54 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 provides an analogue simulation method for accumulated craniocerebral injury under the action of multiple shock waves, and relates to the technical field of data processing. The method comprises the following steps: constructing an explosive shock wave model; constructing a head geometric model; the brain, the cerebellum and the brainstem in the solid area are represented through a Mooney Rivlin model; representing the skin, muscles and skull in the solid area through a linear elastic constitutive model; the cerebrospinal fluid in the fluid area is represented through the fluid dynamic model; coupling the fluid region and the solid region through an embedded boundary method; calculating the explosion impact pressure when the explosion impact waves of each turn are transmitted to the head through an explosion impact wave model; through a finite element analysis method, accumulated skull displacement, skull acceleration, skull stress, skull strain and intracranial pressure under the action of each turn of explosion shock waves are determined, and a brain accumulated injury value is calculated.

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 disclosure relates to compositions and methods for modulating, e.g., reducing or eliminating, the expression of ATXN2 via delivery using an adeno-associated viral (AAV) capsid variant. The compositions and methods of the present disclosure are useful in the treatment of subjects diagnosed with, suspected of having, or having spinocerebellar ataxia type 2 or another ATXN2-related disorder.

The invention discloses a humanoid robot control method based on a neural circuit, and belongs to the technical field of robot control, and the method comprises the steps: S1, constructing a three-layer pulse neural control architecture comprising spinal cord reflex, brainstem balance and cortex decision, and carrying out the weight adaptive distribution and cooperative operation between layers through the dynamic adjustment of neurotransmitter concentration, organic cooperation of prediction and reflection is realized through a three-stage control system; the cerebellar prediction network executes attitude adjustment in advance through a delay compensation algorithm, and compared with pure feedback, the control energy consumption is reduced; the parallel reflex pathway of the spinal cord layer establishes a nerve bypass mechanism, and the emergency response is realized by bypassing high-level processing in an emergency; the multi-layer safety monitoring improves the triggering accuracy of a protection mechanism through error confidence evaluation and contact stability analysis; a weight transfer mechanism of pulse frequency modulation avoids sudden movement change during control mode switching; by means of the design, the stability retention rate of the robot walking on the inclined plane is remarkably improved compared with a traditional method.

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 invention belongs to the technical field of motor speed regulation control, and designs a permanent magnet synchronous motor speed regulation control method based on an improved cerebellum model neural network controller, a storage medium and equipment. Secondly, designing an improved cerebellum model neural network controller to be applied to a speed ring in a vector control structure of the permanent magnet synchronous motor, improving a weight adjustment mode and a learning rate of an original network by the controller, and updating the weight of the cerebellum model neural network by adopting a gradient descent method in combination with a reciprocal relationship of learning times; a calculation mode of dividing by average distribution of a network generalization parameter C is replaced, and the learning efficiency of the network is improved; meanwhile, the value of the network learning rate is dynamically adjusted according to the running state of the motor, and the rapidity and stability of the system are improved. The result shows that the improved cerebellum model neural network controller effectively improves the response speed of the system and greatly improves the dynamic stability of the permanent magnet synchronous motor.

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 present invention relates to methods of treating diseases affecting motor function (e.g., motor function affected by diseases, brain and / or spinal cord injuries) using an adeno-associated virus (AAV) gene therapy vector comprising an adeno-associated virus 5 (AAV5) capsid protein and a gene product of interest flanked by an AAV ITR. In particular, the gene therapy vectors of the invention are administered by injection into cerebrospinal fluid (CSF), preferably by lumbar injection and / or injection into the cerebellar medullary pool.

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.

The invention discloses an artificial muscle soft driver bionic control method based on a spiking neural network, and the method employs a feedforward controller based on a rough inverse model as coarse adjustment, employs a cerebellar-like controller based on the spiking neural network as fine adjustment, and compensates the modeling uncertainty and environment interaction uncertainty in real time through an online learning method. According to the invention, a representative'artificial muscle 'soft driver-pneumatic artificial muscle is used, a pulse neural network simulating a working mechanism of a cerebellar nerve of a mammal is realized, and efficient, robust and adaptive control of the highly nonlinear and time-varying'artificial muscle' soft driver is realized.

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.

The present invention provides a method for simulating cumulative brain damage under the action of multiple shock waves, and relates to the field of data processing technology. The method comprises: constructing an explosion shock wave model; constructing a head geometric model; characterizing the cerebrum, cerebellum, and brainstem in a solid region using a Mooney Rivlin model; characterizing the skin, muscles, and skull in a solid region using a linear elastic constitutive model; characterizing the cerebrospinal fluid in a fluid region using a fluid dynamic model; coupling the fluid region and the solid region using an embedded boundary method; calculating the explosion shock pressure when each round of explosion shock waves is transmitted to the head using the explosion shock wave model; and determining the cumulative skull displacement, skull acceleration, skull stress, skull strain, and intracranial pressure under each round of explosion shock waves using a finite element analysis method to calculate the cumulative brain damage value.

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.