97 results about "Neuron structure" patented technology

Disclosed herein are compounds, compositions, and methods for promoting neuronal growth and / or improving neuronal structure with the compounds and compositions disclosed herein. Also described are methods of treating diseases or disorders that are mediated by the loss of synaptic connectivity and / or plasticity, such as neurological diseases and disorders, with non-hallucinogenic psychoplastogens.

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.

The invention discloses a robot virtual-real cooperative training decision optimization system and method based on digital twinning, and the method comprises the following steps: collecting state data and disturbance data of an entity robot in a real environment, and carrying out the preprocessing of the data to generate standardized input; joint coding and state perturbation mapping are carried out on the standardized data, and a feature vector sequence embedded in a hyperspherical manifold space is generated; inputting to a virtual twin control body based on a hypersurface neural element structure, executing disturbance direction sensitive activation, and outputting an activated state vector sequence; virtual and entity control action prediction sequences are generated respectively, an embedded space difference vector is calculated, and control body parameters are updated based on a consistency optimization criterion; after convergence, the control body executes reasoning to generate a target control action sequence, the entity robot is driven to complete action execution, and control strategy optimization is achieved. According to the method, high-precision migration and rapid convergence of a robot control strategy are realized, and the execution stability in a complex disturbance environment is improved.

The invention discloses a three-dimensional interactive segmentation method and device, computer equipment and a storage medium, and is applied to the technical field of computers, and the method comprises the steps: obtaining a three-dimensional fluorescent microscopic image and interactive prompt information; wherein the interactive prompt information comprises a positive sample point, a negative sample point and a preorder segmentation mask; inputting the three-dimensional fluorescent microscopic image and the interactive prompt information into a constructed interactive segmentation model, and outputting a segmentation result; based on feedback of a user on the segmentation result, performing iterative optimization on the interactive segmentation model until the segmentation result meets preset precision; according to the invention, based on deep learning, the interaction prompt information and the three-dimensional fluorescence microscopic image are fused, accurate segmentation of the neuron structure is realized, and the precision and speed of neuron image labeling are improved.

Disclosed herein are compounds, compositions, and methods for promoting neuronal growth and / or improving neuronal structure with the compounds and compositions disclosed herein. Also described are methods of treating diseases or disorders that are mediated by the loss of synaptic connectivity and / or plasticity, such as neurological diseases and disorders, with benzofuran and benzothiophene psychoplastogens.

Disclosed herein are compounds, compositions, and methods for promoting neuronal growth and / or improving neuronal structure with the compounds and compositions disclosed herein. Also described are methods of treating diseases or disorders that are mediated by the loss of synaptic connectivity and / or plasticity, such as neurological diseases and disorders, with phenoxy or benzyloxy substituted psychoplastogens.

Disclosed herein are compounds, compositions, and methods for promoting neuronal growth and / or improving neuronal structure with the compounds and compositions disclosed herein. Also described are methods of treating diseases or disorders that are mediated by the loss of synaptic connectivity and / or plasticity, such as neurological diseases and disorders, with aryloxy psychoplastogens.

The disclosure provides a multi-modal neuron structure, comprising a reference voltage generation module and a pulse generation module, the reference voltage generation module comprises a first reference voltage generation module and a second reference voltage generation module, the first reference voltage generation module generates a first reference voltage, and the second reference voltage generation module generates a second reference voltage; the pulse generation module is connected with the reference voltage generation module, an input end of the pulse generation module is used for receiving a frequency-coded pulse sequence or a time-coded pulse sequence, and the pulse generation module comprises a first charge packet counter sub-circuit, a second charge packet counter sub-circuit and a mode signal input circuit. The disclosure can receive a frequency-coded pulse sequence or a time-coded pulse sequence, has a simple structure, occupies a small area, and is suitable for various forms of pulse sensors.

The invention discloses a neuron-like structure negative electrode material and a preparation method and application thereof, graphdiyne oxide and a carbon oxide nanotube adopt a one-step hydrothermal self-assembly method, the process is simple, expensive equipment is not needed, the neuron-like structure negative electrode material is suitable for large-scale production, and through hydrogen bond assembly between the graphdiyne oxide and the carbon oxide nanotube, the neuron-like structure negative electrode material is obtained. A bionic structure with a three-dimensional interconnection network and multistage pores is formed, and the ion diffusion path and the electron transmission efficiency are remarkably optimized. The negative electrode material shows high specific capacity and long cycle stability under rapid charge and discharge conditions, can still maintain relatively high capacity in a low-temperature environment, and breaks through the performance bottleneck of a traditional carbon-based material under extreme conditions. The method is applied to energy storage devices including but not limited to high-power lithium ion / sodium ion batteries and supercapacitors, and is suitable for low-temperature / fast-charging scene requirements including but not limited to electric vehicles and aerospace.

Disclosed herein are compounds, compositions, and methods for promoting neuronal growth and / or improving neuronal structure with the compounds and compositions disclosed herein. Also described are methods of treating diseases or disorders that are mediated by the loss of synaptic connectivity and / or plasticity, such as neurological diseases and disorders, with phenoxy or benzyloxy substituted psychoplastogens.

The method and device for real-time compensation of color cast and distortion of endoscopic images can compress the GAN generator and ensure the generation details of large-sized endoscopic images, and improve the quality of images generated by the compression model without adding additional generator structure. The method uses the GAN compression based network as the standard network, which includes: (1) pre-training a cycle GAN network with a multi-scale discriminator added to obtain a teacher generator and a discriminator; (2) compressing the teacher generator using knowledge distillation and neural structure search, and inheriting the multi-scale discriminator to train a student generator; (3) evaluating and selecting the appropriate compression model structure to obtain the final generator model.

The invention relates to a method for marking neurons and application of the neurons to unicellular reconstruction of the neurons. The method comprises the following steps: expressing a proximity marker enzyme with enhanced solubility in a target neuron, and providing biotin to the target neuron so as to form biotinylated protein in the neuron; carrying out integral sample dyeing on the biotinylated protein by using monovalent streptavidin coupled with a detectable marker; imaging the dyed sample to obtain a neuron structure image; and reconstructing the morphological structure of the single neuron based on the image data.

The invention relates to the technical field of motor control, and discloses an electrical transmission system parameter self-tuning system based on a neural network, and the system comprises a state monitoring and gating module which is used for activating the system when the high-frequency energy characteristic of a torque instruction exceeds a threshold value; the homomorphic mapping and reference reconstruction module is used for constructing a homomorphic digital filter to convert a torque instruction into a homomorphic instruction aligned with a feedback speed time sequence; the momentum residual calculation module is used for calculating the difference value between the theoretical impulse and the actual momentum increment based on the momentum theorem; the morphological decoupling and reasoning module is used for calculating morphological similarity between the residual vector and the reference vector by using the neuron structure so as to output confidence; and the parameter updating module is used for adjusting the adaptive updating gain according to the confidence coefficient and iteratively correcting the inertia parameter. According to the method, through signal homomorphic reconstruction and waveform form reasoning, the influence of loop delay and load disturbance is effectively inhibited, and the accuracy and robustness of parameter setting are improved.

This invention provides a research model and construction method for calcium ion imaging of ASH neurons in nematodes, belonging to the field of biological model construction technology. The construction method includes the following steps: nematode culture; construction of a transgenic probe plasmid: obtaining the ASH neuron-specific promoter sra-6 using PCR technology, and connecting HindIII and BamHI restriction sites to both ends of the promoter; amplifying YC3.60 using PCR technology; then inserting the promoter sequence into the pPD95.75 plasmid, and then inserting YC3.60 after the promoter. Transgenic microinjection of nematodes is used, followed by screening to obtain nematodes with ASH neurons carrying green fluorescent protein. In this application, YC3.60 is transferred into nematodes via transgenic means. Using a promoter specifically expressed in ASH neurons to link the gene of this fluorescent probe protein into the nematode, the fluorescent probe can be specifically expressed in the neuron. The constructed nematode model can be used for neuronal structure and functional imaging studies, as well as neuropharmacological efficacy analysis.

Disclosed herein are compounds, compositions, and methods for promoting neuronal growth and / or improving neuronal structure with the compounds and compositions disclosed herein. Also described are methods of treating diseases or disorders that are mediated by the loss of synaptic connectivity and / or plasticity, such as neurological diseases and disorders, with fused tetrahydroazepine or fused piperidine psychoplastogens.

This invention relates to nutritional compositions and their uses. Specifically, this invention relates to a nutritional composition comprising: (1) component A consisting of docosahexaenoic acid and arachidonic acid; (2) component B consisting of medium- and long-chain fatty acid triglycerides and 1,3-dioleoyl-2-palmitoyl triglycerides; and (3) component C consisting of choline and taurine. This invention also relates to the use of this nutritional composition for non-therapeutic purposes, including: promoting neurotransmitter expression; improving neuronal structure and function; and improving anxiety. This invention further relates to the use of this nutritional composition in the preparation of products for non-therapeutic purposes, including improving brain development and / or memory and / or improving anxiety. The nutritional composition of this invention can significantly improve brain development and / or memory and / or improve anxiety, which is beneficial for meeting current market demands.

Disclosed herein are compounds, compositions, and methods for promoting neuronal growth, improving neuronal structure, and / or increasing serotonin efflux through the serotonin transporter with the compounds and compositions disclosed herein. Also described are methods of treating diseases or disorders, such as neurological diseases and disorders, with constrained amine psychoplastogens disclosed herein.

Disclosed herein are compounds, compositions, and methods for promoting neuronal growth and / or improving neuronal structure with the compounds and compositions disclosed herein. Also described are methods of treating diseases or disorders that are mediated by the loss of synaptic connectivity and / or plasticity, such as neurological diseases and disorders, with fused pyrrolidine psychoplastogens.

The invention relates to the technical field of preparation of mixed matrix membrane materials, in particular to preparation of a neuron-inspired CNT (at) DU composite material and application of the neuron-inspired CNT (at) DU composite material in carbon capture. The invention provides a preparation method of a neuron inspired CNT (at) DU composite material. The prepared CNT (at) DU is taken as a filling agent and is physically blended with auto-polymerization constrained Pebax / PEG-MEA (SPM) to form a mixed matrix membrane. The SPM / CNT (at) DU mixed matrix membrane prepared by the method has the advantages of simple preparation process, controllable reaction, cheap and easily available raw materials and mild conditions, and can powerfully promote advantage complementation of a metal organic framework and a polymer matrix. Under the inspiration of a neuron structure, the CNT (at) DU is composed of DUiO-66 which simulates a cell body and CNT which simulates a synapse, so that the CNT (at) DU can play a role of an interactive neuron inspiration transmission network. Wherein the "synaptic imitation" CNTs provide a long distance and ordered channel for rapid CO2 transfer between adjacent DUiO-66. In addition, due to the fact that DUiO-66 of a cell body-like body has an unsaturated active Zr < 4 + > site, CO2 can be rapidly captured. The prepared mixed matrix membrane has an interactive neuron inspiration transfer network, and can strengthen the transfer of CO2 in the membrane.

The invention provides a micro-fluidic chip device for nematode culture, storage, capture and imaging analysis, and relates to the technical field of model animal nematode research methods, the micro-fluidic chip device comprises a culture pool, the top of the culture pool is open, the length and width of the culture pool are 1-2 cm, and the depth of the culture pool is 0.3-0.8 cm. The nematode culture method is the same as the conventional nematode culture method, and the conventional chip internal closed nematode culture method is changed, so that the culture environment is the same as that of the conventional culture, and the influence of a chip technology on nematode culture is eliminated. Meanwhile, open culture is more flexible in operation, accepts external operation and is high in plasticity. According to the device, efficient capturing, fixing, optical imaging analysis and the like of the nematodes cultured in real time as well as recycling and re-culturing can be completed. The imaging analysis can be used for neuron structure imaging, fluorescence labeling imaging, probe molecule imaging, ion level dynamic monitoring, drug stimulation reaction analysis and the like. The design can also be integrated with other nematode operation analysis modules to be developed into a multifunctional nematode analysis micro-fluidic chip.

This invention discloses an optical neuron structure based on an on-chip microring resonator, comprising, from front to back, a weighting unit, a summing unit, and a nonlinear unit. The weighting unit employs a wavelength division multiplexing (WDM) series microring resonator structure to apply weights to multiple input optical signals. The summing unit includes a photodetector and a preamplifier, used to sum the multiple weighted optical signals and drive the microring resonator of the nonlinear unit. The nonlinear unit employs a microring resonator structure to perform nonlinear output processing on the summed optical signals, primarily utilizing the nonlinear transmission characteristics of the microring resonator. This invention features high integration, and the proposed optical neuron exhibits strong scalability. Furthermore, the on-chip integration process is compatible with CMOS, enabling large-scale fabrication and facilitating the miniaturization, high integration, and high scalability of the optical neuron.

The application provides a phishing email recognition model and method based on multi-level multi-features and deep learning, the recognition model comprising a multi-channel input layer, a plurality of sub-channel input layers, and the total number of the sub-channel input layers being configured to correspond to the total number of input multi-level feature types; a multi-level embedding layer comprising a word-level embedding layer and a character-level embedding layer, the total number of the sub-embedding layers being configured to the total number of types of first features, and the first features being input to map from high dimension to low dimension to output third features; a BILSTM layer being configured to correspond to the sub-embedding layer and connected to the sub-embedding layer, the third features being input to splice and output fourth features; a feature joint layer being configured to input the second features and the fourth features, fuse and output joint features; and a full connection layer being configured to have a multi-layer neuron structure, the joint features being processed through the multi-layer neuron structure and output through an output layer structure, and the application has high efficiency and good robustness in recognizing phishing emails.

A system simulating a decisional process in a mammal brain about characteristics of motions related to body gestures of a visually observed body through a simulated visual path is provided. The system includes an interface toward simulated neuronal structures, the interface at least converting luminous information of the observed body to an optic flow data stream conveying information related to the visually observed body and that can be processed in the simulated neuronal structures, the system being a feed-forward system and comprising hierarchically from the visual observation to the decision: the simulated visual path and its interface, a simulated local motion direction detection neuronal structure for the detection of motion directions with receptive fields, a simulated opponent motions detection neuronal structure, a simulated complex patterns detection neuronal structure, and a simulated motion pattern detection neuronal structure.

This invention relates to a method and apparatus for converting an attractor neural network into a digital spiking neural network. The method includes: acquiring the neuronal structure of the attractor neural network; establishing a one-to-one correspondence between each neuron in the neuronal structure of the attractor neural network and a large neuron in the digital spiking neural network, each large neuron including a receptor surface state neuron and a clock excitation neuron; the connection relationship of the digital spiking neural network is as follows: the clock excitation neuron in each large neuron is connected to the receptor surface state neurons in other large neurons (excluding itself), and the receptor surface state neurons in each large neuron are connected to the receptor surface state neurons in other large neurons (excluding itself). Compared with the prior art, this invention has the characteristics of high stability, strong anti-interference, and good real-time performance of parallel data compared with traditional serial input data.

The embodiments of this specification provide a method, device and equipment for determining the stop pressure in a pipeline, including: obtaining index data of a target pipeline, the index data changing with the pipeline stop time; determining the pressure in the target pipeline at different stop times according to the index data and a pipeline stop pressure prediction model; wherein the pipeline stop pressure prediction model is obtained by training a preset autoencoder network model based on stop information, the neuron structure of the preset autoencoder network model considers time series characteristics during the updating process, and the parameter information of the preset autoencoder network model is determined based on a particle swarm optimization algorithm. The embodiments of this specification can improve the accuracy of prediction of the stop pressure of a refined oil pipeline.

The application discloses a channel estimation method, device, equipment, storage medium and program product, relates to the wireless technical field, and the method comprises the following steps: preprocessing channel state information of a frequency domain and a time domain of a communication network to obtain preliminary channel estimation data; optimizing the preliminary channel estimation data by a preset spike neural network adopting a multilayer dynamic neuron structure to generate optimized channel estimation data; extracting time-frequency domain state information of a channel based on joint time-frequency domain data converted by the optimized channel estimation data; and performing channel characteristic analysis on the time-frequency domain state information to generate a channel estimation result. Since the application improves the spike neural network by adopting the multilayer dynamic neuron structure in advance, the channel characteristic analysis is performed on the time-frequency domain state information corresponding to the generated optimized channel estimation data, and a high-precision channel estimation result can be obtained, thereby effectively improving the accuracy and robustness of channel estimation.

The invention belongs to the technical field of reservoir water temperature prediction and regulation and control, and particularly discloses an upstream discharged water temperature prediction and flexible water retaining curtain wall regulation and control method and system. According to the method, a physical mechanism of an LSTM neuron structure is reconstructed, a water body stratification stability mechanism and a vertical thermal diffusion mechanism are deeply fused in a deep learning network, an improved LSTM model can well predict a water temperature stratification phenomenon of upstream incoming water, and high-precision prediction of vertical water temperature distribution of a reservoir under a complex meteorological condition is realized. A water temperature mixing model calculation formula is created for the first time, and the problem that the discharged water temperature requirement is difficult to calculate is solved. The vertical water temperature predicted by the improved LSTM is combined with the discharged water temperature inverted by the downstream ecological target, and the optimal water taking elevation is compared and determined, so that accurate lifting regulation and control of the flexible water retaining curtain wall are guided, the ecological water temperature requirement of a downstream river channel is strictly guaranteed, meanwhile, water resource waste is effectively avoided, and the power generation benefit and the flux utilization rate of a reservoir are maximized.

Disclosed herein are compounds, compositions, and methods for promoting neuronal growth and / or improving neuronal structure using the compounds and compositions disclosed herein.Also described are methods for treating diseases or disorders mediated by loss of synaptic connectivity and / or plasticity, such as neurological diseases and disorders, using phenoxy or benzyloxy substituted psychogenic factors.

The invention discloses a multi-link mechanism optimization method and system based on a neural network and adaptive SQP, and the method comprises the steps: constructing a training sample with the length of each link as the input and the blanking speed, maximum speed and maximum acceleration of a sliding block as the output, carrying out the modeling through a feedforward neural network, optimizing a weight parameter through a multi-layer neuron structure and a gradient descent algorithm, and carrying out the optimization of a multi-link mechanism. Constructing a neural network model; performing an input variable disturbance experiment on the neural network model, extracting the sensitivity of each design variable to an output performance index, and forming a sensitivity matrix and a parameter response trend chart; according to the method, the blanking speed, the maximum speed and the maximum acceleration of a sliding block are comprehensively considered, a weighted multi-objective function is constructed with minimization as a target, a neural network model is used for replacing real simulation to carry out performance prediction on a design variable combination, and a self-adaptive SQP algorithm is used for solving for constraint processing and parameter discretization in the multi-objective optimization process. The neural network modeling and the adaptive SQP algorithm are combined, and the method is accurate and efficient.

The invention discloses a special transformer user voltage monitoring method and system based on a binary collaborative optimization voltage fluctuation model, and relates to the technical field of power system voltage monitoring. The neuron adaptive optimization algorithm can dynamically adjust the neuron structure of the model to match the time sequence complexity of the voltage data, and the prediction precision of the optimal voltage fluctuation model on the voltage fluctuation is greatly improved through the synergistic effect of the neuron adaptive optimization algorithm and the neuron adaptive optimization algorithm, so that the timeliness and accuracy of voltage out-of-limit abnormity decision are ensured. The technical problem that the prediction precision of a voltage fluctuation prediction model in the existing special transformer user voltage monitoring technology is insufficient is effectively solved, and safe and stable operation of a power grid and special transformer user equipment is guaranteed.