WO2023178737A1 - Procédé et appareil d'amélioration de données basés sur un réseau neuronal impulsionnel - Google Patents

Procédé et appareil d'amélioration de données basés sur un réseau neuronal impulsionnel Download PDF

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WO2023178737A1
WO2023178737A1 PCT/CN2022/085668 CN2022085668W WO2023178737A1 WO 2023178737 A1 WO2023178737 A1 WO 2023178737A1 CN 2022085668 W CN2022085668 W CN 2022085668W WO 2023178737 A1 WO2023178737 A1 WO 2023178737A1
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neural network
data
neuron
spiking neural
network model
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郑胜杰
李文艺
李骁健
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中国科学院深圳先进技术研究院
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
    • G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning

Definitions

  • the present invention relates to the technical field of brain-computer interface, and specifically, to a data enhancement method, device, storage medium and equipment based on impulse neural network.
  • the implantable brain-computer interface is a system that allows the brain to directly interact with the outside world, thereby helping patients restore, adjust, and enhance functions.
  • the key technology of the implantable brain-computer interface is that it needs to be located in the cerebral cortex area of the brain's skull. Sensors are implanted to directly extract the information conveyed by neurons inside the brain; at the same time, the brain-computer interface system needs to be equipped with algorithms for interpreting neural information, so that it can interpret neural information, such as decoding the subject's movement intention, visual Information and so on.
  • neural electrodes sensors
  • the neural information collection ability of the neural electrodes will gradually decrease over time.
  • the main reasons are as follows: (1) The electrodes will cause some damage to the brain, causing glial scar tissue to grow around the electrodes, blocking The electrode contacts and neurons are separated; (2) The electrode contacts are gradually damaged in the electrolyte solution environment in the brain, reducing the electrical sensing performance of the electrode.
  • the ability to decode neural information will gradually decline over time; the main reasons are as follows: (1) The neural electrodes will shift due to brain shaking, resulting in changes in recorded information, and most decoding algorithms cannot Adapt to this change in neural information; (2) The brain's own neuroplasticity (the learning process of neurons) leads to the reconnection of neurons and changes in connection strength; therefore, the brain's neural information will change with the learning process.
  • Neural manifold refers to the neural activity pattern of a specific neuron group.
  • the internal functions of the brain are derived from the collaborative information communication of neuron groups.
  • a manifold is a space with local Euclidean space properties. It is used in mathematics. It is suitable for describing geometric shapes; and the activity of neuron clusters can be represented in a low-dimensional manifold space; therefore, under the constraints of the manifold space, it can assist the brain-computer interface to use a small amount of neural signals to complete the decoding of neural information.
  • the spiking neural network is often hailed as the third generation artificial neural network because it simulates the information transmission method of biological neurons; within organisms, communication between neurons is through the transmission of pulse signals, and information is contained in the transmitted pulses. in the sequence; while traditional neural networks use continuous values as the transmission information between artificial neurons, and the model architecture is of artificial design type and generally does not have biological functional characteristics; therefore, the biologically inspired spiking neural network uses spiking neurons and synapses. Therefore, the spiking neural network model based on biological properties is very suitable for studying brain information modeling and intelligent reasoning tasks.
  • Data augmentation is a technology that artificially expands the training data set by generating more equivalent data from limited data. Building a brain information decoding model through a brain-computer interface requires a large amount of brain information data, but the cost of obtaining a large amount of data is too high, and Data is an important factor in improving brain-computer interface decoding. Data enhancement is an effective means to overcome the lack of training data and is also widely used in various fields of artificial intelligence.
  • Embodiments of the present invention provide a data enhancement method and device based on impulse neural networks to solve the problem of data enhancement when training data is insufficient.
  • a data enhancement method based on spiking neural network including the following steps:
  • the raw data includes the neuron population, and convert the spike sequence of the neuron population into spike firing rate data;
  • the dimensionality of the spike sequence information generated by the spiking neural network model is reduced, and a neuron group activity pattern that is similar to the real neuron group activity pattern is obtained;
  • perturbation neurons are set up in the spiking neural network model. Based on the noise signals generated by the perturbing neurons, nerve impulse information data with noise and in line with the laws of real biological nerve activity is created and output.
  • the original data also includes the movement position information of the neuron group, and Gaussian smoothing is performed on the movement position information to remove part of the noise data.
  • a spiking neural network model is established, using the manifold spatial activity patterns of real neuron groups as the objective function, and performing supervised learning including:
  • the spiking neural network model uses the leakage integration firing model as the basis of the model.
  • the leakage integration firing model is as follows:
  • u i represents the membrane potential of the neuron cell, which is related to the cell pulse emission;
  • ⁇ m represents the time constant of the differential equation, which is used to control the change of the membrane potential with time;
  • u rest is a constant parameter , expressed as the resting potential of the cell membrane, that is, the size of the membrane potential of the cell in the resting state;
  • I i (t) is the input current, which will affect the membrane potential of the cell as an external input;
  • R is the cell membrane impedance;
  • ⁇ s is Synaptic time constant;
  • u threshold represents the pulse firing threshold.
  • Supervised learning includes:
  • Supervised learning is performed by replacing the gradient learning algorithm to update the neural network model weights; the replacing gradient learning algorithm is described as follows:
  • S i [n] is represented as a pulse sequence
  • ⁇ (x) is represented as a Heavisitter step function. Since the pulse signal has non-differentiable properties, during back propagation, the ⁇ (x) function is represented by ⁇ (x)
  • S i [n] ⁇ (u i [n]-u threshold )
  • the original data of the implanted brain-computer interface is obtained.
  • the original data includes the neuron group, and the spike sequence of the neuron group is converted into the spike firing rate data as follows:
  • the spike train of each neuron in the neuron population is processed by sliding window and converted into spike firing rate data.
  • the dimensionality of the spike sequence information generated by the spiking neural network model is reduced, and the neuron group activity rules that are similar to the real neuron group activity rules are obtained:
  • the mean square error is the mean of the square sum of the errors corresponding to the predicted data and the original data.
  • the mean square error formula is:
  • yi is the dimensionality reduction data output of the impulse neural network
  • n is the number of samples.
  • perturbation neurons are set up in the spiking neural network model, and based on the noise signals generated by the perturbing neurons, nerve impulse information data with noise and in line with the laws of real biological nerve activity is created and output. Specifically:
  • Some neurons in the spiking neural network model are based on Poisson neurons and set as perturbation neurons.
  • the model of Poisson neurons is:
  • I i is the input current, which is converted from a random pulse signal.
  • the random pulse signal conforms to the Poisson distribution law
  • r represents the spike firing rate of the neuron
  • P T [n] represents the probability that the nth spike of the neuron is fired within the duration T
  • the probability of pulse emission within the time interval ⁇ t can be r ⁇ t, where x rand is a random variable with a value between 0 and 1;
  • a data enhancement device based on spiking neural network including:
  • the pulse conversion module is used to obtain the original data of the implanted brain-computer interface.
  • the original data includes the neuron population and converts the pulse sequence of the neuron population into pulse firing rate data;
  • the first dimensionality reduction module is used to reduce the dimensionality of the pulse firing rate data and obtain the neuron group activity rules in the low-dimensional manifold space;
  • the supervised learning module is used to establish a spiking neural network model and perform supervised learning using the manifold spatial activity patterns of real neuron groups as the objective function;
  • the second dimensionality reduction module is used to reduce the dimensionality of the spike sequence information generated by the spike neural network model during the learning iteration process of the spike neural network model, and obtain neuron group activity patterns that are similar to the real neuron group activity patterns;
  • the data output module is used to set perturbation neurons in the impulse neural network model after the iteration of the impulse neural network model. Based on the noise signals generated by the perturbation neurons, create nerve impulse information data with noise and in line with the laws of real biological nerve activity. Output it.
  • a computer-readable medium stores one or more programs.
  • the one or more programs can be executed by one or more processors to implement data enhancement based on spiking neural networks as any of the above. steps in the method.
  • a terminal device includes: a processor, a memory and a communication bus; the memory stores a computer-readable program that can be executed by the processor;
  • the communication bus realizes the connection and communication between the processor and the memory
  • the embodiment of the present invention proposes a data enhancement method and device based on spiking neural network.
  • the method of this application is used for implanted brain-computer interface data, and can extract information distribution characteristics under the condition of limited neural information data; based on spiking neural network
  • the biological attributes of the network are suitable for learning the distribution characteristics of neural information, thereby generating neural signals that conform to the information distribution characteristics, and use this as brain information data enhancement; the present invention takes into account the cluster activity characteristics of biological neurons as the basis for data enhancement.
  • Impulsive neural networks have biological properties and are suitable for directly generating neural information, thereby enhancing brain-computer interface information, which is of great significance for the research and application of brain-computer interfaces.
  • Figure 1 is a flow chart of the data enhancement method based on spiking neural network according to the present invention
  • Figure 2 shows the neural signals collected by the implantable brain-computer interface of the present invention and the corresponding motion control
  • Figure 3 is a representation in low-dimensional manifold space of the neural signals collected by the implantable brain-computer interface of the present invention corresponding to the test;
  • Figure 4 is a schematic diagram of the impulse neural network architecture designed by the present invention.
  • Figure 5 is a schematic diagram of brain-computer interface data generated by the spiking neural network designed in the present invention.
  • Figure 6 is a schematic diagram of the data enhancement device based on the impulse neural network of the present invention.
  • Figure 7 shows the terminal equipment of the present invention.
  • a data enhancement method based on a spiking neural network including the following steps:
  • the S100 obtains the raw data from the implanted brain-computer interface.
  • the raw data includes the neuron population, and converts the pulse sequence of the neuron population into pulse firing rate data.
  • the original data includes information from the neuron group extracted from the motor cortex, movement position and movement speed information corresponding to the subject's movement control, and markers corresponding to the type of movement control test; each neuron group in the brain-computer interface-related movement
  • the neuron pulse sequence is processed by a sliding window and converted into neuron pulse firing rate data;
  • the movement position information is Gaussian smoothed to remove part of the noise data;
  • the moving average filter can convert the neuron population signal of the original signal into Downsampling improves the signal-to-noise ratio;
  • the Gaussian filter is a filter used to remove noise. It is also used for digital signal processing and removing the noise of the motion position of the original information; as shown in Figure 2, it is an implantable brain-computer interface Collected neural signals and corresponding motor control.
  • the moving average filter moves windows along the data with a length of window size (window Size) and calculates the average of the data contained in each window.
  • x is the signal input and ⁇ is the standard deviation.
  • S200 reduces the dimensionality of the pulse firing rate data and obtains the neuron group activity rules in low-dimensional manifold space.
  • the brain-computer interface collects neural impulse data and performs dimensionality reduction to obtain the neuron activity rules of low-dimensional manifold space. Specifically:
  • Step 1 Organize the original data into a matrix X with n rows and m columns;
  • Step 2 Zero-mean each row of X
  • Step 3 Find the covariance matrix
  • Step 4 Find the eigenvalues and corresponding eigenvectors of the covariance matrix
  • Step 5 Arrange the eigenvectors into a matrix from top to bottom in rows according to the size of the corresponding eigenvalues, and take the first K rows to form the matrix P;
  • S300 establishes a spiking neural network model and uses the manifold spatial activity patterns of real neuron groups as the objective function to perform supervised learning.
  • the present invention uses a spiking neural network as the model for generating this data.
  • the neuron model is based on the Leaky Integrate and Fire Model (LIF).
  • the learning algorithm is based on a supervised learning algorithm and uses a method called substitution.
  • Gradient Learning (Surrogate Gradient Learning) algorithm thereby updating the weights of the neural network model; as shown in Figure 4, it is a schematic diagram of the impulse neural network architecture designed by the present invention.
  • the leaked integration distribution model is as follows:
  • u i represents the membrane potential of the neuron cell, which is related to the cell pulse emission;
  • ⁇ m represents the time constant of the differential equation, which is used to control the change of the membrane potential with time;
  • u rest is a constant parameter , expressed as the resting potential of the cell membrane, that is, the size of the membrane potential of the cell in the resting state;
  • I i (t) is the input current, which will affect the membrane potential of the cell as an external input;
  • R is the cell membrane impedance;
  • ⁇ s is Synaptic time constant;
  • u threshold represents the pulse firing threshold.
  • S i [n] is represented as a pulse sequence
  • ⁇ (x) is represented as a Heavisitter step function. Since the pulse signal has non-differentiable properties, during back propagation, the ⁇ (x) function is represented by ⁇ (x)
  • S i [n] ⁇ (u i [n]-u threshold )
  • S400 During the learning iteration process of the spiking neural network model, reduce the dimensionality of the spike sequence information generated by the spiking neural network model, and obtain a neuron group activity pattern that is similar to the real neuron group activity pattern.
  • the pulse sequence generated by the output layer of the spiking neural network needs to be dimensionally reduced, and the dimensionality reduction data is used as the output.
  • the dimensionality reduction activity pattern of the real neuron group is used as the objective function, and the mean square error (MSE, Mean-Square Loss) as the loss function; the training results need to meet the requirements, and the neuron population activity rules of the output layer need to be similar to the real neuron population activity rules; the mean square error loss is also called quadratic loss, L2 loss, and is often used for regression prediction
  • the mean square error function measures the quality of the model by calculating the square of the distance (i.e., error) between the predicted value and the actual value. That is, the closer the predicted value and the actual value are, the smaller the mean square error between the two is.
  • the mean square error is the mean of the square sum of the errors corresponding to the predicted data and the original data.
  • the formula is as follows:
  • yi is the dimensionality reduction data output of the impulse neural network
  • n is the number of samples.
  • perturbation neurons are set in the spiking neural network model. Based on the noise signals generated by the perturbing neurons, nerve impulse information data with noise and in line with the laws of real biological nerve activity is created and output.
  • Nerve impulse information data As shown in Figure 5, it is a schematic diagram of the brain-computer interface data generated by the impulse neural network designed by the present invention.
  • the model of Poisson neuron is:
  • I i is the input current, which is converted from a random pulse signal.
  • the random pulse signal conforms to the Poisson distribution law
  • r represents the spike firing rate of the neuron
  • P T [n] represents the probability that the nth spike of the neuron is fired within the duration T
  • the probability of pulse emission within the time interval ⁇ t can be r ⁇ t, where x rand is a random variable with a value between 0 and 1;
  • the data generated according to the present invention can be further used in the fields of brain-computer interface information decoding and brain-like intelligence research; the method of this application is specially used for implanted brain-computer interface data, and can extract information under the condition of limited neural information data. distribution characteristics. Based on the biological properties of the spiking neural network, it is suitable for learning the distribution characteristics of neural information, thereby generating neural signals that conform to the information distribution characteristics, as brain information data enhancement.
  • the present invention takes into account the cluster activity characteristics of biological neurons as the basis for data enhancement.
  • spiking neural networks have biological properties and are suitable for directly generating neural information, thereby enhancing brain-computer interface information, which is of great significance for the research and application of brain-computer interfaces.
  • This invention takes into account the collaborative nature of brain-computer interfaces and brain-like intelligent algorithms.
  • the enhanced data is more in line with the internal laws of neural information, and the generated data is more biologically interpretable.
  • a data enhancement device based on a spiking neural network including:
  • the pulse conversion module 100 is used to obtain the original data of the implanted brain-computer interface.
  • the original data includes the neuron population, and converts the pulse sequence of the neuron population into pulse firing rate data;
  • the first dimensionality reduction module 200 is used to reduce the dimensionality of the pulse firing rate data and obtain the neuron group activity rules in the low-dimensional manifold space;
  • the supervised learning module 300 is used to establish a spiking neural network model and perform supervised learning using the manifold spatial activity patterns of real neuron groups as the objective function;
  • the second dimensionality reduction module 400 is used to reduce the dimensionality of the pulse sequence information generated by the spiking neural network model during the learning iteration process of the spiking neural network model, and obtain a neuron group activity pattern that is similar to the real neuron group activity pattern. .
  • the data output module 500 is used to set perturbation neurons in the impulse neural network model after the iteration of the impulse neural network model is completed, and based on the noise signals generated by the perturbation neurons, create nerve impulse information data that is noisy and conforms to the laws of real biological nerve activity. and output it.
  • the embodiment of the present invention proposes a data enhancement method and device based on a spiking neural network.
  • This application is used for implanted brain-computer interface data, which can extract information distribution characteristics under the condition of limited neural information data; based on the spiking neural network
  • the biological attributes of the brain are suitable for learning the distribution characteristics of neural information, thereby generating neural signals that conform to the information distribution characteristics, and use this as brain information data enhancement; the present invention takes into account the cluster activity characteristics of biological neurons as the basis for data enhancement.
  • Impulsive neural networks have biological properties and are suitable for directly generating neural information, thereby enhancing brain-computer interface information, which is of great significance for the research and application of brain-computer interfaces.
  • this embodiment provides a computer-readable storage medium.
  • the computer-readable storage medium stores one or more programs.
  • the one or more programs can be executed by one or more processors to implement The steps in the data enhancement method based on spiking neural network in the above embodiment.
  • a terminal device including: a processor, a memory and a communication bus; the memory stores a computer-readable program that can be executed by the processor; the communication bus realizes connection and communication between the processor and the memory; the processor executes the computer-readable program When implementing the above steps in the data enhancement method based on spiking neural network.
  • this application provides a terminal device, as shown in Figure 7, which includes at least one processor (processor) 20; a display screen 21; and a memory (memory) 22. It can also Including communications interface (Communications Interface) 23 and bus 24. Among them, the processor 20, the display screen 21, the memory 22 and the communication interface 23 can complete communication with each other through the bus 24.
  • the display screen 21 is configured to display a user guidance interface preset in the initial setting mode. Communication interface 23 can transmit information.
  • the processor 20 can call logical instructions in the memory 22 to execute the methods in the above embodiments.
  • the above-mentioned logical instructions in the memory 22 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.
  • the memory 22 can be configured to store software programs, computer-executable programs, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure.
  • the processor 20 executes software programs, instructions or modules stored in the memory 22 to execute functional applications and data processing, that is, to implement the methods in the above embodiments.
  • the memory 22 may include a stored program area and a stored data area, where the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created according to the use of the terminal device, etc.
  • the memory 22 may include high-speed random access memory, and may also include non-volatile memory.
  • program code such as U disk, mobile hard disk, read-only memory (ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, or they can also be temporary state storage media.

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Abstract

L'invention concerne un procédé et un appareil d'amélioration de données basés sur un réseau neuronal impulsionnel. Le procédé est appliqué à des données d'interface cerveau-ordinateur implantable, et peut extraire des caractéristiques de distribution d'informations dans une condition de données d'informations neurales limitées ; sur la base d'attributs biologiques d'un réseau neuronal impulsionnel, le procédé est approprié pour apprendre les caractéristiques de distribution d'informations neurales, de telle sorte qu'un signal neuronal conforme aux caractéristiques de distribution d'informations est généré, et le signal neuronal sert d'amélioration de données d'informations cérébrales ; le procédé considère des caractéristiques d'activité de grappe de neurones biologiques, et utilise les caractéristiques d'activité de grappe en tant que base d'amélioration de données, et le réseau neuronal impulsionnel a des propriétés biologiques, et est approprié pour générer directement des informations neuronales, de telle sorte que des informations d'interface cerveau-ordinateur sont améliorées, et le procédé a une grande importance pour la recherche et l'application d'interfaces cerveau-ordinateur.
PCT/CN2022/085668 2022-03-24 2022-04-08 Procédé et appareil d'amélioration de données basés sur un réseau neuronal impulsionnel WO2023178737A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117257312A (zh) * 2023-11-20 2023-12-22 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) 一种机器学习中增广脑磁图数据的方法
CN117437382A (zh) * 2023-12-19 2024-01-23 成都电科星拓科技有限公司 一种数据中心部件的更新方法及系统
CN117556877A (zh) * 2024-01-11 2024-02-13 西南交通大学 基于数据脉冲特征评估的脉冲神经网络训练方法

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118013308B (zh) * 2024-04-10 2024-06-04 中国科学院深圳先进技术研究院 基于自适应树的时空增强神经电脉冲信号聚类方法及系统
CN118094200B (zh) * 2024-04-28 2024-07-09 中国科学院深圳先进技术研究院 一种基于自监督学习的神经信号生成方法、系统及终端

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109117884A (zh) * 2018-08-16 2019-01-01 电子科技大学 一种基于改进监督学习算法的图像识别方法
CN110263924A (zh) * 2019-06-19 2019-09-20 北京计算机技术及应用研究所 一种神经元群模型的参数和状态估计方法
CN113435577A (zh) * 2021-06-25 2021-09-24 安徽知陉智能科技有限公司 基于训练深度脉冲神经网络的梯度函数学习框架替换方法
WO2021213471A1 (fr) * 2020-04-22 2021-10-28 北京灵汐科技有限公司 Procédé de traitement de données fondé sur un réseau de neurones impulsionnels, circuit de cœur de calcul, et puce
CN114049964A (zh) * 2021-05-21 2022-02-15 浙江大学 一种跨脑区人工神经通路的建模方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109117884A (zh) * 2018-08-16 2019-01-01 电子科技大学 一种基于改进监督学习算法的图像识别方法
CN110263924A (zh) * 2019-06-19 2019-09-20 北京计算机技术及应用研究所 一种神经元群模型的参数和状态估计方法
WO2021213471A1 (fr) * 2020-04-22 2021-10-28 北京灵汐科技有限公司 Procédé de traitement de données fondé sur un réseau de neurones impulsionnels, circuit de cœur de calcul, et puce
CN114049964A (zh) * 2021-05-21 2022-02-15 浙江大学 一种跨脑区人工神经通路的建模方法
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