43 results about "Synaptic junction" patented technology

The invention provides a synaptic connection processing method and system for a brain-like computing network, and the method comprises the steps: decoding a pulse signal through a pulse address decoding module, and obtaining a head address and data length information of a corresponding target synaptic index; reading a corresponding target synaptic index from a memory through a synaptic index DMA module according to the initial address and data length information of the target synaptic index; decoding the target synaptic index through a pulse address decoding module to obtain a first address and a number of corresponding target synaptic connections; reading all target synaptic connections from a memory through a synaptic index DMA module according to the initial addresses and the number of the target synaptic connections; and obtaining target neurons corresponding to the target synaptic connections and corresponding synaptic connection weights through a synaptic connection distribution module, and sending the synaptic connection weights to the corresponding target neurons in the neuron processing system. According to the method, the indexing and distribution of the synaptic connections can be efficiently and reliably realized.

The invention relates to the technical field of brain science image processing, and discloses a brain tissue neuronal morphology three-dimensional reconstruction and connection group analysis method and system, and the method comprises the steps: carrying out the feature point extraction and matching and optical flow registration of continuous ultrathin section electron microscope images, and obtaining three-dimensional image volume data; carrying out semantic segmentation on cell bodies, dendrites, axons and dendritic spines through a three-dimensional deep convolutional neural network, and driving secondary registration through confidence feedback; detecting synapses and classifying the synapses as excitatory or inhibitory types; executing topology perception skeletonized reconstruction and performing segmentation correction through topology anomaly feedback constraint; and constructing a synaptic connection matrix and executing graph theory analysis.

The invention discloses a low-altitude dynamic target unmanned aerial vehicle track traceability identification method based on machine learning. The method comprises the following steps: step 1, constructing a known track state sequence set; 2, constructing an identity track neurograph; 3, updating a synaptic connection weight by adopting an improved Hebbian learning rule, and generating an identity track memory map through a synaptic enhancement and attenuation mechanism; 4, constructing a target trajectory neural map, executing an improved Hebbian learning rule to update the synaptic connection weight, and obtaining a target trajectory memory map; 5, constructing a candidate identity set; 6, calculating a resonance identification score; and 7, identifying the candidate identity track memory map with the highest resonance identification score as a target traceability, and outputting a mapping result of a corresponding identity tag and a synaptic path. According to the invention, improved Hebbian learning rules and atlas resonance identification are fused, and low-altitude dynamic target unmanned aerial vehicle track traceability identification is realized.

An preventive or therapeutic agent for optic nerve diseases comprises an effective amount of an artificial synapse connector or a nucleic acid encoding the artificial synapse connector.

The application relates to a large model-based network attack chain timing inference prediction method and system, and belongs to the technical field of network security. The method comprises the following steps: acquiring a plurality of behavior events in a network attack chain, and performing neural coding on the behavior events to map the behavior events to discrete neuron input pulses. Based on different neuron input pulses, synapse connections corresponding to each neuron input pulse are constructed to generate a neuron attack chain graph. The neuron input pulses are integrated by using a dynamic sliding time window, and a large model is called to simulate the charging and discharging behavior of LIF neurons to determine the current state of the behavior events. According to the historical behavior information and the device security state of the behavior events, the current state of the behavior events is analyzed to predict the behavior trend of the network attack chain. The method significantly improves the expression ability of attack chain timing modeling, the prediction accuracy of attack evolution trend, and the initiative and intelligent level of security response.

The invention discloses a virtual computer network switch system, which comprises a physical resource isolation layer, a virtual computer network switch layer and a virtual computer network switch layer, wherein the physical resource isolation layer is used for dividing independent hardware channels through an FPGA (Field Programmable Gate Array) network card, binding tenants, establishing an encryption tunnel by utilizing quantum key distribution, and distributing resources by combining vGPU (Virtual Graphics Processing Unit) photon switching and a dynamic scheduler; after the virtual network abstraction layer undertakes resources, VXLAN and Geneve configuration are generated and verified in a formalized mode through a natural language intention, a neural morphological topology mapping virtual machine is constructed to be a neuron node, minimum permission communication is achieved through synaptic connection, and meanwhile cross-tenant traffic is subjected to quantum encryption; the intelligent layer deploys a GAN simulation attack test and optimizes a defense strategy, and dynamically adjusts a micro-segmentation rule and encryption strength through digital twin rehearsal configuration change and reinforcement learning; and the security service layer stores the configuration as an alliance chain smart contract, realizes change traceability through multi-signature verification, adopts Lattice cryptography and quantum signature to resist quantum attacks, and combines UEBA to continuously evaluate operation risks and dynamically adjust permissions.

The present invention relates to the technical field of neural stimulation, and specifically relates to a non-invasive spinal cord stimulator for upper limb motor function recovery, comprising a cervical spinal cord pulse control unit, an electrode connection unit, a motion monitoring unit, a wireless communication unit, a speech unit, a paradigm decision unit, an electrode array unit, and an artificial intelligence unit. In the present invention, the non-invasive spinal cord stimulator acts on a specific cervical spinal cord segment by means of non-invasive cervical spinal cord stimulation technology, inducing the reconstruction of a spinal cord neural network, strengthening synaptic connection, treating upper limb motor dysfunction of paralyzed individuals, and achieving upper limb motor function recovery.

The invention provides a transcranial magnetic stimulation treatment sleep disorder data analysis method, which comprises the following steps of: identifying a conversion process of sleep maintenance capability from continuous improvement to a stable plateau phase according to a sleep improvement degree in an early stage of consolidation and a synaptic connection daily attenuation degree, and determining a key turning time point when a transcranial magnetic stimulation treatment effect reaches a peak value; according to the weakening degree of the short interval treatment on the synaptic connection stability, the consolidation later-stage recovery blocking degree and the sleep time, the weekly decline time of the sleep maintaining ability is recognized, and a sleep ability decline record is obtained; dividing and integrating the key turning time point and the sleep ability decline record according to the adjusted treatment stage, evaluating the transcranial magnetic stimulation treatment effect improvement degree of the chronic insomnia patient, and obtaining an overall treatment response evaluation result.

The application discloses a kind of independent time delay supported pulse signal transmission system and method, it is related to information processing technical field, it is applied to brain simulation realized by electronic equipment with computing power, the system sequentially includes: front neuron cluster module, pulse signal transmission module and post neuron cluster module;Pulse signal transmission module is encapsulated with multiple pulse signal transmission operators based on synapse connection rule between neurons, for the simulation of pulse signal transmission between front neuron cluster and post neuron cluster;Pulse signal transmission module is provided with multiple interfaces for obtaining the technical parameters required for calling pulse signal transmission operator;Wherein, multiple interfaces include: synapse connection rule, expression form of synapse connection structure, weight information of synapse, time delay information of synapse, neuron number of front neuron cluster, neuron number of post neuron cluster, simulation total time step.The application can be applicable in different hardware systems and each synapse has independent time delay.

The application discloses a kind of isomorphic neuron system for generating hidden coexisting attractor, it is related to neuromorphic computing and nonlinear circuit field, the system includes: memristor, first neuron network and second neuron network;The structure of the first neuron network and the second neuron network is identical;Synaptic connection is carried out between the first neuron network and the second neuron network by the memristor, to simulate the chaotic dynamic behavior of biological neuron, to generate hidden coexisting attractor.The application can accurately simulate the synaptic transmission characteristics of biological neuron, improve the authenticity of bionics, successfully reproduce the chaotic dynamic behavior of biological neuron, efficiently generate hidden coexisting attractor, and enrich the dynamic performance of neuron system.

The invention discloses a method for training an incremental learning neural network based on multi-synaptic connection and local plasticity modulation, and the method mainly comprises the steps: constructing a multi-synaptic connection-based neural network which comprises a full-connection layer and a convolution layer: introducing a plurality of synaptic connections between two adjacent layers of neurons in the full-connection layer, and introducing a plurality of synaptic connections between two adjacent layers of neurons in the convolution layer; designing a plurality of parallel weight channels for each convolution kernel element; associating a qualification trace for each synaptic weight for recording local synaptic activity intensity, and initializing network parameters; distributing a sub-network for each task through a weight mask, updating a qualification trace according to an output value of a neuron, training a model by adopting a back propagation algorithm, and freezing distributed weights; calculating the value of a synaptic modulation factor according to the qualification trace intensity, and updating the weight in combination with the modulation factor; according to the method, the accuracy of incremental learning can be effectively improved, zero forgetting is realized, and the robustness of task sequence change is kept.

The application relates to a thymoma epithelial cell subpopulation with neuromuscular characteristics and application, and a thymoma epithelial cell subpopulation with neuromuscular characteristics is obtained through single clone dilution culture screening from a thymoma cell line Thy0517 of a patient with myasthenia gravis (MG) in combination, and a thymoma epithelial cell with neuromuscular characteristics in the cell subpopulation is named as Thymus_NMi; the cell subpopulation has synapse-like structures and neuromuscular adhesion characteristics, and efficiently expresses genes participating in neural cell adhesion and synapse connection, highly integrates neuromuscular double characteristics, and simulates key pathological characteristics of abnormal thymus-induced immune tolerance of MG patients in a molecular phenotype and physiological function, so that a cell model closest to a real clinical state is provided for exploring a myasthenia gravis occurrence mechanism.

The invention discloses an isomorphic neuron system for generating hidden coexisting attractors, and relates to the field of neuromorphic calculation and nonlinear circuits, and the system comprises a memristor, a first neuron network and a second neuron network. The structures of the first neural network and the second neural network are the same; the first neuron network and the second neuron network are in synaptic connection through the memristor so as to simulate chaotic dynamic behaviors of biological neurons and generate hidden coexisting attractors. According to the invention, synaptic transfer characteristics of the biological neurons can be accurately simulated, bionic authenticity is improved, chaotic dynamic behaviors of the biological neurons are successfully reproduced, hidden coexisting attractors are efficiently generated, and dynamic performance of a neuron system is enriched.

This invention discloses a pruning method, apparatus, and electronic device for spiking neural networks. The method includes: initializing an initial vector composed of the weights of each connection in the synaptic connection layer to obtain a weight vector; when pruning the spiking neural network using a backpropagation-based algorithm, calculating the gradient of each loss function value with respect to the hidden parameter vector using a predefined derivative function; updating the gradient of the hidden parameter vector using gradient descent, and calculating a target threshold for subsequent gradient updates using a preset incrementing function; based on the target threshold, mapping the hidden parameter vector back to the weight vector using the soft threshold function; and obtaining a trained spiking neural network model when the number of pruning training rounds reaches a preset number. This invention solves the technical problem of effectively deploying spiking neural networks on neuromorphic computing chips in related technologies.

The present disclosure relates to the technical field of computers, and provides a graph neural network-based method and apparatus for monitoring computing resources of a brain-inspired application. The method comprises: acquiring model information of a spiking neural network model for implementing a brain-inspired application, wherein the model information is used for indicating the intrinsic features of spiking neurons and the intrinsic features of synaptic connections in the spiking neural network model; inputting the model information into a pre-trained graph neural network model to determine spatial features of the spiking neurons in the spiking neural network model; acquiring a temporal feature of the brain-inspired application; and on the basis of the temporal feature, the spatial features, and a pre-trained resource monitoring model, determining computing resources occupied by the brain-inspired application during running. The spatial features and temporal feature of the brain-inspired application can be extracted so as to calculate the computing resources occupied by the brain-inspired application during running, thereby predicting the computing resources of the brain-inspired application; moreover, the running process of the spiking neurons does not need to be simulated, and therefore, the computing speed of the computing resources can also be increased.

The invention provides a compiling method and system for a brain-like chip. The method comprises the following steps: constructing a power grid neural map model; according to the power grid operation state data, calculating a task sensitivity score of each node as a node task activation value vector, strengthening the connection strength of a key region, and obtaining a task optimization adjacency matrix; mapping a relay protection rule into a gating channel of a spiking neural network to construct gating neurons, and performing adjacency matrix propagation path control by combining the task optimization adjacency matrix to obtain a dynamic gating adjacency matrix; constructing a joint node score according to the node task activation value vector and the node gating activation vector so as to remove redundant nodes and edges and obtain a sparse neural map meeting brain-like chip resource constraints; and mapping the sparse nerve map to a brain-like chip, initializing a neuron state and configuring synaptic connection. According to the method, a full-chain, strong-constraint and deployable compiling support path is provided for electric brain-like intelligence.

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 application discloses a synapse connection relationship and weight storage method and storage medium for a brain-like processor, and the method comprises the following steps: S1, storage space division and address mapping; the logical storage is divided into a direct index area and a multi-connection area to eliminate source ID redundancy and realize 0 or 1 direct addressing; S2, data format and minimal Hint coding; a 2-bit Hint code is used to uniformly identify a storage state, distinguish storage types and replace a pointer array; S3, adaptive access control; single / multi-connection access logic is adaptively executed according to the Hint code to realize single-connection zero jump, multi-connection continuous traversal and minimization of memory access times. The storage medium is realized based on the above method. The application has the advantages of high compression ratio, low memory access overhead, high scalability and the like.

The application relates to the technical field of brain science image processing, and discloses a brain tissue neuron morphology three-dimensional reconstruction and connection group analysis method and system. The method comprises the following steps: feature point extraction and matching and optical flow registration are performed on continuous ultrathin section electron microscope images to obtain three-dimensional image body data; a three-dimensional deep convolutional neural network is used for semantic segmentation of cell bodies, dendrites, axons and dendritic spines, and secondary registration is driven through confidence feedback; synapses are detected and classified into excitatory or inhibitory types; topological perception skeletonization reconstruction is performed, and segmentation correction is constrained through topological anomaly feedback; and a synapse connection matrix is constructed, and graph theory analysis is performed.

The application relates to a thymoma epithelial cell subpopulation with neuromuscular characteristics and application, and a thymoma epithelial cell subpopulation with neuromuscular characteristics is obtained through single clone dilution culture screening from a thymoma cell line Thy0517 of a patient with myasthenia gravis (MG) in combination, and a thymoma epithelial cell with neuromuscular characteristics in the cell subpopulation is named as Thymus_NMi. The cell subpopulation has synapse-like structures and neuromuscular adhesion characteristics, and efficiently expresses genes participating in neural cell adhesion and synapse connection, highly integrates neuromuscular double characteristics, and simulates key pathological characteristics of abnormal thymus-induced immune tolerance of MG patients in a molecular phenotype and physiological function, so that a cell model closest to a real clinical state is provided for exploring a myasthenia gravis occurrence mechanism.

The invention relates to a brain-like model training method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring activity data of each neuron connected through a target synapse under at least one neural activity; for each neural activity, generating a neuron output signal under the neural activity according to the activity data under the neural activity; the neuronal output signal under each neural activity is converted to obtain a pulse signal; obtaining distribution threshold adjustment information and a neuron distribution threshold corresponding to the pulse signal; and according to a difference condition between the distribution threshold adjustment information and a preset adjustment information threshold, updating the neuron distribution threshold so as to train a pre-constructed brain-like model. By adopting the method, the adaptive capacity and robustness of the brain-like model to the input signal can be enhanced, and the information processing capacity of the brain-like model is improved.

A method is performed by an electronic device for performing edge user allocation. The method includes selecting a first edge server to connect with a first mobile device. Neurons are arranged as a plurality of winner-take-all neuronal groups, each corresponding to a respective mobile device and comprising a respective first set of neurons representing a plurality of edge servers. Activating a first neuron causes an excitatory signal to be transmitted on a first synapse to a first threshold neuron. Each threshold neuron comprises a plurality of inputs connected by respective first synapses to a respective second set of those neurons of the first sets that correspond to a respective edge server. Each first synapse has a respective weight corresponding to a resource requirement of the mobile device. Activation of the first threshold neuron causes inhibitory signal(s) to be transmitted to at least one other neuron of the respective second set.

A method for constructing a clinically-relevant large animal model of traumatic optic neuropathy, employing a goat with orbital anatomy and optic nerve structure similar to humans as a model animal, overing the defects existing in other animals as TON model animals. The target model goat is obtained by screening parameters, such as a maximum transverse diameter of a coronal plane of a body of sphenoid bone and a wall width of a sphenoid bone, thereby facilitating the exposure of an optic nerve in a optic-canal segment under a nasal endoscopic surgery and quantitative injury. With the optic nerve injury animal model, the clinical TON pathogenesis can be explored by studying the optic nerve at the injured optic canal. In the prepared TON animal model, the injured optic nerve is closer to the brain, which is beneficial to rebuilding the synaptic connection.

The invention discloses application of Stab2 protein or coding nucleic acid thereof in preparation of a medicine for treating hearing loss, belongs to the technical field of biological medicine and gene therapy, and aims to solve the problem that in the prior art, an effective repairing means for cochlea banding synapse injury is lacked. The medicine mainly comprises a recombinant virus vector (preferably a lentiviral vector) carrying a Stat2 coding sequence, and the recombinant virus vector is suitable for being delivered to the inner ear through round window injection after being prepared; experiments show that overexpression of Stab2 protein in cochlea can significantly promote regeneration of banding synapses of inner hair cells after noise damage, and the number of synapses is effectively increased; meanwhile, the hearing brainstem reaction threshold value can be remarkably reduced, and the ABR I wave amplitude representing the nerve conduction function is improved; by repairing damaged synaptic junctions, noise-induced hearing loss and implicit hearing loss can be effectively treated, tinnitus, auditory allergy and speech resolution disorder are improved, and good clinical transformation prospects are achieved.

Disclosed herein 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 neuroplastogens.

The present disclosure is directed to pipelining operations of a spiking neural network (SNN) that performs in-memory operations. To model a computer-implemented SNN after a biological neural network, the architecture in the present disclosure involves different memory sections for storing inbound spike messages, synaptic connection data, and synaptic connection parameters (e.g., states). The section of memory containing synaptic connection data to identify matching inbound spike messages. In parallel, the section of memory containing synaptic connection parameters may be accessed to perform various neuromorphic calculations, synaptic plasticity and outbound spike message generation.

A synaptic array includes a plurality of Fowler-Nordheim (FN) synapses. Each FN synapse connected to at least one other FN synapse of the plurality of FN synapses to form a network. Each FN synapse includes a pair of FN tunneling devices each including a floating gate. Each FN synapse is operable to store a synaptic weight as a differential voltage across the floating gates of its FN tunneling devices and to implement synaptic memory consolidation.

The application discloses a low-altitude dynamic target unmanned aerial vehicle track tracing identification method based on machine learning, which comprises the following steps: step one, constructing a known track state sequence set; step two, constructing an identity track neural graph; step three, updating synaptic connection weights by using an improved Hebbian learning rule, and generating an identity track memory graph through synaptic enhancement and attenuation mechanisms; step four, constructing a target track neural graph, updating synaptic connection weights by using the improved Hebbian learning rule, and obtaining a target track memory graph; step five, constructing a candidate identity set; step six, calculating a resonance identification score; step seven, identifying the candidate identity track memory graph with the highest resonance identification score as a target trace source, and outputting a corresponding identity label and synaptic path mapping result. The application realizes low-altitude dynamic target unmanned aerial vehicle track tracing identification by fusing the improved Hebbian learning rule and graph resonance identification.