150 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.

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.

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 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.

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.

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.

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.

The invention relates to an artificial visual nervous system based on a bimodal neural device, which comprises a bimodal visual information calculation array composed of a physical convolution kernel array and a synaptic calculation array, and is characterized in that the physical convolution kernel array is used for receiving an ambient light signal and converting the ambient light signal into an electric signal for output; the synaptic calculation array is used for receiving the electric signals and converting the electric signals into visual signals, and the physical convolution kernel array and the synaptic calculation array are each composed of a plurality of bimodal transistors. The method has excellent bimodal cooperative computing capability, and realizes image feature extraction and preprocessing by simulating a receptive field mechanism (excitement / inhibitory response) of the retina bipolar cells through the physical convolution kernel array; the synaptic plasticity (LTP / LTD mechanism) of a central nervous system is simulated in combination with a synaptic calculation array, advanced calculation of visual signals is completed, and a complete optic nerve bionic system is formed by means of the array composed of single devices.

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.

Exemplary light control devices and methods provide a regional variation of visual information and sampling (“V-VIS”) of an ocular field of view that improves or stabilizes vision, ameliorates a visual symptom, reduces the rate of vision loss, or reduces the progression of an ophthalmic or neurologic condition, disease, injury or disorder. The V-VIS devices and methods generate a moving aperture effect anterior to a retina that samples and delivers to the retina environmental light from an ocular field of view at a sampling rate between 50 hertz and 50 kilohertz. Certain of these V-VIS devices and methods may be combined with augmented or virtual reality, vision measurement, vision monitoring, or other therapies including, but not limited to, pharmacological, gene, retinal replacement and stem cell therapies.

The present invention discloses various embodiments of a word line decoder, a control gate decoder, a bit line decoder, a low voltage row decoder, and a high voltage row decoder, as well as various types of physical layout designs for simulating a non-volatile flash memory array in a neural system. The present invention discloses shared and segmented embodiments of a high voltage row decoder.

The present invention provides compositions and methods for modulation of neuronal networks in the CNS and / or PNS. In certain embodiments, the invention includes modulation of existing networks or restoring one or more damaged or lost axonal connections. In one embodiment, the invention comprises a tissue-engineered composition comprising an elongated tubular construct having at least one neuron and axon extending through the core of the construct.

A nanomaterial for optical stimulation of nerve cells, its preparation method and application. The present invention belongs to the field of nano-biological nerve medicine diagnosis and treatment, and mainly relates to the preparation of an organic / inorganic composite nanomaterial system with a capacitance effect. The present invention uses the supramolecular self-assembly of metal porphyrin to construct functionalized ZnTPyP-Au nanoparticles, which retain the physical and chemical properties of the original assembled molecule ZnTPyP while endowing the material with new optical properties. With ZnTPyP-Au as the core, a manganese oxide shell layer is grown and acts together with nerve cells to stimulate neuron activity using the photocapacitance property. The nanoparticles have good light response performance and electrical characteristics, and the present invention can be used for the treatment of neurological diseases and has important significance in clinical medicine.

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.