43 results about "Cerebellum" patented technology

The invention relates to the technical field of intelligent robots, and provides a cerebellum model and cross-modal attention fused control device, which comprises a three-layer cooperative control framework: a high-level strategy network for receiving global state data, outputting a macroscopic action element instruction and guiding the overall direction of task completion; the middle-layer cerebellar network is used for fusing multi-modal ontology sensing data in real time and outputting compensation torque; and the bottom layer actuator is used for controlling and executing joint torque output through a feed-forward PID. According to the invention, high-level action errors can be corrected online, and the dynamic anti-interference capability is remarkably improved; delay of torque compensation is reduced, and disturbances such as ground slipping and load sudden change are effectively handled; the strategy drift problem in traditional end-to-end training is solved.

The invention relates to the technical field of robot control, and discloses a robot cerebellum reinforcement learning method and system based on adaptive environment change, and the method comprises the steps: collecting multi-modal sensing data in the operation process of a robot; based on the multi-modal sensing data, determining an environment sudden change increment and an environment characteristic variable quantity, and obtaining a main strategy network according to the environment sudden change increment and the environment characteristic variable quantity; outputting a basic action vector of the robot based on the main strategy network, and performing value evaluation on the state of the robot to obtain a value evaluation quantity; performing updating calculation on the basic action vector based on the value evaluation quantity to obtain a compensation action vector; and performing vector superposition fusion on the basic action vector and the compensation action vector to obtain a final control vector, and taking the final control vector as a cerebellum learning action instruction of the robot, thereby realizing dynamic adaptation of a reinforcement learning strategy and cerebellum rhythm control. And the motion stability and the learning convergence efficiency of the robot in the unstructured environment are optimized.

The invention discloses a robot motion control method and system based on cerebellum reinforcement learning, and the method comprises the steps: obtaining a current environment state vector, and inputting the current environment state vector to a main strategy channel and a cerebellum compensation channel in parallel; the main strategy channel outputs a basic action based on a long-term task target, and the cerebellum compensation channel outputs a compensation action responding to real-time dynamic through an efficient query mechanism; synthesizing the basic action vector and the compensation action vector into a synthesized action vector driving robot; feeding back latest data after the robot drives the motion action vector, and determining a sensory prediction error based on the latest data; and updating the original parameters of the cerebellum compensation channel based on the sensory prediction error, and optimizing the original strategy parameters of the main strategy channel based on the latest data. According to the invention, by constructing a parallel double-channel architecture, functional decoupling of advanced decision and rapid adaptation is realized, and unification of rapid adaptation and continuous optimization is realized through a double-loop hierarchical learning system.

The invention discloses a real-time and deterministic communication monitoring system and method for large and small brains of a robot, and relates to the technical field of communication monitoring of the large and small brains, and the method comprises the steps: collecting a communication time sequence and a walking phase state sequence of an EtherCAT master station for brain planning and cerebellum servo driving in the walking control process of the robot; constructing the phase tissue density of the initial monitoring time window, and performing multi-scale time unwrapping and phase resampling on the phase tissue density to generate a plurality of candidate tissue densities; based on phase-time entropy preserving mapping, the organization density is converted into an executable monitoring scheme which is respectively applied to master station periodic communication monitoring, communication feature extraction and time sequence return evaluation, and finally the optimal organization density is screened out, so that key phase high-precision real-time communication monitoring is realized. The problem that existing communication monitoring is insufficient in sensitivity to key phases or excessively sensitive to transient jitter is solved.

The application discloses a kind of robot motion control method and system based on cerebellum reinforcement learning, it includes: obtaining current environment state vector, and parallel input to main strategy channel and cerebellum compensation channel;Main strategy channel outputs the basic action based on long-term task target, and cerebellum compensation channel then outputs compensation action by efficient query mechanism to cope with real-time dynamics;The basic action vector and compensation action vector are synthesized into synthesized action vector to drive robot;After robot drive movement action vector, feedback latest data, and determine sensory prediction error based on latest data;Based on sensory prediction error, the original parameters of cerebellum compensation channel are updated, and the original strategy parameters of main strategy channel are optimized based on latest data.The application realizes the functional decoupling of high-level decision and rapid adaptation by constructing parallel double-channel architecture, and the hierarchical learning system of double loop realizes the unity of rapid adaptation and continuous optimization.

The present application discloses compounds of formula (I) that modulate splicing of a precursor mRNA encoded by a gene, and methods of treating diseases and conditions associated with gene expression or activity of a protein encoded by a gene, such as spinal cerebellar ataxia 3 (SCA3 or Massa-Josephh Disease).

The present invention addresses the problem of providing an agent for preventing or treating amyotrophic lateral sclerosis (ALS), Parkinson's disease (PD), Huntington's disease (HD), spinocerebellar ataxia (SCA), aging-related degenerative or neurological disease, brain aging, or diseases associated with brain aging, as well as a more stable antibody that exhibits an effect of preventing or treating these diseases, Alzheimer's disease (AD), or frontotemporal lobar degeneration (FTLD). A human monoclonal antibody that specifically binds to human HMGB1, wherein the human monoclonal antibody (anti-human HMGB1 antibody) comprises a heavy chain CDR1, heavy chain CDR2, and heavy chain CDR3 each consisting of a specific amino acid sequence and a light chain CDR1, light chain CDR2, and light chain CDR3 each consisting of a specific amino acid sequence, is used as an agent for preventing or treating ALS, PD, HD, SCA, aging-related degenerative or neurological disease, brain aging, or diseases associated with brain aging. An antibody in which the light chain complementarity determining region (CDR) 3 of the anti-human HMGB1 antibody has been modified is used.

The utility model relates to the technical field of fixing devices, in particular to an integrated fixing device for manufacturing rodent pathological sections, which comprises a fixing device shell and a fixing device cover plate which are rotatably connected. The fixing device comprises a fixing device shell, a plurality of brain cutting clamping grooves are formed in the periphery of the fixing device shell, brain clamping grooves are formed in the bottom of the fixing device shell, the brain clamping grooves comprise a cerebellum fixing groove, a brain fixing groove and an olfactory bulb fixing groove, and the cerebellum fixing groove, the brain fixing groove and the olfactory bulb fixing groove are integrated. The closed fixing groove can be adopted, formalin volatilization is reduced, the brain tissue fixing function and the material taking function are integrated, and the operation process is simplified.

The invention discloses application of neurogranular protein in diagnosis and treatment of spinal cerebellar ataxia type 3, and belongs to the technical field of biology. Research finds that SCA3 patients and healthy control can be well distinguished by detecting the concentration of the nerve granule protein in plasma, and the diagnosis efficiency of the nerve granule protein in the SCA3 is proved. Furthermore, by overexpressing the neurogranular protein in an SCA3 mouse model, it is proved that the neurogranular protein can be used as a therapeutic target to treat SCA3. According to the invention, a new biomarker and a treatment target are provided for diagnosis and treatment of SCA3, and a technical support and a theoretical basis are provided for precise diagnosis and treatment of SCA3 and in-depth research of pathogenesis of SCA3.

Provided herein are targeting peptides and vectors containing a sequence that encodes the targeting peptides that deliver agents to specific substructures in the brain. Specifically, the targeting peptide is a component of a modified, sequence-specified adeno-associated virus (AAV) capsid protein further wherein the brain substructure may be the globus pallidus, putamen, internal capsule, caudate, claustrum, substantial nigra, motor cortex, insula,.temporal cortex, thalamus, hippocampus, subiculum, and deep cerebellar nuclei.

This invention overcomes the shortcomings of uncontrollable hardware interrupts in existing general control architectures and provides a hardware interrupt fault-tolerant emergency control method and system based on a brain-like independent architecture. It simulates the brain and cerebellum division of labor logic in the human brain, realizes complete isolation between the emergency control link and hardware interrupts, and completes millisecond-level emergency takeover when the main processor is abnormal, eliminating the risk of equipment loss of control.

The VEM-robot emotional right-brain model construction method decomposes the robot's brain into an emotional right brain, an intellectual left brain, and a motor cerebellum, forming the embodied or humanoid robot operating system VEM-ROS. This enables emotional communication between the robot and humans or other robots. Human output is used as the perceptual spectrum, and robot output as the deductive spectrum. Both the perceptual and deductive spectra are segmented, synthesized, and aligned using the emotional rhythm of the multimodal VEM-Token. LLM-Token decomposition of the large language model calculates lexicalized logical perception, and VEM-Token decomposition calculates the micro-expressions of multimodal emotional components. VEM-ROS also includes priority and interruption mechanisms, VEM memory mechanisms, dialogue relationships, craniofacial and limb sensors and actuators and interfaces, robot cloning, and operating system encapsulation. The right-brain model supports emotional micro-expression communication between the robot and humans, similar to the Turing test, and is expected to distinguish whether the emotional test subject is a robot or a human.

Disclosed are methods of treating ataxia by administering to a patient in need thereof a riluzole prodrug such as troriluzole. Pharmaceutical compositions and kits including the riluzole prodrugs are also disclosed.

The present invention relates to some crystal forms for a cinnamic compound, BMX, which is an inhibitor of histone deacetylase (HDAC), useful as an agent for the prevention or treatment of diseases associated with HDAC, including for treating tumor or cell proliferative diseases, diabetes mellitus, or neurodegenerative diseases such as Alzheimer's disease, Huntington's disease, Spinocerebellar Ataxias (SCA) and human spinal muscular atrophy (SMA). Also provided are a method for preparing the crystal forms and pharmaceutical compositions comprising the crystal forms.

An aspect of the present description relates to compounds useful for improving pre‑mRNA splicing in a cell. In particular, another aspect of the present description relates to substituted heteroaryl compounds, forms, and pharmaceutical compositions thereof and methods of use for treating or ameliorating spinocerebellar ataxia type 3 (SCA3), also known as Machado–Joseph disease (MJD).

The invention discloses a robot brain-like behavior decision model and method based on multi-brain-region cooperation, and belongs to the technical field of robot intelligent control. The basal ganglion module in the invention adopts the DQN to process the action decision of the high-dimensional state space, and the Q value stability is improved through the dynamic fusion of the target network and the MDN; the cerebellum module introduces a neuron remodeling mechanism, and dynamically resets the neuron age to reverse learning ability attenuation; the hippocampus module stores task data through a memory playback mechanism, and optimizes MDN parameters offline to enhance environmental adaptability; designing a self-adaptive step length strategy, dynamically adjusting the motion step length according to the obstacle distance and the rotation angle, and balancing the navigation efficiency and the obstacle avoidance precision; and the hybrid integration module dynamically adjusts cerebellum and basal ganglion output in combination with correlation and trajectory evaluation indexes to generate an optimal decision action. The dynamic decision-making efficiency and the environmental adaptability of the robot are improved by simulating the functions of the biological brain region and combining the optimization algorithm.

Provided is a method of treating spinocerebellar ataxia in a patient. The method includes administering to the patient in need of such treatment troriluzole at a dosage amount of about 200 mg per day and for a treatment period of at least one year, wherein the treatment is effective to provide a reduction in the patient's total f-SARA score of at least 50% compared to an untreated patient.

Provided are compounds, methods, and pharmaceutical compositions for reducing the amount or activity of ATXN1 RNA in a cell or subject, and in certain instances reducing the amount of ATXN1 in a cell or subject. Such compounds, methods, and pharmaceutical compositions are useful to ameliorate at least one symptom or hallmark of a neurodegenerative disease. Such symptoms and hallmarks include gait and limb ataxia, cognitive impairments, difficulty with speaking and swallowing, atrophy of the cerebellum and brainstem in magnetic resonance imaging (MRI), neurochemical abnormalities in the cerebellum and brainstem detected via magnetic resonance spectroscopy (MRS), and death within 10-15 years of symptom onset. Such neurodegenerative diseases include Spinocerebellar ataxia type 1.

Provided are compounds, methods, and pharmaceutical compositions for reducing the amount or activity of ATXN3 RNA in a cell or animal, and in certain embodiments reducing the amount of ATXN3 protein in a cell or animal. Such compounds, methods, and pharmaceutical compositions are useful to ameliorate at least one symptom or hallmark of a neurodegenerative disease. Such symptoms and hallmarks include motor dysfunction, aggregation formation, and neuron death. Such neurodegenerative diseases include spinocerebellar ataxia type 3 (SCA3).

The application belongs to the technical field of robot intelligent control, and relates to a robot behavior decision method and equipment simulating brain learning and memory mechanism. The method combines a developmental network DN2, reinforcement learning and a self-organizing map SOM, simulates functions of a cerebellum learning system, a basal ganglia and a hippocampus learning system, and obtains a self-organizing reinforcement learning model based on the developmental network DN2. The equipment comprises one or more processors and a computer readable medium storing one or more computer readable instructions, and is used for implementing the above robot behavior decision method. The application properly utilizes functions of the cerebellum learning system, the basal ganglia and the hippocampus learning system. The fusion algorithm combines reinforcement learning and supervised learning methods, enhances robustness of the self-organizing reinforcement learning model based on the developmental network DN2, improves the ability of the algorithm to obtain an effective obstacle avoidance strategy, and can help an intelligent agent to quickly learn an efficient solution.

The application relates to the technical field of robot control, and discloses a robot cerebellum reinforcement learning method and system based on adaptive environmental changes, multi-modal sensing data in the running process of a robot is collected; based on the multi-modal sensing data, an environmental mutation increment and an environmental feature change amount are determined, and a main strategy network is obtained according to the environmental mutation increment and the environmental feature change amount; a basic action vector of the robot is output based on the main strategy network, the state of the robot is valued, and a value evaluation amount is obtained; the basic action vector is updated and calculated based on the value evaluation amount, and a compensation action vector is obtained; the basic action vector and the compensation action vector are subjected to vector superposition and fusion, a final control vector is obtained, and the final control vector is taken as a cerebellum learning action instruction of the robot, dynamic adaptation of a reinforcement learning strategy and cerebellum rhythm control is realized, and the motion stability and learning convergence efficiency of the robot in an unstructured environment are optimized.

Described herein are methods and compositions for treating neurodegenerative diseases including Spinocerebellar Ataxia comprising administering a BACE1 inhibitor.

This invention discloses a passive running device for a mouse with its head fixed, comprising a base plate, a support assembly, a head fixation component, and a running mechanism. The support assembly is disposed on the base plate. The head fixation component is used to fix the head of the mouse and is detachably connected to the support assembly. The running mechanism is disposed on the base plate and includes a driver and a running belt assembly. The head fixation component is located above the running belt assembly to restrain the mouse on the running belt assembly. The driver is driven by the running belt assembly to drive the running belt assembly, enabling the mouse on the running belt assembly to passively perform running movements. This invention, by fixing the head of the mouse and utilizing the movement of the running belt assembly to passively induce running movements, can assist in observing the two-photon neuronal electrical signals of the mouse during running using a two-photon microscopy system, and observe the cerebellar neuronal firing patterns during movement, thus contributing to neuroscience research.

The application provides a multi-functional operant conditioning reflex neural network circuit with blocking and competition effect, wherein a stimulating signal is connected with an input end of a hippocampus module, an exciting signal and an output signal are connected with input ends of a cerebellum module, the exciting signal, the hippocampus module and the cerebellum module are connected with an input end of a voltage control module, an output end of the voltage control module is connected with an experience module and a feedback module respectively, an output end of the experience module is connected with a decision module and the feedback module respectively, an output end of the feedback module is connected with the voltage control module and the experience module respectively, and an output end of the decision module is connected with the feedback module and obtains the output signal. The application realizes learning of operant conditioning reflex under multiple input signals, can simulate decision judgment of animals in a complex natural environment, and widens the possibility of the existing operant conditioning reflex circuit.

This invention relates to the field of therapeutic drugs and systems for nervous system diseases, specifically to the application of Nrf2 in the preparation of therapeutic systems for treating nervous system diseases caused by tRNA synthetase deficiency. Through research on tRNA synthetase-deficient mutants, we discovered that tRNA synthetase deficiency causes a reduction in the size of the mesencephalon in zebrafish, resulting in symptoms of cerebellar hypoplasia and cerebral atrophy. In-depth research on the Perk signaling pathway involved in the above process revealed that the nfe2l2b gene and the Nrf2 protein are key factors regulating neural development, and their downstream target gene is p53. The inventors further applied the nfe2l2 gene to the preparation of therapeutic systems for treating nervous system diseases and to related drug screening, solving the technical problem of the lack of effective treatments for nervous system diseases caused by tRNA synthetase deficiency in the prior art, and possessing ideal value for widespread application.

A multifunctional insomnia treatment system provided by the present invention comprises a host, the host comprises a first controller, a first electrical stimulation output module and a first electrode plate group, the first electrical stimulation output module is electrically connected with the first controller, and the first electrode plate group is electrically connected with the first electrical stimulation output module; the first electrode plate group comprises a pair of first electrode plates and a pair of second electrode plates; under the control of the first controller, the first electrical stimulation output module can at least output three kinds of electrical stimulation including transcranial micro-current electrical stimulation, transcutaneous supraframed nerve electrical stimulation and cerebellar top nuclear power stimulation. According to the system, multi-mode electric pulse signals are adopted for treatment, through cooperation of the first controller and the first electrical stimulation output module, three kinds of electrical stimulation including transcranial micro-current electrical stimulation, percutaneous supraframed nerve electrical stimulation and cerebellar top nuclear power stimulation can be output, and therefore the system has multiple treatment modes and is suitable for different use requirements; the functionality is high, and the applicability is high.

A robot arm brain-like control method and control apparatus based on a spiking neural network. The control method comprises: on the basis of a spiking neural network, constructing a cerebellar module, so as to use the cerebellar module to obtain an initial torque instruction for a target robot arm; on the basis of the initial torque instruction, constructing a thalamic module, and using the thalamic module to adjust the initial torque instruction, so as to obtain a final torque instruction; using the final torque instruction to construct a brainstem module, so as to obtain a feedforward torque; and on the basis of spike coding and the feedforward torque, constructing a spinal cord module, and combining the cerebellar module, the thalamic module and the brainstem module to construct a robot arm control model, so as to output a torque control instruction. The control apparatus is used for implementing such a control method. The present invention can solve problems such as reduced control accuracy, impaired universality and inability to adapt to control tasks under complex load conditions, which are caused by significant differences in working requirements when a robot arm executes different tasks due to the impact of the size, mass, etc., of an operated object in complex working scenarios.

According to the withdrawal stage neural recovery brain network prediction method and system for methylamphetamine disorder, time sequence function connection and withdrawal stage dynamic recovery process are combined for the first time through dynamic network modeling, the limitation of traditional static analysis is broken through, dynamic characteristics of withdrawal stage neural recovery are disclosed, and the prediction method and system for the withdrawal stage neural recovery brain network of methylamphetamine disorder are provided. The positive network is mainly based on sensory motor-cerebellar connection, and the negative network relates to default mode network interaction, provides quantifiable neural markers to reveal cortical region composition of the withdrawal network, provides neural targets for clinical intervention and strength values of the withdrawal network during different withdrawal periods, evaluates recovery conditions, and provides quantitative neural markers for clinical intervention. And making of an individual rehabilitation plan is assisted.

This application provides a reinforcement learning system, method, and medium based on cerebellar circuit structure, relating to the field of artificial intelligence technology. The system includes: a state input module for acquiring state information in the reinforcement learning environment; a feature extraction module for extracting features from the state information to obtain state feature representation information; a cerebellar structure processing module for performing high-dimensional expansion, sparse connectivity, sparse activation, and dendritic modulation processing on the state feature representation information by simulating the information processing mechanism of the biological cerebellum to obtain a fused feature representation vector; a reinforcement learning decision module for inputting the fused feature representation vector into a reinforcement learning decision network to obtain an action space and corresponding action value vectors; and a policy output module for selecting the action with the maximum action value based on the action value vector output by the reinforcement learning decision network to generate a target action policy. This system can improve the efficiency of action decision-making tasks.

The application provides a cerebellar Purkinje neuron rapid identification system and method based on multi-modal deep learning, relates to the cross technical field of biomedical engineering and computer vision, and fuses specific fluorescence features and multi-dimensional morphological features, constructs a special multi-modal deep learning model, solves the interference problem of other neurons in a mixed culture system, and greatly reduces the false positive rate; a small sample learning architecture solves the industry pain point of a small amount of biological sample labeled data, improves the model generalization capability, and adapts to multi-scene sample identification. The identification system fuses Purkinje neuron specific fluorescence labeling features and cell morphological features, constructs a multi-modal deep learning recognition model based on small sample learning, is matched with a full-automatic microscopic imaging and analysis system, realizes rapid and high-accuracy identification of Purkinje neurons under a mixed culture system, does not need a complex cell purification step, greatly shortens the experimental period, and reduces the technical threshold.