Neuron positioning method and device, equipment and storage medium
Through stepping scanning and signal preprocessing technology, combined with point cloud map iteration and triangulation algorithm, the efficient and high accuracy of neuronal positioning is achieved, solving the problem of insufficient positioning efficiency and accuracy in traditional technologies.
Patent Information
- Application Number
- CN202510174791.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-18
- Publication Date
- 2025-05-23
AI Technical Summary
The existing neuron positioning technology has a long cycle in chronic experiments, and the localization effect in acute experiments is poor. In addition, traditional high-density electrode arrays are costly and resource consumption is large. There is a lack of effective algorithm for signal positioning of low-density electrodes, and the accuracy and efficiency of multi-neurons in three-dimensional space are poor.
Spike signals are collected through step scanning, the initial Spike signals are processed based on signal preprocessing rules, the first classification operation is performed for initial positioning, the point cloud map is iterated, the second classification is performed using spatial clustering features, and the target position information of the neuron is determined in combination with the triangulation algorithm.
It improves the accuracy and efficiency of neuronal positioning, and can use lower density electrodes to complete the positioning work required for high-density electrodes, which is suitable for resource-constrained environments and meets actual scientific research needs.
Smart Images
Figure CN120032172A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of brain science, and in particular to a neuron positioning method, device, equipment and storage medium. Background Art
[0002] In the field of neuron localization research, the existing mainstream solutions have significant limitations. On the one hand, although it has been used in both chronic and acute experiments, in chronic experimental scenarios, due to the technical limitations of chronic experiments themselves, the experimental cycle is relatively long, making it difficult for research results to be produced and applied in a timely manner. In acute experiments, although existing algorithms can be used, the positioning effect is unsatisfactory. On the other hand, traditional positioning algorithms rely on high-density electrode arrays, which have high manufacturing costs and require a lot of experimental time, which is almost impossible to implement in resource-constrained environments. Moreover, there is currently a lack of effective algorithms for the positioning of low-density electrode acquisition signals, and the accuracy and efficiency of multi-neuron localization in three-dimensional space are poor, which makes it difficult to meet actual scientific research needs.
[0003] Therefore, how to improve the accuracy and efficiency of neuron localization is a technical problem that needs to be solved urgently. Summary of the invention
[0004] In view of this, the purpose of the present invention is to provide a neuron localization method, device, equipment and storage medium, which can improve the accuracy and efficiency of neuron localization. The specific scheme is as follows:
[0005] In a first aspect, the present application provides a neuron localization method, comprising:
[0006] Determine each target scanning position in the target neuron area, scan each of the target scanning positions by step scanning, and process each scanned initial Spike signal based on a signal preprocessing rule to obtain each processed Spike signal;
[0007] Performing a first classification operation on each of the processed Spike signals, and performing an initial positioning operation on the neurons based on the obtained first classified Spike signals to obtain initial position information of each neuron;
[0008] Iterating the initial point cloud map based on the initial position information to obtain a target point cloud map, and performing a second classification operation on the first classified Spike signal using the spatial clustering feature of the target point cloud map to obtain a second classified Spike signal;
[0009] The target position information of each neuron in the target neuron area is determined by using a preset triangulation algorithm based on neuron positioning and the second classified Spike signal.
[0010] Optionally, the processing of each scanned initial Spike signal based on a signal preprocessing rule to obtain each processed Spike signal includes:
[0011] Based on the preset signal preprocessing tool, each scanned initial Spike signal is subjected to denoising and collision suppression processing to obtain each processed Spike signal.
[0012] Optionally, performing a first classification operation on each of the processed Spike signals, and performing an initial positioning operation on the neurons based on the obtained first classified Spike signals to obtain initial position information of each neuron, includes:
[0013] Performing a first classification operation on each of the processed Spike signals to obtain a corresponding first classified Spike signal;
[0014] The neurons in the target neuron area are located using the preset triangulation algorithm based on neuron positioning and the first classified Spike signal to obtain initial position information of each neuron.
[0015] Optionally, iterating the initial point cloud image based on the initial position information to obtain the target point cloud image includes:
[0016] The initial position of each neuron in the current initial position information is obtained, and the initial point cloud image is iterated using each of the initial positions to obtain a target point cloud image.
[0017] Optionally, performing a second classification operation on the first classified Spike signal using the spatial clustering feature of the target point map to obtain a second classified Spike signal includes:
[0018] Determine the spatial clustering characteristics of the target point cloud image based on the DBSCAN clustering algorithm;
[0019] A second classification operation is performed on the first classified Spike signal using the spatial clustering feature, the signal emission frequency of the neuron, and the phase locking information to obtain a second classified Spike signal.
[0020] Optionally, the determining the target position information of each neuron in the target neuron area by using a preset triangulation algorithm based on neuron positioning and the second classified Spike signal includes:
[0021] The target position of each neuron in the target neuron area is calculated based on a preset triangulation algorithm based on neuron positioning and the second classified Spike signal, and the target position is used to generate corresponding target position information.
[0022] In a second aspect, the present application provides a neuron localization device, comprising:
[0023] A signal processing module, used to determine each target scanning position in the target neuron area, scan each of the target scanning positions by step scanning, and process each scanned initial Spike signal based on a signal preprocessing rule to obtain each processed Spike signal;
[0024] A first neuron positioning module, used to perform a first classification operation on each of the processed Spike signals, and perform an initial positioning operation on the neurons based on the obtained first classified Spike signals to obtain initial position information of each neuron;
[0025] A signal classification module, configured to iterate the initial point cloud map based on the initial position information to obtain a target point cloud map, and perform a second classification operation on the first classified Spike signal using the spatial clustering feature of the target point cloud map to obtain a second classified Spike signal;
[0026] The second neuron positioning module is used to determine the target position information of each neuron in the target neuron area by using a preset triangulation algorithm based on neuron positioning and the second classified Spike signal.
[0027] In a third aspect, the present application provides an electronic device, including:
[0028] Memory, used to store computer programs;
[0029] A processor is used to execute the computer program to implement the aforementioned neuron localization method.
[0030] In a fourth aspect, the present application provides a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the aforementioned neuron localization method is implemented.
[0031] In the present application, each target scanning position in the target neuron area is determined, each target scanning position is scanned by a step scanning method, and each scanned initial Spike signal is processed based on a signal preprocessing rule to obtain each processed Spike signal; a first classification operation is performed on each processed Spike signal, and an initial positioning operation is performed on the neuron based on the obtained first classified Spike signal to obtain the initial position information of each neuron; the initial point cloud map is iterated based on the initial position information to obtain a target point cloud map, and a second classification operation is performed on the first classified Spike signal using the spatial clustering features of the target point map to obtain a second classified Spike signal; and the target position information of each neuron in the target neuron area is determined using a preset triangulation algorithm based on neuron positioning and the second classified Spike signal. As can be seen from the above, the present application determines each target scanning position of the target neuron area, collects Spike signals in a step-scanning manner, and obtains processed Spike signals according to signal preprocessing rules; performs a first classification on the processed Spike signals, uses the first classification Spike signals to preliminarily locate neurons, and obtains initial position information; iterates the initial point cloud map based on the initial position information to obtain the target point cloud map, and uses the spatial clustering features of the target point cloud map to secondary classify the first classification Spike signals to obtain the second classification Spike signals; uses the preset neuron positioning triangulation algorithm and the second classification Spike signals to clarify the target position information of neurons in the target neuron area. In this way, the present application can obtain Spike signals at multiple target scanning positions, that is, it provides a large amount of data for neuron positioning, and repeatedly uses a large amount of data in the process of iterating the initial point cloud map, thereby improving the accuracy of neuron positioning. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0033] Figure 1 This is a flow chart of a neuron localization method disclosed in this application;
[0034] Figure 2 A schematic diagram of a specific neuron localization method disclosed in this application;
[0035] Figure 3 This is a schematic diagram of the structure of a neuron localization device disclosed in this application;
[0036] Figure 4 This is a structural diagram of an electronic device disclosed in this application. DETAILED DESCRIPTION
[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0038] In the existing scheme, on the one hand, although it is used in both chronic experiments and acute experiments, in the chronic experiment scenario, due to the technical limitations of the chronic experiment itself, the experimental cycle is relatively long, making it difficult for the research results to be produced and applied in a timely manner. In acute experiments, although the existing algorithms can be used, the positioning effect is unsatisfactory. On the other hand, the traditional positioning algorithm relies on the high manufacturing cost of high-density electrode arrays, which requires a lot of experimental time, which is almost impossible to implement in a resource-constrained environment. Moreover, there is a lack of effective algorithms for the positioning of low-density electrode acquisition signals, and the accuracy and efficiency of multi-neuron positioning in three-dimensional space are poor, which is difficult to meet the actual scientific research needs. To this end, the present application provides a neuron positioning method, device, equipment and storage medium, which can not only improve the accuracy and efficiency of neuron positioning, but also break the limitations of traditional electrode use, that is, the present application can use lower density, low-flux electrodes to complete the work that could only be achieved by high-flux, high-density electrodes in the past, providing a more flexible and economical technical means for neuron positioning.
[0039] See also Figure 1 As shown, an embodiment of the present invention discloses a neuron positioning method, including:
[0040] Step S11, determining each target scanning position in the target neuron area, scanning each target scanning position by step scanning, and processing each scanned initial Spike signal based on a signal preprocessing rule to obtain each processed Spike signal.
[0041] In this embodiment, it is first necessary to determine the target neuron area. In the process of determination, on the one hand, the approximate range can be framed according to the biological function focused on by the research topic. For example, if the neuronal activity related to visual perception is studied, the area near the visual cortex of the brain and its associated neural pathways can be used as the target neuron area. If the motor control mechanism is studied, the motor area of the brain and the brain area that cooperates with it can be used as the target neuron area. On the other hand, the research results of predecessors, anatomical atlases, and the information on hot spots of brain activity obtained in advance by imaging methods can provide a certain reference for the determination of the target neuron area.
[0042] After determining the target neuron area, the target scanning position is further determined. The target scanning position can be determined based on the distribution characteristics and activity patterns of neurons in the target neuron area and the specific needs of the research. It is understandable that neurons are not evenly distributed in the brain, and there are differences in neuron density, functional connectivity, etc. in different areas. For areas where neurons are densely distributed and have complex functions, in order to more accurately capture neuron activity information, the target scanning position will be set more densely to ensure that sufficiently rich and detailed signals can be obtained. In areas where neurons are relatively sparsely distributed, the spacing between scanning positions can be appropriately increased.
[0043] It should be noted that in this embodiment, electrodes with fewer channels and lower density are used to collect initial Spike signals in a step-scanning manner. Although such electrodes are inferior to traditional high-end electrodes in terms of channel number and distribution density, they have outstanding cost-effectiveness and are suitable for large-scale, normalized neuron localization research. In addition, the step-scanning method can capture subtle changes in Spike signals emitted by neurons at different times and different tiny spatial locations, avoiding the omission of important information due to excessively large scanning ranges and overly extensive signal collection.
[0044] Furthermore, in this embodiment, based on the preset signal preprocessing tool, each scanned initial Spike signal is subjected to denoising and collision suppression processing to obtain each processed Spike signal. Specifically, the Spike denoising in the denoising process can, to a certain extent, eliminate abnormal Spike signals caused by external electromagnetic interference, electrode micro-tremor, etc., and restore the true discharge of neurons, while the collision suppression processing can effectively separate the superimposed and confused signals caused by the successive discharge of multiple neurons in a short period of time, ensuring that each processed Spike signal is pure and accurate. Denoising and collision suppression processing jointly guarantee the clarity and accuracy of the processed Spike signal, laying a solid foundation for subsequent neuron positioning and analysis. Among them, the preset signal preprocessing tool can be a neural network denoiser specially trained according to different types of electrodes.
[0045] Step S12: performing a first classification operation on each of the processed Spike signals, and performing an initial positioning operation on the neurons based on the obtained first classified Spike signals to obtain initial position information of each neuron.
[0046] In this embodiment, after obtaining each processed Spike signal, a first classification operation (i.e., Spike Sorting, Spike signal classification) is performed on each processed Spike signal to obtain a corresponding first classified Spike signal. It should be noted that the first classification operation is a semi-automatic classification operation, which is a process of decomposing the processed Spike signal into single neuron activity information, and the discharge activity of the neuron needs to be recorded. In this process, relevant researchers need to select the Spike signal of the neuron near the tip of the electrode and preliminarily distinguish which Spike signal corresponds to which neuron. Semi-automatic classification operations can improve processing speed and classification accuracy, while reducing the workload of relevant researchers. By combining manual and automated methods, the first classification operation can be performed more efficiently.
[0047] After obtaining the first classified Spike signal, in this embodiment, the neurons in the target neuron area are located using the preset triangulation algorithm based on neuron positioning and the first classified Spike signal to obtain the initial position information of each neuron. It should be noted that when the neuron discharges and generates Spike signals, these signals will propagate outward at a certain speed. Due to the difference in the distance between different electrodes and each neuron, the time it takes for the signal to arrive at each electrode will be slightly different. At this time, the first classified Spike signal, the distance between the neuron and the electrode can be used as input and substituted into the preset triangulation algorithm based on neuron positioning. Through a series of calculation processes, the initial position information of each neuron is determined.
[0048] In addition, when using the preset triangulation algorithm based on neuron positioning for positioning, the algorithm needs to input the voltage of the neuron Spike signal, where the calculation formula of the voltage is as follows:
[0049] ;
[0050] Where V is the voltage of the neuron Spike signal, a is the signal amplitude of the neuron Spike signal, b is a fixed parameter independent of the neuron, and r is the distance between the electrode and the neuron.
[0051] Step S13: iterate the initial point cloud map based on the initial position information to obtain a target point cloud map, and perform a second classification operation on the first classified Spike signal using the spatial clustering features of the target point cloud map to obtain a second classified Spike signal.
[0052] In this embodiment, the initial point cloud map is iterated by obtaining the initial position information of each target scanning position. Specifically, first, based on the initial position information, the electrode sites where the signals sent by the neurons can be detected are determined, and the amplitudes of the signals received at these electrode sites are recorded. As the electrodes move in a step-by-step manner, the signals sent by the neurons are continuously detected and captured, thereby obtaining more position information related to the neurons, including changes in signal strength, signal propagation direction, etc. In this process, each time a new set of information about the position of the neuron and related signals is obtained, the initial point cloud map is adjusted accordingly. After multiple rounds of such iterative processing, the point cloud map becomes clearer and more accurate, and finally the target point cloud map is obtained.
[0053] In a specific embodiment, the spatial clustering features of the target point cloud are determined based on the DBSCAN clustering algorithm. The DBSCAN clustering algorithm can automatically identify dense and sparse areas in the target point cloud, and classify points with similar spatial distribution characteristics into the same cluster according to a set density threshold. That is, by running the DBSCAN algorithm on the target point cloud, spatial clustering features such as neuron cell body clustering areas and signal clustering bands in the direction of axon branch extension can be captured.
[0054] Furthermore, the second classification operation is performed on the first-classified Spike signal using the spatial clustering features, the signal emission frequency of neurons, and the phase locking information to obtain the second-classified Spike signal. It should be noted that the signal emission frequency of neurons reflects their activity level. Neurons of different functions and types have specific emission frequency ranges under different stimulation conditions, and the phase locking information reflects the time synchronization relationship between neuronal discharge and external stimulation or other neuronal discharge, which is an important representation of the collaborative work of neurons. Therefore, combining the spatial clustering features with the electrical characteristics of neurons can further subdivide the first-classified Spike signal, determine which Spike signals belong to the same neuron, and thus obtain a more accurate second-classified Spike signal.
[0055] Step S14: Determine the target position information of each neuron in the target neuron area by using a preset triangulation algorithm based on neuron positioning and the second classified Spike signal.
[0056] In this embodiment, after obtaining the second classified Spike signal, the target position of each neuron in the target neuron area is calculated based on the preset triangulation algorithm based on neuron positioning and the second classified Spike signal, and the corresponding target position information is generated using the target position. That is, after completing the relevant calculations, the target position information of each neuron in the target neuron area is obtained. These position information may include the three-dimensional coordinates of each neuron in the target neuron area. And the target position information can present the layout of neurons in the brain area to a certain extent, and can provide an indispensable basic support for the understanding of neuronal circuits in the field of neuroscience research.
[0057] As can be seen from the above, the present application determines each target scanning position of the target neuron area, collects Spike signals in a step-scanning manner, and obtains processed Spike signals according to signal preprocessing rules; performs a first classification on the processed Spike signals, uses the first classification Spike signals to preliminarily locate neurons, and obtains initial position information; iterates the initial point cloud map based on the initial position information to obtain the target point cloud map, and uses the spatial clustering features of the target point cloud map to secondary classify the first classification Spike signals to obtain the second classification Spike signals; uses the preset neuron positioning triangulation algorithm and the second classification Spike signals to clarify the target position information of neurons in the target neuron area. In this way, the present application can obtain Spike signals at multiple target scanning positions, that is, it provides a large amount of data for neuron positioning, and repeatedly uses a large amount of data in the process of iterating the initial point cloud map, thereby improving the accuracy of neuron positioning.
[0058] Combine the following Figure 2 The schematic diagram shown specifically illustrates the technical solution of the embodiment of the present application.
[0059] Specifically, first of all, the original signal (that is, the initial Spike signal) collected by the electrode at each position during the scanning process needs to be processed separately. Specifically, to ensure the signal quality, the first step is to pre-process the original signal, which includes training a dedicated neural network denoiser based on different types of electrodes. The denoising process mainly covers two core parts: Spike denoising and collision suppression.
[0060] Next, the processed Spike signals are semi-automatically classified, and the signals collected by the electrodes at that position are preliminarily classified (i.e., the first classification operation). In addition, the neurons are roughly located for the first time using the triangulation method (i.e., a pre-set triangulation algorithm based on neuron location).
[0061] Furthermore, the rough positioning results (i.e., initial position information) collected by the electrode at each scanning position (i.e., target scanning position) are iterated onto the same point cloud image (i.e., initial point cloud image), thereby obtaining a target point cloud image to realize integrated analysis of multi-site signals. Figure 2 As shown in the figure, there are more than 3 scanning positions, then each rough positioning result at these scanning positions is iterated into the point cloud map. In addition, in order to further improve the signal accuracy of the point cloud map, it is planned to perform Poisson denoising on the point cloud map regularly to weaken the background interference and noise. Subsequently, the DBSCAN clustering algorithm is used to group and analyze the preliminarily classified Spide signals (that is, the second classification operation), and the firing frequency and phase locking of neurons are used for auxiliary judgment to determine which Spide signals belong to the same neuron, thereby obtaining the Spike signal after the second classification (that is, the binary classification Spike signal).
[0062] Finally, the precise position coordinates of the neuron are determined using the Spike signal after the second classification and the triangulation method. In addition, it should be emphasized that in this embodiment, only the triangulation method is used in the process of obtaining a rough positioning result in the first positioning, while the triangulation method and the DBSCAN clustering algorithm are used in the process of obtaining a precise positioning result in the second positioning.
[0063] Accordingly, see Figure 3 As shown, the embodiment of the present application provides a neuron locating device, comprising:
[0064] The signal processing module 11 is used to determine each target scanning position in the target neuron area, scan each target scanning position by step scanning, and process each scanned initial Spike signal based on the signal preprocessing rule to obtain each processed Spike signal;
[0065] A first neuron positioning module 12, configured to perform a first classification operation on each of the processed Spike signals, and perform an initial positioning operation on the neurons based on the obtained first classified Spike signals, so as to obtain initial position information of each neuron;
[0066] A signal classification module 13 is used to iterate the initial point cloud map based on the initial position information to obtain a target point cloud map, and perform a second classification operation on the first classified Spike signal using the spatial clustering feature of the target point cloud map to obtain a second classified Spike signal;
[0067] The second neuron positioning module 14 is used to determine the target position information of each neuron in the target neuron area by using a preset triangulation algorithm based on neuron positioning and the second classified Spike signal.
[0068] As can be seen from the above, the present application determines each target scanning position of the target neuron area, collects Spike signals in a step-scanning manner, and obtains processed Spike signals according to signal preprocessing rules; performs a first classification on the processed Spike signals, uses the first classification Spike signals to preliminarily locate neurons, and obtains initial position information; iterates the initial point cloud map based on the initial position information to obtain the target point cloud map, and uses the spatial clustering features of the target point cloud map to secondary classify the first classification Spike signals to obtain the second classification Spike signals; uses the preset neuron positioning triangulation algorithm and the second classification Spike signals to clarify the target position information of neurons in the target neuron area. In this way, the present application can obtain Spike signals at multiple target scanning positions, that is, it provides a large amount of data for neuron positioning, and repeatedly uses a large amount of data in the process of iterating the initial point cloud map, thereby improving the accuracy of neuron positioning.
[0069] In a specific implementation, the signal processing module 11 includes:
[0070] The signal processing unit is used to perform denoising and collision suppression processing on each scanned initial Spike signal based on a preset signal preprocessing tool to obtain each processed Spike signal.
[0071] In a specific embodiment, the first neuron location module 12 includes:
[0072] a first signal classification unit, configured to perform a first classification operation on each of the processed Spike signals to obtain a corresponding first classified Spike signal;
[0073] The first neuron positioning unit is used to position neurons in the target neuron area by using the preset triangulation algorithm based on neuron positioning and the first classified Spike signal to obtain initial position information of each neuron.
[0074] In a specific implementation, the signal classification module 13 includes:
[0075] The point cloud image determination unit is used to obtain the initial position of each neuron in the current initial position information, and iterate the initial point cloud image using each of the initial positions to obtain a target point cloud image.
[0076] In a specific implementation, the signal classification module 13 includes:
[0077] A feature determination unit, used to determine the spatial clustering features of the target point cloud image based on a DBSCAN clustering algorithm;
[0078] The second signal classification unit is used to perform a second classification operation on the first classified Spike signal by using the spatial clustering feature, the signal emission frequency of the neuron and the phase locking information to obtain a second classified Spike signal.
[0079] In a specific embodiment, the second neuron location module 14 includes:
[0080] A position determination unit is used to calculate the target position of each neuron in the target neuron area based on a preset triangulation algorithm based on neuron positioning and the second classified Spike signal, and generate corresponding target position information using the target position.
[0081] Furthermore, the present application also discloses an electronic device. Figure 4 : is a structural diagram of an electronic device 20 shown according to an exemplary embodiment, and the content in the figure cannot be regarded as any limitation on the scope of use of this application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input and output interface 25 and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the neuron localization method disclosed in any of the aforementioned embodiments. In addition, the electronic device 20 in this embodiment may specifically be an electronic computer.
[0082] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and the external device, and the communication protocol it follows is any communication protocol that can be applied to the technical solution of the present application, and is not specifically limited here; the input and output interface 25 is used to obtain external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs and is not specifically limited here.
[0083] In addition, the memory 22 as a carrier for resource storage may be a read-only memory, a random access memory, a disk or an optical disk, etc. The resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage method may be temporary storage or permanent storage.
[0084] The operating system 221 is used to manage and control the hardware devices and computer program 222 on the electronic device 20, which can be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program that can be used to complete the neuron localization method performed by the electronic device 20 disclosed in any of the aforementioned embodiments, the computer program 222 can further include a computer program that can be used to complete other specific tasks.
[0085] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, the aforementioned disclosed neuron localization method is implemented. The specific steps of the method can refer to the corresponding contents disclosed in the aforementioned embodiments, and will not be repeated here.
[0086] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.
[0087] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.
[0088] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
[0089] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0090] The technical solution provided by the present application is introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technicians in this field, according to the idea of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.
Claims
1. A neuron localization method, characterized in that: include: Determine each target scanning position in the target neuron area, scan each of the target scanning positions by step scanning, and process each scanned initial Spike signal based on a signal preprocessing rule to obtain each processed Spike signal; Performing a first classification operation on each of the processed Spike signals, and performing an initial positioning operation on the neurons based on the obtained first classified Spike signals to obtain initial position information of each neuron; Iterating the initial point cloud map based on the initial position information to obtain a target point cloud map, and performing a second classification operation on the first classified Spike signal using the spatial clustering feature of the target point cloud map to obtain a second classified Spike signal; The target position information of each neuron in the target neuron area is determined by using a preset triangulation algorithm based on neuron positioning and the second classified Spike signal.
2. The neuron localization method according to claim 1, characterized in that: The method of processing each scanned initial Spike signal based on the signal preprocessing rule to obtain each processed Spike signal includes: Based on the preset signal preprocessing tool, each scanned initial Spike signal is subjected to denoising and collision suppression processing to obtain each processed Spike signal.
3. The neuron localization method according to claim 1, characterized in that: The step of performing a first classification operation on each of the processed Spike signals, and performing an initial positioning operation on the neurons based on the obtained first classified Spike signals to obtain initial position information of each neuron, includes: Performing a first classification operation on each of the processed Spike signals to obtain a corresponding first classified Spike signal; The neurons in the target neuron area are located using the preset triangulation algorithm based on neuron positioning and the first classified Spike signal to obtain initial position information of each neuron.
4. The neuron localization method according to any one of claims 1 to 3, characterized in that: The iterating the initial point cloud image based on the initial position information to obtain the target point cloud image includes: The initial position of each neuron in the current initial position information is obtained, and the initial point cloud image is iterated using each of the initial positions to obtain a target point cloud image.
5. The neuron localization method according to claim 1, characterized in that: The step of performing a second classification operation on the first classified Spike signal by using the spatial clustering feature of the target point map to obtain a second classified Spike signal includes: Determine the spatial clustering characteristics of the target point cloud image based on the DBSCAN clustering algorithm; A second classification operation is performed on the first classified Spike signal using the spatial clustering feature, the signal emission frequency of the neuron, and the phase locking information to obtain a second classified Spike signal.
6. The neuron localization method according to claim 1, characterized in that: The method of determining the target position information of each neuron in the target neuron area by using a preset triangulation algorithm based on neuron positioning and the second classified Spike signal includes: The target position of each neuron in the target neuron area is calculated based on a preset triangulation algorithm based on neuron positioning and the second classified Spike signal, and the target position is used to generate corresponding target position information.
7. A neuron locating device, characterized in that: include: A signal processing module, used to determine each target scanning position in the target neuron area, scan each of the target scanning positions by step scanning, and process each scanned initial Spike signal based on a signal preprocessing rule to obtain each processed Spike signal; A first neuron positioning module, used to perform a first classification operation on each of the processed Spike signals, and perform an initial positioning operation on the neurons based on the obtained first classified Spike signals to obtain initial position information of each neuron; A signal classification module, configured to iterate the initial point cloud map based on the initial position information to obtain a target point cloud map, and perform a second classification operation on the first classified Spike signal using the spatial clustering feature of the target point cloud map to obtain a second classified Spike signal; The second neuron positioning module is used to determine the target position information of each neuron in the target neuron area by using a preset triangulation algorithm based on neuron positioning and the second classified Spike signal.
8. The neuron locating device according to claim 7, characterized in that: The signal classification module comprises: The point cloud image determination unit is used to obtain the initial position of each neuron in the current initial position information, and iterate the initial point cloud image using each of the initial positions to obtain a target point cloud image.
9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the neuron localization method according to any one of claims 1 to 6.
10. A computer-readable storage medium, characterized in that: Used to store a computer program; wherein, when the computer program is executed by a processor, the neuron localization method according to any one of claims 1 to 6 is implemented.