracted from the hyperspectral image, and classification of the hyperspectral image is realized.

The invention discloses a pulse neural network error back propagation training method based on local synaptic plasticity, and the method comprises the steps: 1, building a pulse response model, taking a pulse sequence before and after synaptic neurons discharge as input, calculating a post-synaptic potential through a synaptic kernel function and weighted summation, and enabling the post-synaptic neurons to generate a new pulse as output; 2, simplifying the post-synaptic potential into two independent components by refining the pulse discharge time and approximately calculating the post-synaptic potential boundary value; 3, approximating a derivative between the post-synaptic potential and pulse counting by using pulse time dependent plasticity through a simplified post-synaptic potential calculation formula; and 4, combining local synaptic plasticity with a global optimization mechanism through a pulse time dependent plasticity approximate unknown derivative. According to the method, the advantages of local synaptic plasticity and a global optimization mechanism are fully utilized, the learning and adaptive capacity of the spiking neural network is enhanced, the method is closer to a biological nervous system, and the classification precision on training static and neuromorphic data sets is improved.

The invention discloses a photovoltaic buried cable fault positioning system based on a graph neural network, and the system comprises a multi-source signal collection module which is used for collecting electromagnetic response characteristics, and generating a multi-source signal sample set; the topological relation construction module is used for forming weighted directed topological relation data; the frequency domain mapping and fusion module is used for forming frequency domain-space joint feature input; the frequency domain self-evolution graph neural model module is used for executing feature propagation and aggregation of nodes and edges and outputting fault probability and uncertainty results; the space-time active detection module is used for selecting a cable section in an area with the maximum potential energy as an active detection target to form a self-tuning active detection closed loop; and the multi-source constraint correction module is used for outputting the type, the position and the confidence degree result of the buried cable fault. According to the invention, through the frequency domain self-evolution graph nervous system, accurate identification and positioning of the photovoltaic buried cable fault are realized.

Embodiments disclosed herein are directed to systems and processes that derive clinically meaningful data from a test device and a production device for assessing gait and speech in subjects having a neurological condition. The test device for gait analysis may be a simpler or more updated device in reference to a production device. Speech characteristics may be evaluated using a digital mobile speech-based platform. Other aspects are disclosed and claimed.

Applications for a Content Engagement Power (CEP) value include generating a script, text, or other communication content or for controlling playout of communication content based on neuro-physiological data, gathering neuro-physiological data correlated to consumption of communication content, or rating effectiveness of personal communications. The CEP is computed based on neuro-physiological sensor data processed to express engagement with content along multiple dimensions such as valence, arousal, and dominance. An apparatus is configured to perform the method using hardware, firmware, and / or software.

The invention discloses a neuroscience literature processing method and system for constructing a brain connection knowledge graph. The method comprises the following steps: training an entity recognition model; training a relation extraction model; literature data acquisition and standardization processing; entity and relation extraction; performing entity standardization; construction and storage of the knowledge graph; and application analysis of the knowledge graph. According to the method, the entity boundary recognition and relation classification accuracy is remarkably improved, the problems of inaccurate boundary prediction, wrong relation recognition and the like are remarkably relieved, and standardization and linking of entities are achieved by introducing a fuzzy matching and word vector similarity calculation method; meanwhile, reasoning and visual analysis of the brain region connection relation are supported, application scenes such as document retrieval, specific brain region input and output loop analysis and author community structure recognition are covered, and comprehensive and systematic knowledge support is provided for nervous system structure and function research.