9 results about "Biological neural network" patented technology

A method and system for differentiating acute and chronic pain based on the functional state of specific neural circuits is proposed. This method utilizes in vivo two-photon / endoscopic calcium imaging and ex vivo brain slice patch-clamp techniques to longitudinally monitor the activity dynamics of neurons in the DRN and ZI brain regions during the acute and chronic phases of a neuropathic pain model. By extracting and analyzing calcium and electrophysiological signal parameter sets, distinguishing criteria are established: if the activity of 5-HTergic neurons in the DRN is specifically enhanced in the acute phase, the indication is acute pain; if the activity of GABAergic neurons in the ZI is specifically enhanced in the chronic phase, the indication is chronic pain. This invention achieves an objective and precise differentiation of the transition from acute to chronic pain at the level of specific neural circuits, providing a new strategy and tool for pain mechanism research and clinical diagnosis and treatment.

This invention provides a method and device for refractive development assessment based on visual development prediction neural circuits, relating to the field of myopia management technology. The method includes: acquiring color retinal optical imaging signals and refractive ground state quantification values ​​of the subject; performing retinal stabilization preprocessing; extracting multi-level morphological features through a reentrant layered retinal choroid encoder; performing spatial feature aggregation gating on the extracted feature maps to obtain feature vectors; concatenating and integrating the feature vectors with the refractive ground state quantification values ​​to obtain fused feature vectors; constructing a fused feature vector sequence and inputting it into a refractive development dynamics internal model inferrer to generate a future multi-time-point refractive state prediction sequence; converting it into an interpretable refractive state numerical sequence; and providing risk warnings based on preset risk thresholds. This invention explicitly embeds physiological mechanisms prior into the model structure and parameter adaptive strategies, thereby maintaining the stability and interpretability of predictions even in a single medical visit scenario.

This invention discloses the application of prostaglandin A2 in the preparation of drugs for treating anxiety disorders and the related pharmaceutical formulation. It belongs to the field of biomedical technology, and specifically relates to the application of prostaglandin A2 in the preparation of drugs for treating anxiety disorders. Through chronic restraint stress (CRS) mouse models, corticosterone-induced (CORT)-damaged PC12 cell models, and multi-level molecular, circuit, and behavioral analyses, this invention demonstrates that prostaglandin A2 can exert multi-target anti-anxiety effects at the levels of overall behavior, cellular function, neural circuit structure, and molecularity. Prostaglandin A2 can treat anxiety disorders by improving anxiety-like behaviors and reversing behavioral phenotypic damage.

The application provides an Alzheimer's pathological oscillation electric stimulation signal screening method based on a description function, which comprises the following steps: constructing a virtual cortex network comprising a plurality of virtual neurons to simulate an Alzheimer's state of a patient's brain nerve; then performing linearization processing on the virtual cortex network model by using a description function method to obtain an equivalent linear closed loop system; generating a plurality of initial electric stimulation signals according to a range of electric stimulation parameters that can be borne by the patient, and inputting the initial electric stimulation signals into the equivalent linear closed loop system; and screening out candidate electric stimulation signals capable of making the system out of the Alzheimer's state by analyzing the response state of the system when the system is affected by each initial electric stimulation signal. The above scheme solves the problem that the existing linear system theoretical model is difficult to intuitively restore the dynamic regulation and control effect of electric stimulation on a biological neural network; and the effective electric stimulation signal can be efficiently screened out within the range of electric stimulation parameters that can be borne by the patient, thereby avoiding the disadvantages of blind attempts of different frequency and amplitude electric signals.

The present application provides a high-efficiency, safe and clinical-grade standard neural stem cell preparation method, which realizes the fate reprogramming and directional differentiation of cells under serum-free culture conditions by screening and optimizing specific small molecule compound combinations, and induces the obtainment of neural stem cells with high proliferation capacity and multi-directional differentiation potential. The method not only significantly improves the induction efficiency and quality of neural stem cells, but also avoids the influence of serum batch difference on experimental results in traditional methods, reduces production cost, and provides reliable technical support for clinical application. By transplanting the neural stem cells into the damaged area of cerebral palsy patients, the reconstruction of neural circuit and functional recovery can be promoted, and the motor ability and life quality of the patients can be improved. The serum-free clinical-grade neural stem cell preparation method and its application in cerebral palsy rehabilitation provided by the present application have the characteristics of high efficiency, stability and clinical applicability, and provide a new solution for central nervous system injury repair.

The application discloses an end-to-end automatic driving method and system under complex road conditions, and the method comprises the following steps: acquiring historical vehicle driving data under complex road conditions and making a data set; dividing the data set into a training set and a test set; inputting the training set into a feature extraction network to obtain a high-dimensional feature vector; establishing a pulse neural network, and performing pulse mapping on the extracted high-dimensional feature vector by using the pulse neural network; inputting the high-dimensional feature vector after pulse mapping into a neural circuit network model to obtain a prediction value; calculating the loss between the prediction value and the test set; adjusting the training parameters in the neural circuit network model according to the loss and training the neural circuit network model to obtain a prediction model; acquiring a real-time vehicle front image under complex road conditions, inputting the real-time vehicle front image into the prediction model, and obtaining a decision result.

This invention discloses a method and device for coordinated temporal modulation of central and peripheral ultrasound for children with motor neuron developmental disorders, comprising: acquiring multidimensional medical information and target physiological parameter information of the target child; prioritizing central and peripheral targets and determining the synergistic relationship between central and peripheral targets based on neural circuit conduction mechanisms; generating initialization timing parameters based on priority ranking and synergistic relationship, combined with a preset timing parameter database; controlling the ultrasound transducer to perform coordinated ultrasound stimulation on central and peripheral targets according to the initialization timing parameters, and monitoring the physiological response signal of the target child in real time; dynamically adjusting at least one of the initialization timing parameters, and continuing to perform coordinated ultrasound stimulation according to the adjusted timing parameters, thereby achieving synergistic enhancement by adjusting the timing to match the physiological neural conduction delay of the child, rather than basic synchronous energy superposition stimulation.

This invention discloses a human brain-based element-sharing data processing architecture and its implementation method. Based on the perception, memory, and neuronal connectivity characteristics of the human brain's biological neural network, this architecture decomposes the target processing object into indivisible and indispensable basic core elements according to their inherent attributes. These elements are then combined according to a preset hierarchical relationship to construct a multi-level sub-element structure. The basic core elements carry real attribute information and are fixedly stored in the bottom-level module. Sub-elements introduce an attention mechanism to filter and adapt element combinations, recording only the temporal, spatial, and logical relationships with the basic core elements / other sub-elements, without storing specific attribute information. All event elements exist in a distributed manner based on the hierarchical structure and are processed and expressed through relationships. This invention constructs a global sharing, multi-level bidirectional traceability and association mechanism between basic core elements and sub-elements. Information import naturally maps the inherent relationships of things, abandoning the rigid dependence on large-scale sample training and ultra-large-scale computing power. It adopts a distributed element-sharing and hierarchical parallel computing architecture, enabling efficient operation in conventional computing environments.

The present application relates to the technical field of neural information, in particular to a movement disorder treatment analysis method and system based on neural loop feedback, comprising the following steps: dividing functional partitions based on neural loop activity, extracting signal fluctuation and discharge trajectory to identify abnormal intervals, analyzing signal gradient and direction consistency according to candidate set, extracting distribution matrix to analyze uniformity and screen abnormal areas, evaluating deviation calibration level, screening partition adjustment, and outputting neural regulation linkage adjustment data table. In the present application, signal fluctuation and discharge trajectory are extracted based on neural loop activity division and time synchronization analysis, which can accurately identify abnormal intervals, and signal distribution matrix and functional feature analysis are used to strengthen local uniformity evaluation, optimize neural loop response coordination and signal conduction stability, realize accurate positioning and dynamic regulation of movement disorder causes, and ensure the pertinence and consistency of neural function evaluation and regulation.