Virtual power plant (VPP) composed of a large number of distributed energy resources (DERs) has become a regional multienergy aggregation model to realize the large-scale integration of renewable energy generation into the grid. Due to the characteristics of centralized management, the existing energy operation mode is difficult to simply apply to distributed energy resources transactions. The decentralization, transparency, contract execution automation and traceability of blockchain technology provide a new solution to the aggregation of decentralized resources and the opacity of transactions in VPP. In this paper, the existing problems of virtual power plants are analyzed, and the virtual power plant trading model is designed, which realizes the transparent benefit distribution and message transmission of virtual power plants. The virtual power plant blockchain network based on blockchain technology in this model solves the DERs coordination problem in VPP and the security and efficiency problems in information transmission. Combined with the actual situation of virtual power plant, the blockchain network collaboration mechanism (BNCM), which is convenient to reach agreement, is designed. Compared with the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm, this mechanism can make DERs reach a consensus quickly. Finally, simulation experiments on the consensus algorithm show that the algorithm can reduce the collaboration time between DERs under the premise of ensuring the same fault tolerance rate and is more suitable for VPP scenarios with a large number of DERs. Virtual power plant (VPP) is used to realize the aggregation and coordination optimization of distributed generator, energy storage system, controllable load, electric vehicles and other distributed energy resources (DERs) through advanced information and communication technology, to participate in the power market and power grid operation as a special power plant . VPP canVPP has no specific constraints on the geographical location and composition of DERs ,4. WhileResearchers are eager to propose a highly flexible and adaptable DERs management method. Tomasz Sikorski et al. analyzed the technical and economic possibility of distributed energy integration into the virtual power plant and evaluated the economic efficiency of the virtual power plant model . Aiming As one of the hottest concepts in the Internet, blockchain technology has the characteristics of being decentralized, transparent, and distributed, which can be used to solve the trust problem in virtual power plants. Blockchain is essentially the integration and innovation of distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, and other computer technologies . BrilliaThe first problem is that the transaction is not transparent. VPP is used to realize the exchange of information and data on the power generation side, demand side, and power trading market, but it lacks a transparent benefit distribution mechanism. DERs and VPP operators cannot form a transparent choice of information, which increases the credit cost in the electricity transaction process. The second problem is the slow transaction speed. The use of blockchain in VPP has solved the security problem to a certain extent. However, the speed of node consensus in blockchain is slow. For example, the world’s largest blockchain network Bitcoin only supports 6–7 transactions per second, and it takes an hour to reach a final consensus for the transaction . MoreoveBased on the above problems, we established a virtual power plant trading model (VPPTM) and designed the operation process of VPPTM. The virtual power plant blockchain network (VPPBN) based on blockchain technology in this model solves the security and efficiency problems in the information transmission of DERs in VPP. Finally, based on the actual situation of virtual power plants, this paper designs a blockchain network collaboration mechanism (BNCM). The mechanism consists of the fast consensus stage and the practical byzantine fault tolerance (PBFT) consensus stage. Compared with practical Byzantine fault tolerance (PBFT) consensus algorithms, this mechanism enables DERs to reach consensus quickly. The results of simulation experiments on the algorithm show that BNCM can achieve shorter cooperation time and better performance. This mechanism is more suitable for virtual power plant scenarios with many DERs.The rest of this paper is organized as follows. VPP externally participates in the regulation of the power market as a whole, and internally realizes the coordinated operation control of each DERs. At present, the trust problem caused by the nontransparent information, the non-open rules, and the non-timely subsidies in the process of distributed energy trading has been paid more attention. For example, some energy service providers or load aggregators, after grasping the information on the national subsidy issuance policy in advance, forged information to defraud them . In addiTherefore, in order to realize the transparency of information and benefit exchanges between DERs and VPP operators, this paper presents an improved virtual power plant trading model (VPPTM), through which the DERs in the virtual power plant construct the flow of transactions. As shown in VPPTM relies on blockchain technology to achieve DER collaboration, and its blockchain structure is virtual power plant blockchain network (VPPBN). This section first introduces the composition of VPPPN, and then introduces its operation process. Blockchain has the characteristics of tamper-proof and traceability , all DERThe structure of the collaborative planning block is shown in When VPPBN is running, the VPP operator is responsible for packaging the transaction plan block. Each DER participates in the energy bidding on the VPPBN, and the smart contract is automatically executed when the smart contract trigger condition is reached. In VPPBN, both parties involved in the transaction will disseminate the transaction plan that has been reached. Finally, the DERs reach a consensus on the validity of this transaction plan through the blockchain network collaboration mechanism (BNCM) proposed in this paper. After a specified time, VPP operators will package all transaction plans in this period into transaction plan blocks and spread them on the VPPPN. Each DER verifies the legality of the transaction plan block containing all the transaction plans at the specified time. If it is legal, the block is stored in its own ledger, and if it is illegal, it is discarded.The traditional trading model using the PBFT algorithm has the disadvantages of wasting energy and low system throughput . A bitcoConsensus algorithms are used to reach a consensus among nodes in the blockchain . The praThe nodes involved in reaching a consensus in the PBFT algorithm are divided into primary node and backup nodes. The primary node is responsible for receiving client requests and sending them to other backup nodes. The backup node is responsible for verifying the correctness of the received message and sending corresponding reply messages to the primary node and other backup nodes. f. In order to ensure the normal operation of the entire system, the PBFT algorithm needs to have 2f + 1 normal nodes. Therefore, the total number of nodes in the system is 3f + 1. In other words, the PBFT algorithm can tolerate less than 1/3 invalid or malicious nodes. The consensus process of the PBFT consensus algorithm is divided into five stages: request, pre-preparation, preparation, submission, and response [The number of invalid or malicious nodes tolerated by the PBFT algorithm is response . Figure Request: The client  in VPPBN is composed of the fast consensus stage and the practical byzantine fault tolerance (PBFT) consensus stage. The overall operation logic of the BNCM is shown in The traditional PBFT consensus algorithm is usually used to solve the consistency problem in distributed systems . The colCancel the pre-prepare step in PBFT algorithmBecause the cost of delegating DERs in PBFT to participate in virtual power plant is very high, the pre-prepare step in PBFT is cancelled in the fast consensus stage. If the DERs cannot reach consensus, then the PBFT consensus algorithm is adopted to reach the consensus through the PBFT consensus stage. 2.VPP operators act as packers of transaction plan blocksThe main node in PBFT algorithm is selected by view number and number of nodes through fixed algorithm. If the selected master node is a malicious node, it will appear that the node does not broadcast the client request message or overtime broadcast, which will trigger the overtime mechanism of the PBFT algorithm and reselect the master node. If the reselected master node is still malicious node, the next round of reelection will continue, which greatly reduces the efficiency of the algorithm and is not conducive to the operation of virtual power plants . In this3.In the rapid consensus stage, the virtual power plant operator verifies the response message reliability as the main node rather than the cross-validation of response message reliability by each DERs.In the prepare stage and commit stage of PBFT algorithm, each replica node needs to synchronize point-to-point consensus with other nodes. However, when many distributed energy nodes in the virtual power plant communicate point-to-point, the communication time cost of broadcasting before nodes will increase exponentially . CombineThe blockchain network collaboration mechanism (BNCM) in VPPBN consists of the fast consensus stage and the PBFT consensus stage. Each DER first reaches a consensus through the fast consensus stage, and the flow chart of this stage is shown in f invalid or malicious nodes, the total number of nodes in this system is set to 3f + 1. The specific operation process of BNCM consists of eight steps, among which steps 1 to 4 are the fast consensus stage, and steps 5 to 8 are the PBFT consensus stage. Because the PBFT algorithm can tolerate less than Message preprocessingThe virtual power plant operator will summarize the transaction plan information collected from the VPPPN, and then package the information collected within the specified time interval into the transaction plan block. Finally, the virtual power plant operator broadcasts the block in the system.2.DERs send response messagesreceived, n, d, I > to the virtual power plant operator.After each DER receives the transaction plan block, it verifies the correctness of the block. If it is not correct, the block is discarded. If the verification is valid, each DER adds its own number to this block, and then sends a response message < n is used to sort the requests of the virtual power plant operator; d represents the summary of the transaction information in the transaction plan block, and I represents the current DER’s own number.Where 3.The virtual power plant operator counts the number of response messages< agree, n, d, I > to each DER. If the virtual power plant operator does not receive When 4.DERs process approval messagesAfter receiving the approval message, DERs update the transaction plan block to their own system. Consensus is completed.5.Pre-prepared phase< pre-prepared, n, d > to each DERs, where n is used to sort the requests of the virtual power plant operator; d represents the summary of the transaction information in the transaction plan block.When a consensus is not successfully reached in the fast consensus phase, the virtual power plant operator reaches it through the PBFT consensus stage. First, the virtual power plant operator broadcasts a pre-prepared message 6.Preparation phaseprepare, n, d, I > to other nodes and virtual power plant operators, where I represents the current DER’s own number.When each DER receives a pre-preparation message from the virtual power plant operator, each DER checks the correctness of the message. If it is not correct, the message is discarded. If the message is verified correctly, the DER sends a preparation message < 7.Commit phasef + 1 successful preparation messages are received, each DER sends a commit message < commit, n, d, I > to other nodes and virtual power plant operators.After the virtual power plant operator and other DERs receive the preparation message, they verify the correctness of the message. If it is not correct, the message is discarded. If 28.Reply phasereply, n, d, I > to the virtual power plant operator. If the virtual power plant operator receives After receiving the submitted message, virtual power plant operators and DERs check the correctness of the message. If it is not correct, the message is discarded. If DER receives It can be seen from the above specific operation process that in steps 1 to 4 of the fast consensus stage, the virtual power plant operator needs to receive the response messages of In this chapter, we use Ethereum to realize VPPTM. This chapter first introduces Ethereum technology, and then simulates the functions of power transaction and ledger maintenance in VPPTM through this technology.Ethereum is one of the largest blockchain platforms in the world. It firstly proposed the smart contract concept . The smaIn this paper, we use Go-Ethereum client to implement the bottom layer of VPPTM, using Ethereum-browser to implement the data visualization in VPPTM. When finishing the building of programming environment, we set the parameter value of genesis block as The pseudocode of this smart contract is shown in After VPPTM has been running some time, blockchain stores all electricity energy trading data and maintenance data, which are shown in In order to verify the feasibility and performance of BNCM, an experimental simulation system is designed. We used Docker to simulate multiple DERs interactions in a single machine. Docker is an opensource application container engine based on Go language, which can easily package simulated DERs into transplantable container, and achieve complete virtualization by sandbox mechanism. Each Docker instance simulates a DER with the Docker version number of Docker enterprise 3.0. The experimental system is shown in The physical server uses i7 7700k processor and 32 GB of running memory. The server operating system uses Ubuntu 16. 04 LTS. The experiment calls TcpDump to collect and analyze port network data, and calls ODBC library to write the collected data to MySQL database. In BNCM, Docker thread is used to simulate DERs and VPP operators. The DER port listens for the message, and after receiving the message, it passes the message to the core logic layer to judge the validity of the message and make corresponding processing. All historical data of DERs are stored in the account book. The intelligent contract is used to connect the DER interface layer, logic layer, and data layer. When the contract execution conditions are reached, the connect will be executed automatically. As shown in By using the experimental environment, this section tests the BNCM coordination time, fault tolerance, and other indicators, and compares them with the PBFT algorithm.OPE) and four DERs  are set up in the experiment, and the communication link between each DER and VPP operator is normal. After running the BNCM and PBFT algorithms sequentially for 50 times, the time it takes to reach a consensus is counted in In order to verify the efficiency of the consensus mechanism, a total of one VPP operator  and four DERs  are set up in this experiment. Because fault tolerance experiments show that when there are 3f + 1 nodes in the system, at most, f malicious nodes are allowed. Therefore, this experiment conducts a comparative experiment in the case of one malicious node in the system.To verify the efficiency of BNCM when no consensus is reached in the fast consensus stage, this section conducts the experiment based on DER is set to have 20%, 40%, and 60% probability of failure by modifying the state of BDER. When a communication failure occurs, BDER will not be able to communicate with other nodes normally. Experimental results when BDER has 20%, 40%, and 60% failure rates are shown in In this experiment, the communication link of BDER can communicate normally, BNCM reaches a consensus through the fast consensus stage. Therefore, the consensus time is shorter than the traditional PBFT algorithm. When a communication failure occurs in BDER, the fast consensus phase of BNCM fails, and each DER reaches a consensus through the PBFT phase. Therefore, the time to reach a consensus is longer than the traditional PBFT algorithm.The experimental results in DER increases, the advantage of BNCM slowly diminishes. When the failure rate of BDER reaches 60%, the PBFT algorithm is more efficient. We can conclude that when the failure rate of f nodes in the system increases, the efficiency of BNCM will gradually decrease. Eventually, BNCM will lose its efficiency. Overall, BNCM is more efficient than traditional PBFT algorithms. Finally, after analyzing the experimental data, it is found that the average time for BNCM to reach consensus is 3.598 s, 4.615 s, and 5.579 s in This section tests the relationship between the collaborative completion time and the number of DERs under different mechanisms. By recording the average value of the consensus process of 30 times, the PBFT algorithm and the BNCM are compared when the number of consensus nodes increases from one to eight.As shown in In the collaborative time comparison experiment, the collaborative speed of DERs in BNCM is significantly shorter than that of PBFT. In the actual scene of VPP, due to the long distance between DERs and the higher error rate of data transmission than the single machine environment, the synergy effect of BNCM will be more advantageous in the actual situation. The fault tolerance experiment shows that when there are three DERs in BNCM, malicious DERs can be allowed at most. The fault tolerance performance of BNCM is the same as that of PBFT. Comparative experiments on the scale of DERs show that with the increase in the number of DERs, the synergy performance of BNCM is higher than that of PBFT.The innovation of this paper mainly has the following aspects. Through the analysis of the problems existing in the virtual power plant, the virtual power plant trading model is designed, which realizes the transparent distribution of benefits and message transmission in the virtual power plant.Combined with the advantages of blockchain technology, such as decentralization, transparency, contract execution automation, and traceability, this paper designs a virtual power plant blockchain network named VPPBN based on blockchain technology in VPPTM model, which solves the problems of DERs coordination, security, and efficiency in information transmission in VPP.Combined with the actual situation of virtual power plants, this paper designs a convenient and agreed internal DERs coordination mechanism in VPP named blockchain network collaboration mechanism (BNCM). The experimental results show that, compared with the current common PBFT, the collaborative mechanism can achieve shorter collaborative time and better performance.The data in this paper are only obtained in the simulation experiment platform. In the future, we will continue to improve VPPTM and test this model in a real environment. The emergence of new technologies such as side chain  and mult


To the Editor: We thank Arbache et al.,However, the use of tattoo devices may also theoretically lead to the inadvertent delivery of higher concentrations of 5-fluorouracil, an antimetabolite agent, thereby increasing the risk of local and systemic side effects. The settings of the tattooing needle and its depth may need to be changed according to skin thickness. The benefits of using a hypodermic needle are that it can be easily manipulated based on the thickness of the skin of the treatment area and the presence of pinpoint bleeding. We recommend the application of topical anesthesia or local anesthesia for increased patient comfort.Procedures should always be performed under aseptic conditions when tattoo devices, microneedling devices, or a simple hypodermic needle are used for treatment. As mentioned in our article, the use of a hypodermic needle for the treatment of idiopathic guttate hypomelanosis is a simple, cost-effective, office-based procedure that can be easily performed in remote areas or in resource-poor settings that have no access to tattoo machines and consumables.None disclosed.


Nerve discharge is the carrier of information transmission, which can reveal the basic rules of various nerve activities. Recognition of the nerve discharge rhythm is the key to correctly understand the dynamic behavior of the nervous system. The previous methods for the nerve discharge recognition almost depended on the traditional statistical features, and the nonlinear dynamical features of the discharge activity. The artificial extraction and the empirical judgment of the features were required for the recognition. Thus, these methods suffered from subjective factors and were not conducive to the identification of a large number of discharge rhythms.The ability of automatic feature extraction along with the development of the neural network has been greatly improved. In this paper, an effective discharge rhythm classification model based on sparse auto-encoder was proposed. The sparse auto-encoder was used to construct the feature learning network. The simulated discharge data from the Chay model and its variants were taken as the input of the network, and the fused features, including the network learning features, covariance and approximate entropy of nerve discharge, were classified by Softmax. The results showed that the accuracy of the classification on the testing data was 87.5%, which could provide more accurate classification results. Compared with other methods for the identification of nerve discharge types, this method could extract the characteristics of nerve discharge rhythm automatically without artificial design, and show a higher accuracy.The sparse auto-encoder, even neural network has not been used to classify the basic nerve discharge from neither biological experiment data nor model simulation data. The automatic classification method of nerve discharge rhythm based on the sparse auto-encoder in this paper reduced the subjectivity and misjudgment of the artificial feature extraction, saved the time for the comparison with the traditional method, and improved the intelligence of the classification of discharge types. It could further help us to recognize and identify the nerve discharge activities in a new way.The online version contains supplementary material available at 10.1186/s12859-022-04592-3. The neuron is the basic structure and functional unit of the nervous system. The nervous system can receive, transmit, and integrate information through abundant nerve discharge rhythms. Nerve discharge plays a key role in nerve information processing . A typicResearches on the nerve discharge can be helpful to better understand the neuronal coding and neural information processing, and to reveal basics and underlying mechanisms of various neural activities. Different types of nerve discharges contain abundant and complex information . TherefoThe simulation of the neuron model is also usually used to study the nerve discharge mechanism. Neuron models can make the complex influencing factors in the real biological neuron systems to be standardized, concrete and programmed. They are helpful to understand the mechanisms such as the generation of AP, the dynamics change of nerve discharges under different electric current stimulations, and so on . MeanwhiIn both experimental and numerical simulation studies of neural activities, the following methods were often used to identify the nerve discharge rhythms: First, the observed discharge was fitted to the existing pattern by using the statistical descriptor or distribution of discharge activity. Second, based on the visualization of discharge sequence and the statistical histogram of interspike interval (ISI) , nonlineHowever, these methods with unknown parameters of nerve discharge mode were always subjective, as the parameters were usually estimated empirically by researchers or inferred from theoretical simulation data . This maRecently, deep learning rapidly developed and greatly improved the ability of the feature extraction methods in various fields, such as classification, recognition, and segmentation –17. SuchThe purpose of this study is to automatically extract some important features from the testing nerve discharge sequence and effectively classify the nerve discharge rhythms. Considering each nerve discharge sequence as a vector, an effective nerve discharge rhythm classification model based on SAE was proposed in this paper. The SAE was used to construct the feature learning network. The nerve discharge data, which was simulated by the commonly used theoretical neuronal firing model (Chay model) and its variants, were taken as the input of the network. The features learned were classified by the Softmax classifier. To combine supervised and unsupervised learning, two other common characteristics from testing time series were also fused with the features extracted from the SAE. Finally, by some restrictions on the hidden layer, the network could effectively learn the features that can best express the complex sample, the dimensions of the sample, and get a higher accuracy compared with other methods.The simulation data were obtained by the numerical simulation of the deterministic Chay model, stochastic Chay model with global Gauss white noise, stochastic Chay model with global Gauss color noise, stochastic Chay model with potassium ion Gauss white noise, stochastic Chay model with potassium ion Gauss color noise and improved deterministic Chay model. The simulation data set was composed of four types of nerve discharge: periodic discharge (PD) with category label 0, random discharge alternating with two periodic clusters discharge (RD) with category label 1, chaotic discharge (CD) with category label 2, and integer multiple discharge (IMD) with category label 3. Each discharge rhythm consisted of 60 ISI sequences with a length of 1024, of which 40 were training datasets \documentUsing the above models, the discharge sequences could be simulated, and the ISI time series could be transformed from the spike trains.The structure and training parameters of the SAE in this paper were shown in Table N rows and N columns [CM, also known as error matrix, was a standard form of precision evaluation, which expressed in  columns . Each coThrough the CM, the following equation could be obtained:Ni j represented the number of discharge sequences of class i predicted to class j. Ni,i represented the number of discharge sequences of class i predicted to class i.The overall accuracy was the proportion of all correctly judged results in the total observation value of the classification model. We validated the SAE in this paper on the simulation datasets, which contained four basic types of nerve discharge. The total training time was 17 min and the test time was 15 s. With the same data size, the time consumption of traditional artificial classification method was at least 30 min. The CM on the testing datasets classified by only the automatically extracted features from stacked SAE was shown in Table The results showed that the overall accuracy was ACC = 68.75%. All the IMD rhythms were predicted correctly, and almost all the RD rhythms were classified incorrectly. Most of them were classified into CD rhythms. Therefore, we considered adding some important statistical features to the network manually and then observed the classification results. As a classical statistical index to measure the overall error of variables, covariance was widely used in neuroscience –39. In tThe results showed that after incorporating the covariance of discharge data, the overall accuracy was increased from 68.75 to 76.25%. The accuracy of classification of RD rhythms was improved, while the classification results of other rhythms remained unchanged. However, the improvement to the classification result of RD rhythms with covariance fused was limited. As a complexity measure suitable for short data, approximate entropy was frequently applied to feature description of neural discharge data –7. So thThe results showed that after incorporating the ApEn of the discharge data, the overall accuracy was increased from 68.75 to 72.5%. The introduction of ApEn to the features learned from the stacked SAE further improved the classification of RD rhythms. But simultaneously, the number of CD sequences correctly classified declined. Therefore, we merged the ApEn and covariance of discharge data with the features automatically extracted by the stacked SAE and trained them in the Softmax classifier. The testing data were validated, and the CM was showed in Table The results showed that the overall accuracy was significantly increased with a value of 87.5%. Especially, by the fine-tuning with these fused features, the Softmax classifier obtained better training and correctly identified more RD sequences. Thus the results suggested that the fusion of the covariance, ApEn with the features extracted automatically by the stacked SAE, could effectively distinguished and identified the different types of nerve discharge rhythms, which was based on the good classification effect.Table The nerve discharge rhythms, which constructed the classification experiment dataset of the present study, were simulated by the original deterministic Chay model and its variants, including stochastic Chay model with global Gauss white noise, stochastic Chay model with global Gauss color noise, stochastic Chay model with potassium ion Gauss white noise, stochastic Chay model with potassium ion Gauss color noise and improved deterministic Chay model. The improved deterministic Chay model based on the rational biological fact had a strong ability to simulate abundant nerve discharge forms. It could simulate not only the rhythms simulated by the original Chay model, but also the rhythms simulated by the stochastic Chay model with noise. Various complex discharge rhythms would be observed in a same dynamical bifurcation process. This improved model may provide the possibility to understand the unity of the uncertainty and randomness of the neuron system in a new light, and further enrich the understanding of the mechanism of neuron multi-peak discharge modes.In this work, a learning neural network model based on SAE was proposed for the automatic classification of nerve discharge rhythms. In fact, the auto-encoder (AE) itself has powerful abilities of unsupervised feature extraction and has a great advantage in vector processing. The more neurons exit in the hidden layer, the stronger expression ability the model has and the more complex features could be learned. But too many hidden neurons would easily lead to over-fitting, effecting the generalization ability of the network. This was the reason why we chose the stacked sparse auto-encoder. Without sparsity, the input couldn't be correctly expressed in compression. Specifically, if sparse constraints were added to hidden neurons, the auto-encoder neural network could find some interesting structures in the input data even in the case of a large number of hidden neurons, and effectively avoided the over-fitting. The results showed that the feature of the nerve discharge rhythms could be really automatically extracted by the SAE learning, especially accurately for the IMD and PD rhythms. This model exactly classified the nerve discharge rhythms more intelligently with low time-consuming than the previous methods.However, the accuracy obtained by only the SAE model was low as the RD rhythms were not well distinguished from the CD rhythms. In fact, these two discharge rhythms were quite confounding in both previous biological experiment and numerical simulation studies, in which the statistical characteristics and the nonlinear characteristics of these two rhythms are very similar. Considering the combination of supervised and unsupervised classification, a statistical characteristic "covariance" and a nonlinear characteristic "ApEn" often used in previous analysis were fused additively with the features extracted by the second SAE. The covariance was a measurement of dispersion, and revealed the degree of fluctuation among samples. The ApEn could describe the complexity of the discharge sequences, and detect determinate components and random components in the mixed information. The larger value of ApEn indicated the higher complexity and the stronger randomness of the discharge sequence. It was found that the accuracy was significantly improved after incorporating these two features, indicating that the model was helpful for the classification of nerve discharge and improved the reliable quantitative evaluation for the recognition of nerve discharge rhythms.These results also suggested that the classification model based on SAE proposed in this paper still had limitations to identify the nerve discharge rhythms with more disorder, although its advantages were significant. Because of the high complexity, irregularity, nonlinearity and difference of the nerve discharge activities, it was difficult to classify the nerve discharge rhythms by a single method or simple methods. These were also the reason that the time-consuming, empirical and subjective combined methods were used in the past but couldn't get a satisfying result. The final results of this work also provided a new effective approach to classify the more complex or unknown nerve discharge rhythms, that was integrating some simple features to the classification model based on SAE.In this study, an automatic classification of nerve discharge rhythms based on sparse auto-encoder and time series feature was proposed. A stacked sparse auto-encoder was constructed by integrating two SAEs to realize the automatic extraction of nerve discharge features. A Softmax classifier was integrated on the top of the stacked sparse auto-encoder to complete the automatic classification of simulated nerve discharge rhythms. The time series feature of the simulated nerve discharge sequence was fused with the feature extracted from the SAE, and significantly improved the accuracy of classification results. In addition, the improved deterministic Chay model based on the generation mechanism of AP was used to simulate the nerve discharge in this paper. This model showed great ability for simulation and promoted the construction of simulation datasets.SAE was firstly used to classify the basic types of discharge data from a neuron or a single nerve fiber, and achieved automatically extract the characteristics of the nerve discharge. This model reduced the time of artificial judgment, avoided the empiricism of traditional methods, and made the classification of nerve discharge types more intelligent. This work could provide new viewpoints and relatively reliable methods for the recognition of nerve discharge.The final goal of this method proposed is to solve the identification of real biological nerve discharge rhythms. However, the real data from neurophysiological experiment is very difficult to obtain, as experimental instrument, experimental operator, and even experimental reagent may become restrictive factors. With the accumulation of real data, the method proposed in this paper will be validated and improved in the further studies.As the parameters of neuron models, which were set by researchers, were different during simulation, the length of the nerve discharge sequence and the size of the ISI obtained would also be different. Therefore, we normalized the discharge data and controlled the length of the discharge sequence at 1024. This length was also frequently used to analyze the nerve discharged data in many experimental and clinical studies.The stacked sparse automatic encoder used in this paper was made up of two SAEs superimposed . Its netx was mapped by function h of hidden layer was re-mapped by function L2 regular constraint, As an unsupervised nonlinear neural network, the SAE was trying to make the output of the model infinitely close to the model input, so that the feature representation of the model was more robust. The encoding process was from the input layer to the hidden layer, and the decoding process was from the hidden layer to the output layer. In encoding, the input vector For each SAE, Eq.  was usedThen, in the stacked sparse auto-encoder constructed by two SAE models, the output of the SAE on the previous level served as the input of the SAE on the next layer. More and deeper abstract information from data could be obtained. However, the deep information learned from stacked sparse auto-encoder was adaptive and could not classify. Therefore, to effectively classify the learned features, a classifier was integrated at the top of the stacked sparse auto-encoder to complete the training of the classification model. Sometimes, several statistical features would be fused with the learned features by stacked SAE to fine-tune the training of classifier.When SAE was used in the classification field, a robust classifier was often added to the last layer of the network. Since this work was a multi-category classification experiment, Softmax classifier was used for classifying to make the network application more extensive and reach more accurate results .x(1), y(1)}, {x(2), y(2)},…, {x(m), y(m)}}, label y was assigned k different values to represent k categories, and p( y = j | x) was assumed to represent the probability that the sample is determined as category j when input x. Therefore, for a k-class classifier, the classification result was a k-dimensional vector, and the classification result was expressed as:θ was a matrix of k rows, and each row corresponded to the parameters of the classifier. For training sets {{In the above formula: 1{·} was the indicator function, that was, 1{true} = 1, 1{false} = 0.Additional file 1. Training datasets: Training datasets composed of different types of ISI sequences simulated by theoretical models, and the Category labels of Training datasets.Additional file 2. Testing datasets: Testing datasets composed of different types of ISI sequences simulated by theoretical models, and the Category labels of Training datasets.


In this paper, we propose a cooperative strategy-based self-organization mechanism to reconstruct the network. The mechanism includes a comprehensive evaluation algorithm and structure adjustment mechanism. The self-organization mechanism can be carried out simultaneously with the parameter optimization process. By calculating the similarity and independent contribution of normative neurons, the effectiveness of fuzzy rules can be jointly evaluated, and effective structural changes can be realized. Moreover, this mechanism should not set the threshold in advance in practical application. In order to optimize the parameters of SC-IR2FNN, we developed a parameter optimization mechanism based on an interaction strategy. The parameter optimization mechanism based on a joint strategy, namely multilayer optimization engine, can split SC-IR2FNN parameters into nonlinear and linear parameters for joint optimization. The nonlinear parameters are optimized by an advanced two-level algorithm, and the linear parameters are updated with the minimum biological multiplication. Two parameter optimization algorithms optimize nonlinear and linear parameters, reduce the computational complexity of SC-IR2FNN, and improve the learning rate. Using the principal component factor analysis method, seven representative common factors are selected to replace the original variables, which include the profitability factor of the financing enterprise, the solvency factor of the financing enterprise, the profitability factor of the core enterprise, the operation guarantee factor, and the growth ability of the financing enterprise. Factors, supply chain online degree factors, financing enterprise quality, and cooperation factors, can well measure the credit risk of online supply chains. The logistic model shows that the profitability factor of the financing company, the debt repayment factor of the financing company, and the profitability of the core company are three factors that have a significant impact on the credit risk of online supply chain finance. Based on the improved credit calculation model, we developed an online clue risk calculation. This method is based on site conditions and can evaluate credit risk. From the test results, the improved credit scoring system is the result of facing speculative and circular credit fraud and implies that the traders of risk commentators are in a leading position in each electronic device. The results show that risk analysis is effective in any case. At present, artificial intelligence is developing rapidly, and artificial neural network algorithm is the core of research . The comWith the development of the e-commerce industry, its risk issues have become increasingly prominent . Credit The interval class II fuzzy neural network structure asks the problems of solution difficulty and computational complexity to design the interval class II structure. The interval second kind of fuzzy Shinto network is constructed in an organized structure, and the computational complexity is low. B2C electronics led event supply finance credit risk crunch creates a comment price model and defines a comment price index by analyzing the models attributed to factor analysis and logistic back. When calculating the risk of a transaction, according to historical transaction records, the risk calculation is divided into three categories. When calculating the risk, the commodity price, seller credit value, seller credit rating, historical average price, and transaction failure threshold are considered. The calculation of the risk of cycle deception also takes into account the important factor of the deception cycle. At the same time, this article also gives the algorithm idea of seeking the deception cycle. Through experiments on periodic fraud and credit speculation transactions, we can see the impact of commodity prices, credit values, credit ratings, and transaction failure rates on the value of risk. The graphic image drawn by MATLAB illustrates the historical transaction process. The credit scoring method proposed in this article closely links to credit and risk and divides commodity prices into ranges. The scoring of “good reviews” and “bad reviews” changes dynamically with the progress of transactions. This method effectively suppresses periodic fraud by increasing the opportunity cost of users with high credit values. Using the range occupancy rate of commodity prices to score effectively suppresses credit speculation and protects the interests of sellers who only sell low-priced commodities. The experimental results show that the scoring method is effective and feasible.Relevant scholars improved the genetic algorithm and then used the improved algorithm to optimize the weights of the feedforward network so that the training accuracy of the algorithm was significantly improved . ResearcRelevant scholars introduced mutation operators on the basis of the PSO algorithm and then intelligently fused with wavelet neural network, diagnosed transformer faults, and obtained good experimental results . RelevanIt realizes the processing and expression of the ambiguity in data through membership functions and fuzzy neurons. Relevant scholars combine evolutionary computing with neural networks to propose multiple groups of classification algorithms to effectively adopt the membership functions of the fuzzer and defuzzer to the data set and use actual economic data to successfully test . RelatedRelevant scholars use weighted Euclidean distance to improve traditional clustering, use rough sets to reduce attributes, build a neural network based on the clustering results, and establish a new prediction model and have achieved good results in wind speed prediction . The resRelevant scholars have incorporated average purchase prices, transaction density, and historical transaction records into the evaluation index system to comprehensively reflect the credit status of sellers in the transaction process . ResearcOn the basis of previous studies, relevant scholars have investigated the historical transaction volume of sellers and the credit level of evaluation subjects and objects into the credit evaluation model . RelevanResearchers propose an online credit evaluation method that measures the similarity between new transactions and past transactions in the dimensions of product type, number of sold products, and transaction amount, thereby establishing a multidimensional credit evaluation index system . From thThe topology of the neural network has a greater impact on its performance and calculation speed. Compared with the general neural network, the IT2FNN structure is more complex and contains more parameters and faces a greater computational burden in practical applications. In general, more neurons can ensure that the neural network has better performance, but too many neurons will make the calculation of the neural network too complicated, which is not conducive to practical applications. In addition, fewer neurons will reduce the performance of the neural network. Therefore, how to determine the appropriate IT2FNN structure has always been the focus of research.IT2FNN not only has excellent uncertainty processing capabilities but also has adaptive learning capabilities, so it can well realize the identification and control of complex nonlinear systems with uncertainties and time-varying properties. Different from a type 1 fuzzy neural network, IT2FNN adopts an interval type 2 membership function to convert the exact value into an interval fuzzy set so that it can better deal with the uncertainty in the system. In addition, IT2FNN avoids the use of primary and secondary membership functions in the type 2 fuzzy neural network to cause an overly complicated calculation process so that IT2FNN can be better applied in practical engineering.The structure of IT2FNN is shown in The output of IT2FNN is as follows:y′(t) and y″(t) are the lower and upper output bounds of the consequent layer at time t, q(t) is the scale factor, hj(t) is the consequence of the j-th fuzzy rule, wij(t) is the consequent weight of the i-th input corresponding to the j-th regular neuron, and bj(t) is the j-th deviation. In addition, n represents the number of input neurons in the input layer; M represents the number of regular neurons in the regular layer; and fj(t) and fj″(t) are the lower and upper bounds of the activation intensity of regular neurons, respectively. It can be expressed as follows:where In order to improve the performance of IT2FNN, the article proposes a self-constructed interval type 2 fuzzy neural network based on collaborative strategy, including a self-organization mechanism based on collaborative strategy and a parameter optimization mechanism based on collaborative strategy. The self-organization mechanism uses a comprehensive evaluation algorithm and a structure adjustment mechanism to make the structure adjustment of the self-constructed interval type 2 fuzzy neural network coordinate with the parameter optimization process. The comprehensive evaluation algorithm uses the interneuron and interlayer information to comprehensively evaluate the structure of SC-IT2FNN and then uses the structure adjustment mechanism to add and delete fuzzy rules to realize the structural self-organization of SC-IT2FNN. In addition, SC-IT2FNN does not need to preset any threshold when making self-organizing judgments in the learning process. This feature is conducive to practical applications.In IT2FNN, the activation intensity of regular neurons reflects the ability of fuzzy rules. Effective or redundant fuzzy rules can be found so that SC-IT2FNN has a suitable network structure. In the comprehensive evaluation algorithm, the similarity is as follows:The comprehensive evaluation algorithm uses the similarity and independent contribution of rule neurons to evaluate the effectiveness of fuzzy rules. Similarity can indicate the necessity of each fuzzy rule, and independent contribution degree can indicate the effectiveness of each fuzzy rule. Through these two evaluation indicators, it can be judged whether each fuzzy rule in SC-IT2FNN is redundant and effective. At the same time, when calculating the similarity and independent contribution of fuzzy rules, the state value of the rule neuron is used to ensure the accuracy of the similarity and independent contribution.In the design of this coordination mechanism, the connection of fuzzy weights and the independent transmission of degrees are three structural transformation states, which correspond to three stages of adjustment. Structure diagram of adjustment mechanism is shown in Traditional optimization algorithms such as backpropagation algorithm, gradient descent algorithm, Newton method, and Levenberg–Marquardt algorithm are widely used in the parameter optimization of neural networks, but these algorithms have the problems of slow convergence speed and difficulty in obtaining optimal solutions, such as backpropagation algorithm and gradient descent algorithm. The calculation process of Newton's method and Levenberg–Marquardt algorithm is complicated and time-consuming. There are many restrictions in the use process. Due to the numerous parameters of the interval type 2 fuzzy neural network, more calculation time is required in the process of optimizing the parameters.In order to optimize the parameters of SC-IT2FNN, the article proposes a parameter optimization mechanism based on a collaborative strategy, that is, a hierarchical optimization mechanism. In this hierarchical optimization mechanism, the parameters are divided into nonlinear and linear parameters for collaborative optimization. Nonlinear parameters are updated using an improved second-order algorithm, and linear parameters are updated using a least-squares algorithm. This layered optimization mechanism can quickly update the SC-IT2FNN parameters, effectively improve learning accuracy, and reduce computational complexity. The hierarchical optimization mechanism includes two parts: parameter analysis and parameter optimization. In the parameter analysis, the SC-IT2FNN parameters including uncertain mean, standard deviation, scale factor, subsequent weights, and deviations will be divided into two types: nonlinear parameters and linear parameters. The following parameters are nonlinear parameters:N(t) is a nonlinear parameter set containing three nonlinear parameters. Therefore, SC-IT2FNN can be expressed as follows:where ΦThere are two parts: updating nonlinear parameters using a modified second algorithm and optimizing linear parameters using at least two algorithms. But the usual parameter normalizer is the best. In addition, SC-IR2FNN process is optimal for construction and general parameter modification, as well as nonlinear and general linear parameters. The improved quadratic algorithm and least square algorithm are used in the structure transformation process. The SC-IR2FNN network structure is used to adjust and total parameters. We downloaded it based on sales connect policy SC-IR2FNN and learned about the real stacking steps of processing. By designing a self-adjusting learning rate, the difficulty of choosing a learning rate is solved. For SC-IT2FNN, the layered optimization mechanism can effectively improve accuracy and reduce computational complexity. Using multiple sets of sample data to calculate the similarity and independent contribution of the ruled neurons not only avoids frequent adjustments to the network structure that may cause the network to fail to converge but also ensures the accuracy of the network structure evaluation and reduces the computational burden. Therefore, SC-IT2FNN has the characteristics of good generalization performance and fast calculation speed. The specific implementation process of the learning process of SC-IT2FNN is based on collaborative strategy as shown in In terms of supply chain financial risk management, foreign countries have developed a variety of methods to measure supply financial risk. Traditional methods include expert scoring method, fuzzy comprehensive evaluation method, credit rating method, BP neural network, and logistic model. The logistic model has the following advantages:First, the logistic model has relatively simple requirements for data collection and processing and has strong operability; second, the model predicts the probability that the result is between 0 and 1, which can intuitively see the credit risk of the financing enterprise; Third, the model's preconditions are relatively loose and can be applied to continuous or categorical independent variables. Therefore, this paper finally chooses the logistic model to evaluate the credit risk of B2C e-commerce online supply chain finance.p. When the value of p is closer to 1, it indicates that the credit status of the enterprise is better; otherwise, the credit is not good. The logistic model does not theoretically have a critical value, and 0.5 is used as the critical value during model analysis. Therefore, this paper also uses 0.5 as the critical value when studying corporate credit risk. If the calculated value is greater than or equal to 0.5, the company's credit is considered good; otherwise, the company's credit is bad.Suppose the conditional probability of credit risk in financing enterprises is This article takes an e-commerce company's participation in online supply chain finance as an example, applies the evaluation index system established above, comprehensively evaluates the company's credit status based on the online supply chain financing model, and compares and analyzes the results. The example quantitatively analyzes the effectiveness of the logistic model on the financial credit risk control of the B2C e-commerce online supply chain.E-commerce is one of the previous generation B2C online shopping platforms. There are many kinds of household appliances, such as traditional household appliances, 3C household appliances, and daily necessities. Online finance and e-commerce is one of the main directions. The supply of financial consumption platoon, investment asset management, tips, ointment, prepaid cards, preleasing, private equity financing, and other businesses have been launched. Its attempts in the internet field have further expanded the influence of an e-commerce company and realized collaboration and cooperation with upstream and downstream enterprises for a win-win situation.The supply chain finance business of an e-commerce company is developed on the basis of its one-stop supply chain service platform. Customers can realize the transaction and financing process on the platform. However, as the business layout expands, an e-commerce company has also encountered applications. In order to help an e-commerce company better cope with the credit risk of small- and medium-sized enterprises in online supply chain finance, this article selected 40 e-commerce companies on an e-commerce platform and upstream and downstream companies in the supply chain from the Wonder database as samples. We take the lower value of the interest-bearing debt ratio of each industry in 2020 as the limit. Among the existing 40 sample companies, 6 companies have breached the contract, and 34 companies have not breached the contract.When using the logistic model to model the data, it is required that there should be no collinearity between the independent variables. Therefore, we first use factor analysis to standardize the variables and select representative independent variables to replace the original indicator variables. The newly acquired common factors are linear combinations of the original variables.First, we need to check tapinle's data with Bartlett to see if the indicators selected by individuals are factor analysis. If KMO is 0.9 or greater, factor analysis is considered to be used. If it is 0.8–0.9, it is open. 0.6–0.8 spacing is normal. At one level, we then do factor analysis. The results of the Bartlett and KMO tests are shown in The KMO test coefficient in this article is greater than 0.5, and the partial correlation between variables is strong. The Sig. of the Bartlett sphere test is less than 1%, indicating that we can do factor analysis.We have to substitute the logistic regression model for the seven principal components. The sample companies are used as dependent variables, and the seven principal component factors are used as independent variables. The factors F1∼F7 are substituted into the logistic model for regression analysis using the entry method, that is, all variables are substituted into the equation at one time. The results of using SPSS software are shown in It can be seen from the regression results that the final explanatory variables of the seven models are all retained, and their Sig. values are all less than 0.5, indicating that these seven variables are very convincing for predicting the credit risk of financing enterprises.The seven principal component factors calculated by using SPSS software are used as independent variables of the model, and two types of enterprises  are selected as dependent variables. The sample results are shown in As shown in As shown in Here, we select a set of experimental data with this feature to compare and analyze the difference between the credit scoring method proposed in this article and Taobao's credit scoring method. The experimental results are shown in We select realistic data that meet this situation for analysis, and the experimental results are shown in Under Taobao's credit scoring method, although the seller did not cheat after the credit speculation, the credit at this time has been greatly reduced, and the buyer is easily confused by the credit value. It can be seen from From the empirical results, it can be seen that indicators such as net sales interest rate, return on net assets, and net profit rate of total assets are positively correlated with the profitability factor of financing enterprises. At the same time, the profitability factor of financing enterprises is positively correlated with the compliance probability of online supply chain finance SMEs. Therefore, the higher the net profit margin of the financing company's sales and the net profit margin of the financing company's total assets, the lower the credit risk of SMEs participating in online supply chain financing. The solvency of financing companies is negatively related to the probability of online credit risk. That is, the stronger the solvency of SMEs, the smaller the credit risk in their online supply chain financing business. From the empirical results, we can see that the current ratio, quick ratio, and interest coverage are positively correlated with solvency, and the debt-to-asset ratio is negatively correlated with solvency. Therefore, the higher the current ratio, quick ratio, and interest protection multiple, the stronger the solvency of SMEs, and the lower the credit risk of their participation in online supply chain financing; the higher the debt-to-asset ratio, the lower their solvency. The online degree factor is negatively related to the probability of the occurrence of financial credit risk in the online supply chain. That is to say, the stronger the online degree of this business, the smaller the credit risk of this online supply chain financing business. It can be seen from the component score coefficient matrix that the indicators reflecting the online degree of the supply chain, such as the electronic order processing capacity and the degree of information sharing, are positively correlated with the online degree factor of supply chain finance, and the online degree factor is related to the compliance of small- and medium-sized enterprises. The probability is positively correlated. Therefore, the higher the electronic order processing capability and the degree of information sharing, the lower the credit risk of SMEs participating in online financing business. When dealing with the problem of using low-priced commodities for credit speculation, the improved credit scoring method considers prices in different segments, and the increase in credit value is related to the ratio of price segments in all price segments. The closer the price range of the goods sold, the faster the credit growth of sellers. Once the price range of the goods sold by the seller fluctuates sharply, the growth of the credit value slows down. In this way, credit hype can be curbed while protecting the interests of sellers who only sell low-priced goods. Comparative experiments show that the self-constructed interval type 2 fuzzy neural network is effective in suppressing credit speculation. When dealing with the problem of periodic fraud, the improved credit scoring method sets different credit levels according to the level of credit value. In different credit levels, different deduction coefficients are set. A high credit rating sets a high deduction factor to increase the opportunity cost of periodic deception. Through this kind of punishment, the cycle of deception is effectively curbed. The comparative experiment shows that the self-constructed interval type 2 fuzzy neural network is effective in punishing the periodic deception.