108 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.

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.

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.

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.

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.

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 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.

Implementations described and claimed herein provide systems and methods for a cloud-based seizure management platform for personalized management of seizures while providing assured connectedness across multiple stakeholders. The systems and methods address the needs of a patient in the area of seizure management, through such functionalities as seizure detection, seizure histories and other health-related data management, stakeholder connectedness, tachyphylaxis detection, drug titration guidance, and / or treatment aggressiveness management. The system provides for access for multiple parties, including allowing neurologists and / or caregivers to monitor progression of neurological conditions on a continuous basis while at home or otherwise remote from the patient. The unique methodology of the seizure management system includes wearable technologies coupled with artificial intelligence, machine-learning algorithms, and data modeling techniques to enable personalization of care.

The application provides a power transmission line naming recognition method based on a bidirectional enhanced nonlinear pulse nerve, and the core innovation is that a BiENSNP module is constructed to deeply simulate a dynamic information processing mechanism of a biological nerve system, the model is endowed with powerful basic representation capability, and is especially good at modeling complex nonlinear semantic patterns contained in text. The fusion architecture significantly improves the robustness and accuracy of the model in identifying entities in the power transmission line construction field (especially generative text), thereby laying a solid and good scalable technical foundation for constructing high-quality power transmission line construction knowledge base and other key application scenarios.

The invention relates to a neural tissue unit for use in implantation into the nervous system of a human or non-human mammal, wherein said neural tissue unit contains differentiated post-mitotic neuronal cells in an extracellular matrix, said unit being obtained from a cellular microcompartment comprising a hydrogel capsule surrounding the neural tissue unit, and said hydrogel capsule being at least partially removed before use of the neural tissue unit. The invention also relates to a process for preparing such a neural tissue unit.

The invention discloses a sparse electroencephalogram signal source positioning method based on functional magnetic resonance guidance, and relates to the crossing field of electroencephalogram signal processing and neuroimaging technologies. The method mainly comprises three parts of fMRI-guided sparse source space construction, subject specificity forward model construction and multi-target regularization inverse problem solving. The method comprises the following steps: firstly, screening high-activation voxels through BOLD signal intensity of fMRI, and constructing a sparse source space through connected component analysis and representative point selection; then, a subject specific boundary element (BEM) head model is constructed based on the structural MRI, and electrode registration and lead field matrix calculation are completed; and then a multi-target regularization model fusing data fidelity, space compactness and intensity consistency is constructed, optimal estimation of source current intensity is obtained through analysis and solution, and finally a positioning result of brain power activation is output. According to the method, the advantages of high time resolution of the EEG and high spatial resolution of the fMRI are fully played, the limitations of inverse problem morbidity, low spatial resolution and insufficient multi-mode fusion in traditional brain power supply positioning are effectively solved, and brain power supply positioning with high precision, noise resistance and high interpretability is realized; and a new scheme is provided for cognitive neuroscience research, nervous system disease diagnosis and brain-computer interface development.

The application discloses a neural system disease intraoperative monitoring system based on a large model, relates to the technical field of medical information processing, and comprises a synchronous acquisition module, a mapping construction module, a consistency evaluation module, a semantic normalization module and an attribution decision module. The system synchronously acquires double-channel vocal cord electromyography signals and stimulation events, intercepts a response segment and calculates a dominant side symbol; the consistency score of normal and reverse syntax is counted in a recording window, and channel side reversal caused by tracheal intubation rotation is actively identified; accordingly, semantic normalization processing of channel exchange or retention is performed, and the underlying anatomical mapping is corrected; finally, the large model reasons the normalized signal, accurately distinguishes real physiological damage from device measurement artifacts, and maps and outputs action instructions. The application eliminates semantic pollution caused by side error to the large model from the root, and greatly improves the accuracy of intraoperative intelligent monitoring.

The invention relates to a method (100) for integrating optical and electromagnetic sensor data, comprising the following steps, which are performed by the transformer-based neural system (30), in particular a transformer-based neural network (30): - Receiving (101) optical and electromagnetic sensor data from one or more sources in an environment, - Preprocessing (102) the received optical and electromagnetic sensor data to generate representations of features suitable for input into the transformer-based neural system (30), - Transforming (103) the preprocessed optical and electromagnetic sensor data via the transformer-based neural system (30) which employs one or more self-attention mechanisms and / or one or more cross-modal interaction techniques to integrate and combine the generated representations of the features, - Generating (104) a unified representation based on transforming (103).

This is a medical device consisting of proprietary hardware integrated with sensor technology (telemetry) for head and neck stabilization and monitoring, to offer improved stabilization and comfort while collecting data on the transportation process. By providing critical insights, the efficiency and efficacy of Emergency Medical Services (EMS) and military medical personnel can be enhanced, while also mitigating the risk of neurological worsening.

The invention relates to a neural tissue unit for use in implantation into the nervous system of a human or non-human mammal, wherein said neural tissue unit contains differentiated post-mitotic neuronal cells in an extracellular matrix, said unit being obtained from a cellular microcompartment comprising a hydrogel capsule surrounding the neural tissue unit, and said hydrogel capsule being at least partially removed before use of the neural tissue unit. The invention also relates to a process for preparing such a neural tissue unit.

A neuromelanin sensitive magnetic resonance imaging (“MRI”) technique, method and computer-accessible medium for measuring the extent of, providing a diagnosis of, monitoring the treatment of, assessing novel treatments for, or determining a prognosis related to one or more neurological conditions. To support these applications, the present disclosure accurately determines the normative range of neuromelanin-sensitive MRI signal and volume metrics in cognitively normal older adults. Those displaying certain characteristic neuromelanin-sensitive MRI signals falling outside of the normative range should be assessed and treated according to the particular diagnosis as provided by the present application.

The invention belongs to the technical field of artificial intelligence, and particularly discloses a cross-modal injury perception associative memory circuit based on an AIST memristor, and the circuit comprises an injury perception module which is used for converting touch, visual and auditory multi-modal injury signals into corresponding pulse voltage signals; the associative learning module is used for simulating synaptic plasticity through a memristor and strengthening the associative memory of a cross-modal signal; when the multi-modal damage signals are input at the same time, the resistance value of the memristor is reduced, the connection weight between neurons is enhanced, and associated storage and mutual activation of cross-modal signals are realized; the generalization differentiation module is used for extracting common characteristics of cross-modal signals, enabling similar stimuli to trigger the same response, and distinguishing signal specificity characteristics by using a resistance state separation mechanism of a memristor; and the output neuron module is used for generating a final response signal according to the processing result of the generalization differentiation module and simulating the stress response of the organism. The biological nervous system information processing mechanism can be comprehensively simulated.

Systems and methods in accordance with embodiments of the present disclosure include the creation and utilization of synthetic eye and head movement datasets that can be used as digital biomarkers for the screening, diagnosis or monitoring of neurologic diseases, including rare neurologic conditions. The synthetic datasets can be used to train deep learning models that can identify and phenotype neurologic diseases based on distinct eye movement patterns. The system and method create synthetic eye movement datasets using generative Al techniques. A pose-guided video generation framework is utilized to produce synthetic eye movement videos. A latent video diffusion mechanism translates segmented mask inputs — simplified representations of eye movements — into realistic visual sequences that mimic real eye movements associated with specific neurologic diseases.

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.

The present invention relates to a system and method for real-time screening of Attention Deficit Hyperactivity Disorder using electroencephalographic signals processed through a GPU-accelerated time-frequency inference architecture. The invention enables continuous acquisition of multi-channel electroencephalographic data, adaptive preprocessing for artifact suppression and signal stabilization, and parallel execution of time-frequency transformations to extract neurologically relevant features in real time. Extracted features are analyzed using an inference process configured to identify neurological patterns associated with Attention Deficit Hyperactivity Disorder, while continuous validation of signal quality and temporal consistency ensures diagnostic reliability. The system dynamically adapts analytical parameters based on evolving signal characteristics and regulates computational workload to achieve energy-efficient operation during prolonged monitoring. The invention provides a technically integrated, machine-implemented screening solution capable of delivering reliable, real-time neurological assessment suitable for clinical environments.

Described herein are systems and methods used to evaluate individuals such as children for behavioral disorders, developmental delays, and neurological impairments. An exemplary method includes receiving input data of an individual related to a behavioral disorder, neurological impairment, or developmental delay, and evaluating the input data using an evaluation module comprising at least one machine learning model, thereby generating an evaluation result, where the machine learning model comprises one or more decision threshold hyperparameters that differentiate between a positive evaluation, negative evaluation, and an indeterminate evaluation with respect to a presence or an absence of the behavioral disorder, neurological impairment, or developmental delay.

The application relates to an artificial visual nervous system based on a bimodal neural device, comprising a bimodal visual information calculation array composed of a physical convolution kernel array and a synapse calculation array, the physical convolution kernel array is used for receiving an ambient light signal and converting into an electrical signal output, the synapse calculation array is used for receiving the electrical signal and converting into a visual signal, and the physical convolution kernel array and the synapse calculation array are both composed of a plurality of bimodal transistors. The application has excellent bimodal collaborative calculation capability, the receptive field mechanism (excitatory / inhibitory response) of a retinal bipolar cell is simulated through the physical convolution kernel array, image feature extraction and preprocessing are realized, the synaptic plasticity (LTP / LTD mechanism) of a central nervous system is simulated in combination with the synapse calculation array, high-level calculation of the visual signal is completed, and a complete visual nerve bionic system is formed by virtue of an array composed of single devices.

The invention discloses a micro-grid operation random optimization method based on source load probability prediction. The method comprises the following steps: firstly, performing energy-sensitive self-organizing segmentation on source-load historical time sequence data, and extracting morphological fingerprint features; and adopting an improved affinity propagation clustering algorithm fused with a power system operation constraint penalty mechanism to identify a typical operation mode. Secondly, a quantile regression model based on a gated pulse neural P system is established in each mode for probability prediction, and a probability scene library with weights is generated; and finally, constructing a two-stage stochastic optimization model with the goal of minimizing the expected operation cost, and solving by adopting a Benders decomposition algorithm to obtain a fixed equipment plan and a flexible operation strategy. According to the method, through refined mode recognition and probability modeling, on the basis of fully considering the uncertainty of the source load, robust optimization of economic operation of the micro-grid is realized, and the expected operation cost of the system is effectively reduced.

The application belongs to the field of robot control, and provides a high-risk operation robot control system and method imitating human nervous system. The high-risk operation robot control system imitating human nervous system comprises a sensing element, a lower computer, a middle computer and an upper computer; the sensing element is used for sensing robot state information and transmitting the information to the corresponding lower computer; the lower computer is used for transmitting the received robot state information to the upper computer through the middle computer; the upper computer is used for forming instruction information according to a high-risk operation preset target and the received robot state information, and issuing the instruction information to the corresponding lower computer through the middle computer; and the lower computer is also used for controlling the corresponding motion component to execute the corresponding instruction information, and feeding back the received robot state information to the upper computer through the middle computer, to form a sensing-decision-execution closed loop control.

Subject measurement systems can generate indications of neurological state, disease, dysfunction, or injury using a machine learning model. The machine learning model can include at least one encoding model and a sequential model pre-trained to perform language processing tasks. The machine learning model can further include a classifier configured to output classifications. The at least one encoding model, sequential model, and at least one decoding model can be jointly trained to predict timeseries output, thereby adapting the pre-trained sequential model for use with neurologically relevant input domains, such as medical images, EEG data, evoked response data, speech data, or the like. The at least one encoding model, sequential model, and classifier can be jointly trained to output indications of neurological state, disease, dysfunction, or injury. A subject measurement system can then generate such indications using patient data and the at least one encoding model, sequential model, and classifier.

Methods providing biofeedback generally comprising: correlating an emotional state with an emotional resonance signal of a person; determining a bio-resonant frequency and wave form from a segment of the emotional resonance signal; and producing a feedback signal that is a higher harmonic of the emotional resonance signal. Said feedback signal can help adjust emotional states, balance brainwave activity, forge new neuro-circuits, increase interhemispheric communication, creativity and brain-efficiency thereby improving a full spectrum of nervous system activity from hyperarousal to deep sleep.

The invention relates to the technical field of image fusion, in particular to a multi-modal image fusion method based on a roundworm spiking nervous system, which comprises the following steps of: respectively inputting registered multi-modal source images into a bionic roundworm spiking nervous system model, and calculating the pulse excitation frequency of each pixel position in a period of time; the image information is converted into a pulse counting graph; comparing the sizes of the two pulse counting diagrams pixel by pixel to generate a corresponding binary weight map; and carrying out weighted average fusion on the two source images according to the weight map to obtain a final fusion image with complementary information and enhanced features. Compared with a pulse neural network, the method is simple in structure, convenient to apply, capable of being rapidly deployed, good in interpretability and capable of flexibly adjusting related parameters according to requirements. Different from the application of pulse neural network excitation, the method achieves an expected fusion effect through weight calculation based on pulse excitation counting.

The invention discloses a nervous system disease intraoperative monitoring system based on a large model, and relates to the technical field of medical information processing, and the system comprises a synchronous acquisition module, a mapping construction module, a consistency evaluation module, a semantic normalization module and an attribution decision module. The system synchronously collects a dual-channel vocal cord electromyographic signal and a stimulation event, intercepts a response segment and calculates a dominant side symbol; the channel lateral inversion caused by trachea cannula rotation is actively identified by counting the normal and inversion grammar consistency score in the recording window; performing semantic normalization processing of channel interchange or maintenance, and correcting underlying anatomical mapping; and finally, reasoning the normalized signal by the large model, accurately distinguishing real physiological injury and equipment measurement illusion, and mapping and outputting an action instruction. According to the method, semantic pollution to a large model caused by identification errors is eliminated from the source, and the accuracy of intraoperative intelligent monitoring is greatly improved.