Neuron data processing method and system and storage medium

Abstract

The invention provides a neuron data processing method, a neuron data processing system and a storage medium. The neuron data processing method comprises the following steps: acquiring space coordinates of axons and dendrites of a plurality of neurons; according to the first space coordinate of the axon of the first neuron and the second space coordinate of the dendrite of the second neuron, whether space interaction exists between the axon of the first neuron and the dendrite of the second neuron is judged; and in response to a judgment result that interaction exists between the axon of the first neuron and the dendrite of the second neuron, determining a connection relationship between the first neuron and the second neuron, and judging that the first neuron is an upstream neuron of the second neuron and the second neuron is a downstream neuron of the first neuron. According to the processing method, the upstream and downstream relationship of the neurons can be analyzed and judged based on the morphological structure of the neurons, so that a brain structure and a brain network connection rule are efficiently represented, an accurate navigation map is provided for deep analysis of brain functions and brain diseases, and an important reference is provided for design of a novel brain-like intelligent system.

Description

Technical Field

[0001] The present invention relates to data analysis technology of neurons and brain regions, and in particular to a method for processing neuron data, a system for processing neuron data, and a computer-readable storage medium. Background Art

[0002] Better knowledge and understanding of the brain can not only help treat brain diseases, but also provide new ideas for the development of artificial intelligence. Neurons are one of the most basic components of the brain. The connections between them form a complex neural network, which constitutes the structural basis of brain function. Therefore, clarifying the complex connection patterns and basic rules between neurons is of great significance for understanding the organizational structure and information processing mechanism of the brain.

[0003] The information exchange between different brain regions of the cerebral cortex depends on the long-range projection of neurons. Neurons with different projection patterns are often involved in different brain functions. Therefore, studying the projection patterns of neurons and their basic laws is of great significance for analyzing the organizational structure and information processing mechanism of the brain. The mutual projection between neurons is the basis for the brain to perform various functions. Clarifying the connection information between upstream and downstream neurons is often the first step in conducting research on the function of neural circuits.

[0004] Therefore, this field is in urgent need of a neuronal data processing technology that can analyze and determine the principles and feasibility of upstream and downstream relationships of neurons, so as to more systematically and comprehensively analyze the working principles of the brain, more effectively mine brain map data, and then more effectively understand the brain structure and brain network connection rules, provide accurate navigation maps for in-depth analysis of brain functions and brain diseases, and provide important references for the design of new brain-like intelligent systems. Summary of the invention

[0005] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or decisive elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides a neuron data processing technology, which can analyze and determine the principle and feasibility of the upstream and downstream relationships of neurons, so as to more systematically and comprehensively analyze the working principles of the brain, more effectively mine brain map data, and then more effectively understand the brain structure and brain network connection rules, provide an accurate navigation map for in-depth analysis of brain functions and brain diseases, and provide an important reference for the design of new brain-like intelligent systems.

[0007] Specifically, the method for processing the above-mentioned neuron data provided in accordance with the first aspect of the present invention includes the following steps: acquiring the spatial coordinates of axons and dendrites of multiple neurons; judging whether there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron according to the first spatial coordinates of the axon of the first neuron and the second spatial coordinates of the dendrite of the second neuron; and in response to the judgment result that there is an interaction between the axon of the first neuron and the dendrite of the second neuron, determining the connection relationship between the first neuron and the second neuron, and judging that the first neuron is the upstream neuron of the second neuron and the second neuron is the downstream neuron of the first neuron.

[0008] Preferably, in one embodiment of the present invention, the step of judging whether there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron according to the first spatial coordinates of the axon of the first neuron and the second spatial coordinates of the dendrite of the second neuron includes: dividing the axon of the first neuron into a plurality of axon segments, and dividing the dendrite of the second neuron into a plurality of dendrite segments; judging whether each axon segment intersects with each dendrite segment according to the first spatial coordinates of each axon segment and the second spatial coordinates of each dendrite segment; adding the intersecting axon segments and dendrite segments to the intersection of the axon of the first neuron and the dendrite of the second neuron; and in response to the number of elements in the intersection being greater than a preset number threshold, judging that there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron.

[0009] Preferably, in one embodiment of the present invention, the step of judging whether each axon segment intersects with each dendritic segment based on the first spatial coordinates of each axon segment and the second spatial coordinates of each dendritic segment includes: determining the first spatial coordinate based on the midpoint coordinates of each axon segment; determining the second spatial coordinate based on the midpoint coordinates of each dendritic segment; respectively calculating the distance between the first spatial coordinates of each axon segment of the first neuron and the second spatial coordinates of each dendritic segment of the second neuron; and in response to the distance between the first spatial coordinate of any axon segment and the second spatial coordinate of any dendritic segment being less than a preset distance threshold, judging that the axon segment intersects with the dendritic segment, and adding the axon segment and the dendritic segment to the intersection of the axon of the first neuron and the dendrite of the second neuron.

[0010] Preferably, in one embodiment of the present invention, the step of judging whether each axon segment intersects with each dendritic segment according to the first spatial coordinates of each axon segment and the second spatial coordinates of each dendritic segment includes: establishing a first cylindrical model of each axon segment according to the first spatial coordinates of each axon segment and a preset first offset distance; establishing a second cylindrical model of each dendritic segment according to the second spatial coordinates of each dendritic segment and a preset second offset distance; respectively calculating the overlapping volume of each first cylindrical model and each second cylindrical model; and in response to the overlapping volume of any first cylindrical model and any second cylindrical model being greater than a preset first volume threshold, judging that the axon segment intersects with the dendritic segment; or in response to the ratio of the overlapping volume to any first cylindrical model being greater than or equal to a preset first volume ratio threshold, judging that the second neuron is a downstream neuron of the first neuron; and / or in response to the ratio of the overlapping volume to any second cylindrical model being greater than or equal to a preset second volume ratio threshold, judging that the first neuron is an upstream neuron of the second neuron.

[0011] Preferably, in one embodiment of the present invention, the step of determining whether there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron based on the first spatial coordinates of the axon of the first neuron and the second spatial coordinates of the dendrite of the second neuron includes: establishing a first pipeline model of the axon of the first neuron based on the first spatial coordinates of the axon of the first neuron and a preset first offset distance; establishing a second pipeline model of the dendrite of the second neuron based on the second spatial coordinates of the dendrite of the second neuron and a preset second offset distance; calculating the overlapping volume of the first pipeline model and the second pipeline model; and in response to the overlapping volume of the first pipeline model and the second pipeline model being greater than a preset second volume threshold, determining that the axon segment intersects the dendrite segment; or in response to the ratio of the overlapping volume to the first pipeline model being greater than or equal to a preset third volume ratio threshold, determining that the second neuron is a downstream neuron of the first neuron; and / or in response to the ratio of the overlapping volume to the second pipeline model being greater than or equal to a preset fourth volume ratio threshold, determining that the first neuron is an upstream neuron of the second neuron.

[0012] Preferably, in an embodiment of the present invention, in response to any of the neurons obtaining user focus, an updated simulation image is rendered and displayed according to a judgment result that there is interaction between the target neuron obtaining the user focus and the remaining neurons.

[0013] In addition, the neuron data processing system provided according to the second aspect of the present invention includes a memory and a processor. The memory stores computer instructions. The processor is connected to the memory and is configured to execute the computer instructions stored in the memory to implement the neuron data processing method provided in any one of the above embodiments.

[0014] In addition, the computer-readable storage medium provided according to the third aspect of the present invention stores computer instructions. When the computer instructions are executed by the processor, the method for processing neuron data provided by any one of the above embodiments is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above features and advantages of the present invention can be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or features may have the same or similar reference numerals.

[0016] Figure 1 A flowchart of a method for processing neuron data provided according to some embodiments of the present invention is shown.

[0017] Figure 2 A schematic diagram showing a neuron data processing system for determining spatial interaction according to some embodiments of the present invention is shown.

[0018] Figure 3A-3B A schematic diagram showing a neuron data processing system for determining spatial interaction according to some embodiments of the present invention is shown.

[0019] Figure 4 A schematic diagram showing a neuron data processing system for determining spatial interaction according to some embodiments of the present invention is shown.

[0020] Figure 5 A schematic diagram of neurons with connection relationships provided according to some embodiments of the present invention is shown.

[0021] Figure 6 A schematic diagram showing the visualization of the upstream and downstream relationship of a single neuron in a neuron data processing system provided according to some embodiments of the present invention is shown.

[0022] Figure 7 A schematic diagram showing the visualization of upstream and downstream relationships of neuron clusters in a neuron data processing system provided according to some embodiments of the present invention is shown. DETAILED DESCRIPTION

[0023] The following specific embodiments illustrate the implementation of the present invention, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Although the description of the present invention will be introduced in conjunction with the preferred embodiment, this does not mean that the features of this invention are limited to this implementation. On the contrary, the purpose of introducing the invention in conjunction with the implementation is to cover other options or modifications that may be extended based on the claims of the present invention. In order to provide a deep understanding of the present invention, the following description will include many specific details. The present invention can also be implemented without using these details. In addition, in order to avoid confusion or blurring the focus of the present invention, some specific details will be omitted in the description.

[0024] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the terms "installation", "connection" and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] In addition, the terms "upper", "lower", "left", "right", "top", "bottom", "horizontal" and "vertical" used in the following description should be understood as the directions shown in the paragraph and the related drawings. Such relative terms are only used for the convenience of description and do not mean that the device described therein must be manufactured or operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0026] It is understood that although the terms "first", "second", "third", etc. may be used herein to describe various components, regions, layers and / or parts, these components, regions, layers and / or parts should not be limited by these terms, and these terms are only used to distinguish different components, regions, layers and / or parts. Therefore, the first component, region, layer and / or part discussed below may be referred to as a second component, region, layer and / or part without departing from some embodiments of the present invention.

[0027] As mentioned above, studying the projection patterns of neurons and their basic laws is of great significance for analyzing the organizational structure and information processing mechanism of the brain. The mutual projection between neurons is the basis for the brain to perform various functions. Clarifying the projection information of neurons upstream and downstream is often the first step in conducting research on the function of neural circuits.

[0028] In order to overcome the above-mentioned defects of the prior art, the present invention provides a neuron data processing technology, which can analyze and determine the principle and feasibility of the upstream and downstream relationships of neurons, so as to more systematically and comprehensively analyze the working principles of the brain, more effectively mine brain map data, and then more effectively understand the brain structure and brain network connection rules, provide an accurate navigation map for in-depth analysis of brain functions and brain diseases, and provide an important reference for the design of new brain-like intelligent systems.

[0029] In some non-limiting embodiments, the above-mentioned neuron data processing method provided in the first aspect of the present invention can be implemented via the above-mentioned neuron data processing system provided in the second aspect of the present invention. Specifically, the neuron data processing system can be configured with a memory and a processor. The memory includes but is not limited to the above-mentioned computer-readable storage medium provided in the third aspect of the present invention, on which computer instructions are stored. The processor is connected to the memory and is configured to execute the computer instructions stored on the memory to implement the neuron data processing method provided in the first aspect of the present invention.

[0030] Please refer to Figure 1 , Figure 1 A flowchart of a method for processing neuron data provided according to some embodiments of the present invention is shown.

[0031] like Figure 1 As shown, the neuron data processing system first obtains the spatial coordinates of axons and dendrites of multiple neurons.

[0032] Here, the processing system of neuron data determines the spatial coordinate ranges occupied by multiple brain regions and multiple neurons in the brain based on the anatomical data of the brain, and establishes a spatial model of the brain according to the spatial coordinate ranges occupied by multiple brain regions and multiple neurons in the brain. Based on the spatial model of the brain, an information file is generated that records the index, type and spatial coordinates of each brain region and each neuron in the brain. The information file can be a file configured using a json file. In a non-limiting embodiment, the file is named info.json. Furthermore, the processing system of neuron data can directly read the relationship between neurons and brain regions from the information file (e.g., info.json file). The information file records the relationship between neurons and related brain regions, such as the source region, input region, and output region to which the neurons belong, and also records the relationship between brain regions and related neurons, such as owned neurons, projecting regions, and receiving regions, as well as the relationship between brain regions, such as upstream regions and downstream regions, and also records the relationship between neurons, such as upstream neurons and downstream neurons. In addition, the file may also record upstream neuron arrays and / or downstream neuron arrays. Here, the neuron data processing system reads the above information file to determine the spatial coordinates of the axons and dendrites of each neuron.

[0033] Afterwards, it is determined whether there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron according to the first spatial coordinate of the axon of the first neuron and the second spatial coordinate of the dendrite of the second neuron.

[0034] Specifically, the neuron data processing system can divide the axon of the first neuron into a plurality of axon segments, and divide the dendrite of the second neuron into a plurality of dendrite segments, and then determine whether each axon segment intersects with each dendrite segment according to the first spatial coordinates of each axon segment and the second spatial coordinates of each dendrite segment, and add the intersecting axon segment and dendrite segment to the intersection of the axon of the first neuron and the dendrite of the second neuron. In response to the number of elements in the intersection being greater than a preset number threshold, it is determined that there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron.

[0035] Please refer to Figure 2 , Figure 2 A schematic diagram showing a neuron data processing system for determining spatial interaction according to some embodiments of the present invention is shown.

[0036] exist Figure 2 In the embodiment shown, determining whether each axon segment intersects with each dendrite segment can be achieved by determining the midpoint coordinates. Specifically, firstly, the first spatial coordinates are determined according to the midpoint coordinates of each axon segment, and then the second spatial coordinates are determined according to the midpoint coordinates of each dendrite segment. After that, the distance 23 between the first spatial coordinates of each axon segment of the first neuron and the second spatial coordinates of each dendrite segment of the second neuron is calculated respectively. Figure 2 As shown, in response to the spacing 23 between the first spatial coordinate of any axon segment 21 and the second spatial coordinate of any dendrite segment 22 being less than a preset spacing threshold, it is determined that the axon segment 21 intersects with the dendrite segment 22, and the axon segment 21 and the dendrite segment 22 are added to the intersection of the axon of the first neuron and the dendrite of the second neuron. Here, the model radius R (Model Radius) with the midpoint coordinates of each axon segment and the midpoint coordinates of each dendrite segment as the sphere center can be set by oneself, in micrometers, and the preset spacing threshold is the diameter of the spherical model.

[0037] In addition, the neuron data processing system can also use a cylinder model to determine whether each axon segment intersects with each dendrite segment. Specifically, first, according to the first spatial coordinates of each axon segment and the preset first offset distance, a first cylinder model of each axon segment is established. Then, according to the second spatial coordinates of each dendrite segment and the preset second offset distance, a second cylinder model of each dendrite segment is established. Here, the first and second offset distances are the same values ​​set and input by the user. After that, the overlap volume of each first cylinder model and each second cylinder model is calculated respectively, and in response to the overlap volume of any first cylinder model and any second cylinder model being greater than the preset first volume threshold, it is determined that the axon segment intersects with the dendrite segment. In addition, considering that the upstream and downstream connection weights may be asymmetric, the upstream and downstream relationship can also be determined by the relative volume method. At this time, in response to the ratio of the overlap volume to any first cylinder model being greater than or equal to the preset first volume ratio threshold, the second neuron is determined to be the downstream neuron of the first neuron, and the first volume ratio threshold is the downstream volume ratio threshold set by the user. Accordingly, in response to the ratio of the overlapped volume to any second cylindrical model being greater than or equal to a preset second volume ratio threshold, the first neuron is determined to be an upstream neuron of the second neuron. The second volume ratio threshold is a self-set upstream volume ratio threshold.

[0038] Please refer to Figure 3A-3B , Figure 3A-3B A schematic diagram showing a neuron data processing system for determining spatial interaction according to some embodiments of the present invention is shown.

[0039] like Figure 3A As shown, according to the first spatial coordinates of each axon line segment 311 and the preset first offset distance, a square column first cylinder model of each axon line segment 311 is established, and the first cylinder model takes each axon line segment 311 as the center line, and the preset first offset distance is the radius R (Model Radius) of the circumscribed circle of its square section. Then, according to the second spatial coordinates of each dendrite line segment 322 and the preset second offset distance, a square column second cylinder model of each dendrite line segment 322 is established, and the second cylinder model takes each dendrite line segment 322 as the center line, and the preset second offset distance is the radius of the circumscribed circle of its square section. Here, the first and second offset distances are the same values ​​set and input by the user. Afterwards, the overlap volume of each first cylinder model and each second cylinder model is calculated respectively, and in response to the overlap volume of any first cylinder model and any second cylinder model being greater than the preset first volume threshold, it is determined that the axon line segment 311 intersects with the dendrite line segment 312. In addition, considering that the weights of the upstream and downstream connections may be asymmetric, at this time, the ratio of the overlapped volume of the first cylinder model and the second cylinder model to the first cylinder model can be compared with the downstream volume ratio threshold. If it is greater than or equal to the downstream volume ratio threshold, the second neuron where the dendrite segment corresponding to the second cylinder model is located is determined to be the downstream neuron of the first neuron where the axon segment corresponding to the first cylinder model is located. Alternatively, if the ratio of the overlapped volume of the first cylinder model and the second cylinder model to the second cylinder model is greater than or equal to the upstream volume ratio threshold, at this time, the first neuron where the axon segment corresponding to the first cylinder model is located is determined to be the upstream neuron of the second neuron where the dendrite segment corresponding to the second cylinder model is located.

[0040] like Figure 3B As shown, according to the first spatial coordinates of each axon segment 321 and the preset first offset distance, a cylindrical first cylinder model of each axon segment 321 is established. The first cylinder model takes each axon segment 321 as the center line and the preset first offset distance as its radius R (Model Radius). Then, according to the second spatial coordinates of each dendrite segment 322 and the preset second offset distance, a cylindrical second cylinder model of each dendrite segment 322 is established. The second cylinder model takes each dendrite segment 322 as the center line and the preset second offset distance as its radius. Here, the first and second offset distances are the same value set and input by the user. Afterwards, the overlapping volume of each first cylinder model and each second cylinder model is calculated respectively. In response to the overlapping volume of any first cylinder model and any second cylinder model being greater than the preset first volume threshold, it is determined that the axon segment 321 intersects with the dendrite segment 322. Similarly, it is also possible to Figure 3A Description of the content of the substance is determined by the relative volume method.

[0041] In addition, when determining whether there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron, the neuron data processing system can also establish a first pipeline model of the axon of the first neuron according to the first spatial coordinates of the axon of the first neuron and the preset first offset distance, and then establish a second pipeline model of the dendrite of the second neuron according to the second spatial coordinates of the dendrite of the second neuron and the preset second offset distance. Calculate the overlap volume of the first pipeline model and the second pipeline model, and in response to the overlap volume of the first pipeline model and the second pipeline model being greater than the preset second volume threshold, determine that the axon segment and the dendrite segment intersect. Here, the preset first offset distance and the preset second offset distance are the same value set and input by the user. In addition, considering that the upstream and downstream connection weights may be asymmetric, the upstream and downstream relationship can also be determined by the relative volume method. At this time, in response to the ratio of the overlap volume to the first pipeline model being greater than or equal to the preset third volume ratio threshold, determine that the second neuron is the downstream neuron of the first neuron, and the third volume ratio threshold is the downstream volume ratio threshold set by itself. Correspondingly, in response to the ratio of the overlap volume to the second pipeline model being greater than or equal to the preset fourth volume ratio threshold, determine that the first neuron is the upstream neuron of the second neuron. The fourth volume ratio threshold is a self-set upstream volume ratio threshold.

[0042] Please refer to Figure 4 , Figure 4 A schematic diagram showing a neuron data processing system for determining spatial interaction according to some embodiments of the present invention is shown.

[0043] like Figure 4 As shown, according to the first spatial coordinates of the first neuron axon 10 and the preset first offset distance, a first pipeline model of the first neuron axon 10 is established, and then according to the second spatial coordinates of the second neuron dendrite 20 and the preset second offset distance, a second pipeline model of the second neuron dendrite 20 is established. Here, the first offset distance is the same as the second offset distance, which is the preset pipeline model radius R (Model Radius). Different from the cylindrical model in other embodiments, the axons and dendrites of neurons will contain multiple bends, such as Figure 4 In the embodiment shown, the established pipeline model will also bend at the corresponding position. Compared with the cylinder model, the spatial modeling of the pipeline model is more difficult, but it avoids the error of intersection counting, so it can more accurately determine whether there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron.

[0044] Afterwards, in response to the judgment result that there is an interaction between the axon of the first neuron and the dendrite of the second neuron, the connection relationship between the first neuron and the second neuron is determined, and the first neuron is determined to be the upstream neuron of the second neuron, and the second neuron is the downstream neuron of the first neuron. Similarly, a pipeline model of the dendrite of the first neuron and a pipeline model of the axon of the second neuron can also be established, so as to judge the upstream and downstream relationship between the first neuron and the second neuron by the absolute volume method or the relative volume method.

[0045] Please refer to Figure 5 , Figure 5 A schematic diagram of neurons with connection relationships provided according to some embodiments of the present invention is shown.

[0046] like Figure 5 As shown, in response to the judgment result that there is an interaction between the axon a1 of neuron A and the dendrite b2 of neuron B, the connection relationship between neuron A and neuron B is determined, and neuron A is determined to be the upstream neuron of neuron B. Similarly, in response to the judgment result that there is an interaction between the dendrite c2 of neuron C and the axon b1 of neuron B, the connection relationship between neuron C and neuron B is determined, and neuron C is determined to be the downstream neuron of neuron B.

[0047] Further, the information file (e.g., info.json) is read to obtain the upstream and downstream connection relationship between neurons. In response to any neuron obtaining user focus (e.g., the mouse hovers over its image, or clicks on the image), according to the judgment result that there is interaction between the target neuron that obtains user focus and the remaining neurons, an updated simulation image is rendered and displayed.

[0048] Please refer to Figure 6 and Figure 7 , Figure 6 A schematic diagram showing the visualization of the upstream and downstream relationship of a single neuron in a neuron data processing system provided according to some embodiments of the present invention is shown. Figure 7 A schematic diagram showing the visualization of upstream and downstream relationships of neuron clusters in a neuron data processing system provided according to some embodiments of the present invention is shown.

[0049] like Figure 6 As shown, in response to any neuron gaining user focus, the target neuron 601 is rendered and displayed in dark gray, the upstream neuron 602 of the target neuron 601 gaining user focus is rendered and displayed in light gray, and the downstream neuron 603 of the target neuron 601 is rendered and displayed in bright white. The remaining neurons that are not connected to the target neuron 601 gaining user focus are hidden. Figure 7 In the illustrated embodiment, in response to any neuron cluster (Target NeuronCluster) 701 obtaining user focus, the neuron data processing system may render and display the neuron cluster 701 in black, and render and display the downstream neuron cluster (Downstream Neuron Cluster) 702 of the neuron cluster 701 in another bright white, and hide the remaining neurons that are not connected to the neuron cluster 701.

[0050] In summary, the neuron data processing technology provided by the present invention analyzes the spatial coordinates of neurons to determine whether there is interaction between neurons and further determine the connection relationship, such as upstream and downstream relationships, thereby providing a method for predicting neuron pathways, and then effectively understanding the brain structure and brain network connection rules, providing an accurate navigation map for in-depth analysis of brain function and brain diseases, and providing an important reference for the design of new brain-like intelligent systems.

[0051] Although the above methods are illustrated and described as a series of actions for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the actions, because according to one or more embodiments, some actions may occur in a different order and / or concurrently with other actions from those illustrated and described herein or not illustrated and described herein but understandable to those skilled in the art.

[0052] Those skilled in the art will appreciate that information, signals, and data may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols, and chips cited throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or optical particles, or any combination thereof.

[0053] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. The technician may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0054] The various illustrative logic modules and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0055] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0056] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, a server, or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0057] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for processing neuron data, characterized in that: The following steps are involved: Obtain the spatial coordinates of axons and dendrites of multiple neurons; Determining whether there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron according to the first spatial coordinate of the axon of the first neuron and the second spatial coordinate of the dendrite of the second neuron; as well as In response to a judgment result that there is interaction between the axon of the first neuron and the dendrite of the second neuron, a connection relationship between the first neuron and the second neuron is determined, and the first neuron is determined to be an upstream neuron of the second neuron and the second neuron is a downstream neuron of the first neuron.

2. The processing method according to claim 1, characterized in that: The step of determining whether there is spatial interaction between the axon of the first neuron and the dendrite of the second neuron according to the first spatial coordinate of the axon of the first neuron and the second spatial coordinate of the dendrite of the second neuron comprises: dividing the axon of the first neuron into a plurality of axon segments, and dividing the dendrite of the second neuron into a plurality of dendrite segments; Determining whether each axon line segment intersects with each dendrite line segment according to the first spatial coordinates of each axon line segment and the second spatial coordinates of each dendrite line segment; adding the intersecting axonal and dendritic segments to the intersection of the axon of the first neuron and the dendrite of the second neuron; and In response to the number of elements in the intersection being greater than a preset number threshold, it is determined that there is a spatial interaction between the axon of the first neuron and the dendrite of the second neuron.

3. The processing method according to claim 2, characterized in that: The step of judging whether each axon segment intersects with each dendrite segment according to the first spatial coordinates of each axon segment and the second spatial coordinates of each dendrite segment comprises: Determining the first spatial coordinates according to the midpoint coordinates of each axon line segment; Determining the second spatial coordinates according to the midpoint coordinates of each of the dendrite line segments; respectively calculating the distances between the first spatial coordinates of each axon segment of the first neuron and the second spatial coordinates of each dendrite segment of the second neuron; and In response to the distance between the first spatial coordinate of any axon segment and the second spatial coordinate of any dendrite segment being less than a preset distance threshold, it is determined that the axon segment intersects with the dendrite segment, and the axon segment and the dendrite segment are added to the intersection of the axon of the first neuron and the dendrite of the second neuron.

4. The processing method according to claim 2, characterized in that: The step of judging whether each axon segment intersects with each dendrite segment according to the first spatial coordinates of each axon segment and the second spatial coordinates of each dendrite segment comprises: Establishing a first cylindrical model of each axon line segment according to the first spatial coordinates of each axon line segment and a preset first offset distance; Establishing a second cylindrical model of each dendrite segment according to the second spatial coordinates of each dendrite segment and a preset second offset distance; respectively calculating the overlapping volumes of each of the first cylindrical models and each of the second cylindrical models; and In response to the overlap volume of any first cylindrical model and any second cylindrical model being greater than a preset first volume threshold, determining that the axon line segment intersects the dendrite line segment; or In response to the ratio of the overlapped volume to any first cylindrical model being greater than or equal to a preset first volume ratio threshold, determining that the second neuron is a downstream neuron of the first neuron; and / or In response to the fact that the ratio of the overlapping volume to any second cylindrical model is greater than or equal to a preset second volume ratio threshold, the first neuron is determined to be an upstream neuron of the second neuron.

5. The processing method according to claim 1, characterized in that: The step of determining whether there is spatial interaction between the axon of the first neuron and the dendrite of the second neuron according to the first spatial coordinate of the axon of the first neuron and the second spatial coordinate of the dendrite of the second neuron comprises: Establishing a first pipeline model of the axon of the first neuron according to the first spatial coordinates of the axon of the first neuron and a preset first offset distance; Establishing a second pipeline model of the dendrite of the second neuron according to the second spatial coordinates of the dendrite of the second neuron and a preset second offset distance; calculating the overlapping volume of the first pipeline model and the second pipeline model; and In response to the overlap volume of the first pipeline model and the second pipeline model being greater than a preset second volume threshold, determining that the axon line segment intersects the dendrite line segment; or In response to the ratio of the overlapped volume to the first pipeline model being greater than or equal to a preset third volume ratio threshold, determining that the second neuron is a downstream neuron of the first neuron; and / or In response to the ratio of the overlapped volume to the second pipeline model being greater than or equal to a preset fourth volume ratio threshold, it is determined that the first neuron is an upstream neuron of the second neuron.

6. The processing method according to claim 1, characterized in that: In response to any of the neurons obtaining the user focus, an updated simulation image is rendered and displayed according to a determination result that there is interaction between the target neuron obtaining the user focus and the remaining neurons.

7. A neuron data processing system, characterized in that: include: a memory having computer instructions stored thereon; as well as A processor is connected to the memory and configured to execute computer instructions stored in the memory to implement the neuron data processing method according to any one of claims 1 to 6.

8. A computer-readable storage medium having computer instructions stored thereon, characterized in that: When the computer instructions are executed by a processor, the neuron data processing method according to any one of claims 1 to 6 is implemented.

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