阅读说明：本技术 神经元信号的处理方法、处理装置以及可读存储介质 (Processing method and processing device for neuron signals and readable storage medium ) 是由 岳斌 李骁健 于 2021-06-22 设计创作，主要内容包括：本申请涉及神经元信号处理技术领域,公开了神经元信号的处理方法、处理装置以及可读存储介质。该方法包括：获取神经元信号,神经元信号包括膜电位数据；对膜电位数据进行求导处理,以得到导数数据；根据导数数据确定神经元信号的脉冲位置、单脉冲发放时间范围、连续脉冲发放时间范围中的至少一种。通过上述方式,能够提升从神经元信号中提取脉冲位置、单脉冲发放时间范围以及连续脉冲发放时间范围的提取速度以及准确性。(The application relates to the technical field of neuron signal processing, and discloses a processing method and a processing device for neuron signals and a readable storage medium. The method comprises the following steps: acquiring a neuron signal, wherein the neuron signal comprises membrane potential data; performing derivation processing on the membrane potential data to obtain derivative data; at least one of a pulse position, a single pulse firing time range, and a continuous pulse firing time range of the neuron signal is determined from the derivative data. By the method, the extraction speed and the accuracy of extracting the pulse position, the single pulse emitting time range and the continuous pulse emitting time range from the neuron signal can be improved.)

1. A method of processing a neuron signal, the method comprising:

acquiring a neuron signal, wherein the neuron signal comprises membrane potential data;

performing derivation processing on the membrane potential data to obtain derivative data;

determining at least one of a pulse position, a single pulse firing time range, a continuous pulse firing time range of the neuron signal from the derivative data.

2. The method of claim 1,

the derivation processing of the membrane potential data to obtain derivative data includes:

acquiring first membrane potential data and second membrane potential data which are adjacent in the neuron signal; the second film potential data is subsequent to the first film potential data;

using a difference value between the first film potential data and the second film potential data as reference film potential data;

deriving the derivative data based on a plurality of the reference membrane potential data.

3. The method of claim 2,

said deriving said derivative data based on a plurality of said reference membrane potential data, comprising:

performing binarization operation on a plurality of reference membrane potential data;

converting the value of the reference membrane electric potential data meeting the first preset condition into a first preset value;

taking the value of the reference membrane electric potential data meeting the second preset condition as a second preset value;

and obtaining the derivative data based on a plurality of first preset values and a plurality of second preset values.

4. The method of claim 3,

said determining a continuous pulse firing time range of said neuron signal from said derivative data comprises:

acquiring target reference membrane potential data in the derivative data, and a first number of reference membrane potential data in a left neighborhood and a second number of reference membrane potential data in a right neighborhood of the target reference membrane potential data to obtain a plurality of first membrane potential data fragments;

determining an average value of all reference film potential data in each of the first film potential data segments as a first numerical value of the target reference film potential data in each of the first film potential data segments in order from left to right; wherein a first value of said target reference film potential data will participate in the calculation of a next said first film potential data segment;

determining an average value of all reference film potential data in each of the first film potential data segments in order from right to left as a second numerical value of the target reference film potential data in each of the first film potential data segments; wherein a second value of said target reference film potential data will participate in the calculation of a next said first film potential data segment;

determining said continuous pulse firing time range from a plurality of said first values and a plurality of said second values.

5. The method of claim 4,

said determining said continuous pulse firing time range from a plurality of said first values and a plurality of said second values comprises:

and determining the time range corresponding to the first numerical value or the second numerical value meeting the preset condition as the continuous pulse sending time range.

6. The method of claim 2,

the determining pulse positions of the neuron signals from the derivative data comprises:

acquiring adjacent first reference film potential data and second reference film potential data in the derivative data; the second reference film potential data is subsequent to the first reference film potential data;

and if the first reference film potential data is larger than the second reference film potential data and larger than 0 and the second reference film potential data is smaller than 0, taking the moment corresponding to the first reference film potential data as the pulse position.

7. The method of claim 6,

before determining a single pulse firing time range of the neuron signal from the derivative data, comprising:

converting the value of the reference membrane electric potential data corresponding to the pulse position into a first preset value;

converting the value of the reference membrane electric potential data which does not correspond to the pulse position into a second preset value;

and obtaining the derivative data based on a plurality of first preset values and a plurality of second preset values.

8. The method according to claim 3 or 7,

the determining a single pulse firing time range of at least one of a pulse position, a single pulse firing time range, a continuous pulse firing time range of the neuron signal from the derivative data comprises:

acquiring target reference membrane potential data in the derivative data, and a first number of reference membrane potential data in a left neighborhood and a second number of reference membrane potential data in a right neighborhood of the target reference membrane potential data to obtain a plurality of second membrane potential data segments;

determining an average value of all reference film potential data of each of the second film potential data segments as a value of the target reference film potential data;

determining the single-pulse firing time range from a plurality of values of the target reference membrane potential data.

9. The method of claim 8,

said determining said single-pulse firing time range from a plurality of values of said target reference membrane potential data, comprising:

and determining a time range corresponding to a plurality of target reference membrane potential data continuously meeting a preset condition as the single-pulse distribution time range.

10. A processing apparatus of a neuron signal, comprising a processor and a memory coupled to the processor, wherein the memory has stored therein a computer program for executing the computer program to implement the processing method according to any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the processing method of any one of claims 1-9.

Technical Field

The present application relates to the field of neuron signal processing technology, and in particular, to a processing method, a processing apparatus, and a readable storage medium for neuron signals.

Background

Electrophysiology is the field of research in studying the change in current or voltage across a cell membrane. Electrophysiology is widely used in various neuroscience and physiology applications, ranging from understanding of single ion channel behavior in a certain cell membrane to whole-cell changes of membrane potential data in a certain cell, to a larger range of field potentials in an in vitro brain slice or in vivo brain region. Ion channels are the primary targets of researchers due to their critical roles and physiology in many neurological and cardiovascular diseases, while patch clamp (one of the most widely used electrophysiological techniques) is the best tool to study ion channel activity.

The processing of neuron signals acquired by patch clamp in the related art has defects, which can cause the extracted data to be inaccurate.

Disclosure of Invention

The technical problem mainly solved by the present application is to provide a processing method, a processing apparatus and a readable storage medium for neuron signals, which can improve the extraction speed and accuracy of pulse positions, single pulse emission time ranges and continuous pulse emission time ranges from neuron signals.

In order to solve the above problem, one technical solution adopted by the present application is to provide a method for processing a neuron signal, the method including: acquiring a neuron signal, wherein the neuron signal comprises membrane potential data; performing derivation processing on the membrane potential data to obtain derivative data; at least one of a pulse position, a single pulse firing time range, and a continuous pulse firing time range of the neuron signal is determined from the derivative data.

Wherein, carrying out derivation processing on the film electric potential data to obtain derivative data comprises the following steps: acquiring first membrane potential data and second membrane potential data which are adjacent in the neuron signal; the second film potential data follows the first film potential data; using the difference value between the first film potential data and the second film potential data as reference film potential data; derivative data is derived based on a plurality of reference membrane potential data.

Wherein deriving derivative data based on the plurality of reference membrane potential data comprises: performing binarization operation on the plurality of reference membrane potential data; converting the value of the reference membrane electric potential data meeting the first preset condition into a first preset value; taking the value of the reference membrane electric potential data meeting the second preset condition as a second preset value; derivative data is obtained based on the plurality of first preset values and the plurality of second preset values.

Wherein determining a continuous pulse firing time range of the neuron signal from the derivative data comprises: acquiring target reference membrane potential data in the derivative data, first quantity of reference membrane potential data in a left neighborhood and second quantity of reference membrane potential data in a right neighborhood of the target reference membrane potential data to obtain a plurality of first membrane potential data fragments; determining an average value of all reference film potential data in each first film potential data segment as a first numerical value of target reference film potential data in each first film potential data segment in order from left to right; wherein a first value of the target reference membrane potential data within a first number of ranges of the left neighborhood will participate in the calculation of the current first membrane potential data segment; determining an average value of all reference film potential data in each first film potential data segment as a second numerical value of the target reference film potential data in each first film potential data segment in the order from right to left; wherein a second value of the target reference film potential data within a second number of ranges of the right neighborhood will participate in the calculation of the current first film potential data segment; the continuous pulse firing time range is determined based on the plurality of first values and the plurality of second values.

Wherein determining the continuous pulse firing time range from the first plurality of values and the second plurality of values comprises: and determining a time range corresponding to the first numerical value or the second numerical value which continuously meets the preset condition as a continuous pulse sending time range.

Wherein determining the pulse position of the neuron signal from the derivative data comprises: acquiring adjacent first reference film potential data and second reference film potential data in the derivative data; the second reference film potential data is subsequent to the first reference film potential data; if the first reference film potential data is larger than the second reference film potential data and larger than 0 and the second reference film potential data is smaller than 0, the time corresponding to the first reference film potential data is taken as the pulse position.

Wherein determining the single pulse firing time range of the neuron signal prior to determining the single pulse firing time range from the derivative data comprises: converting the value of the reference membrane electric potential data corresponding to the pulse position into a first preset value; converting the value of the reference membrane electric potential data which does not correspond to the pulse position into a second preset value; derivative data is obtained based on the plurality of first preset values and the plurality of second preset values.

Wherein determining a single pulse firing time range for at least one of a pulse position, a single pulse firing time range, and a continuous pulse firing time range for the neuron signal based on the derivative data comprises: acquiring target reference membrane potential data in the derivative data, and a first number of reference membrane potential data in a left neighborhood and a second number of reference membrane potential data in a right neighborhood of the target reference membrane potential data to obtain a plurality of second membrane potential data segments; determining an average value of all reference membrane potential data of each second membrane potential data segment as a value of target reference membrane potential data; a single-pulse firing time range is determined from the values of the plurality of target reference membrane potential data.

Wherein determining a single-pulse firing time range from values of a plurality of target reference membrane potential data comprises: and determining a time range corresponding to a plurality of target reference membrane potential data continuously meeting the preset condition as a single-pulse distribution time range.

In order to solve the above problem, another technical solution adopted by the present application is to provide a processing apparatus for a neuron signal, the processing apparatus including a processor and a memory coupled to the processor, the memory storing a computer program therein, and the processor being configured to execute the computer program to implement the processing method provided in the above technical solution.

In order to solve the above problem, another technical solution adopted by the present application is to provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the processing method provided in the above technical solution.

The beneficial effect of this application is: the present application provides a processing method, a processing apparatus, and a readable storage medium for neuron signals, which are different from the prior art. According to the method, correlation can be generated among discrete membrane potential data by performing derivation processing on the membrane potential data, at least one of the pulse position, the single pulse emitting time range and the continuous pulse emitting time range of the neuron signal can be determined from the correlated derivative data, and the extraction speed and the accuracy of extracting the pulse position, the single pulse emitting time range and the continuous pulse emitting time range from the neuron signal can be improved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:

FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a method for processing a neuron signal provided by the present application;

FIG. 2 is a schematic diagram of one embodiment of a neuron signal provided herein;

FIG. 3 is a schematic flow chart diagram illustrating one embodiment of step 12 of FIG. 1 provided herein;

FIG. 4 is a schematic flow chart diagram illustrating a method for processing neuron signals according to another embodiment of the present disclosure;

FIG. 5 is a schematic diagram of one embodiment of a pulse position provided herein;

FIG. 6 is a partial schematic view of FIG. 2 and FIG. 5 in comparison as provided herein;

FIG. 7 is a schematic flow chart diagram illustrating a method for processing neuron signals according to another embodiment of the present disclosure;

FIG. 8 is a schematic flow chart diagram illustrating a method for processing neuron signals according to another embodiment of the present disclosure;

FIG. 9 is a schematic diagram illustrating one embodiment of a single pulse delivery time range provided herein;

FIG. 10 is a partial schematic view of FIG. 2 in comparison to FIG. 9 provided herein;

FIG. 11 is a schematic flow chart diagram illustrating a method for processing neuron signals according to another embodiment of the present disclosure;

FIG. 12 is a schematic diagram of one embodiment of a continuous pulse delivery time range provided herein;

FIG. 13 is a partial schematic view of FIG. 2 and FIG. 12 in comparison as provided herein;

FIG. 14 is a schematic structural diagram of an embodiment of a device for processing neuron signals provided by the present application;

FIG. 15 is a schematic structural diagram of an embodiment of a computer-readable storage medium provided in the present application.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

The terms "first", "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

Electrophysiology is the field of research in studying the change in current or voltage across a cell membrane. Electrophysiology is widely used in various neuroscience and physiology applications, ranging from understanding of single ion channel behavior in a certain cell membrane to whole-cell changes of membrane potential data in a certain cell, to a larger range of field potentials in an in vitro brain slice or in vivo brain region. Ion channels are the primary targets of researchers due to their critical roles and physiology in many neurological and cardiovascular diseases, while patch clamp (one of the most widely used electrophysiological techniques) is the best tool to study ion channel activity.

The patch clamp technique is a versatile electrophysiological tool for understanding ion channel behavior. Each cell expresses an ion channel, but the most common cells studied by patch clamp techniques include nerve cells, muscle fibers, cardiomyocytes, and oocytes that highly express a single ion channel. To assess the conductance of individual ion channels, microelectrodes form high resistance seals with the cell membrane, and the cell membrane containing the ion channel of interest is removed. Alternatively, when the microelectrode is sealed to a cell membrane, the membrane sheet is ruptured, thereby allowing the electrode to be in electrical communication with the entire cell. After which a voltage is applied, a voltage clamp is formed and the membrane current is measured. Current clamps can also be used to measure changes in voltage inside and outside the cell membrane (called membrane potential data). Changes in voltage or current within the cell membrane can be altered by blocking or activating the channel by the addition of compounds. These techniques allow researchers to understand how ion channels behave in normal and disease states, and how different drugs, ions, or other analytes change these states.

Membrane potential data generally refers to the potential difference that occurs between two solutions separated by a membrane. Generally refers to the electrical phenomenon accompanying the cell's vital movement, which is the potential difference existing across the cell membrane. Membrane potential data plays an important role in the process of neural cell communication.

Neurons are the fundamental unit of biological brain computing, which, through the exchange of impulses and the transmission of information, memorize and learn through synapses. The biological brain comprises a network consisting of billions of neurons, and the neurons are connected with each other through trillions of synapses. In this network, a pulse-based temporal processing mechanism allows sparse and efficient information to be conveyed in the human brain.

The membrane potential data acquired by the patch clamp are often disordered, and how to find effective information from the disordered membrane potential data is a problem to be solved at present.

Based on this, the present application performs signal processing in the following manner.

Referring to fig. 1, fig. 1 is a schematic flowchart of an embodiment of a processing method of a neuron signal provided by the present application. The method comprises the following steps:

step 11: neuron signals are acquired, the neuron signals comprising membrane potential data.

The neuron signal may be membrane electric potential data acquired by the patch clamp within a preset time. It is understood that each membrane potential data corresponds to an acquisition time.

At this time, the neuron signal may be represented in the form of fig. 2. In fig. 2, the abscissa is the acquisition time, and the ordinate is the membrane potential data of the neuron signal.

Step 12: the membrane potential data is subjected to derivative processing to obtain derivative data.

The description is made with reference to fig. 2:

since the membrane potential data are all disordered, it is necessary to perform derivation processing on the neuron signals to associate the discrete membrane potential data.

In some embodiments, a first derivative may be performed on the membrane potential data to obtain derivative data. Wherein each derivative data corresponds to membrane potential data.

For example, referring to fig. 3, step 12 may be the following process:

step 121: and acquiring adjacent first membrane electric potential data and second membrane electric potential data in the neuron signal.

Wherein the second film potential data is subsequent to the first film potential data.

Step 122: the difference value of the first film potential data and the second film potential data is used as reference film potential data.

Wherein, the first difference obtained by subtracting the second film potential data from the first film potential data may be used as the reference film potential data, and the reference film potential data may correspond to the first film potential data. The first film potential data may be subtracted from the second film potential data to obtain a second difference value as reference film potential data, and the reference film potential data may correspond to the second film potential data.

Step 123: derivative data is derived based on a plurality of reference membrane potential data.

Because the neuron signal has a plurality of membrane potential data, a plurality of reference membrane potential data can be correspondingly generated, and the reference membrane potential data can form derivative data according to the neuron signal mode. Wherein the time of each reference membrane potential data corresponds to the time of membrane potential data in the neuron signal.

Discrete neuron signals can then be represented by derivative data.

Step 13: at least one of a pulse position, a single pulse firing time range, and a continuous pulse firing time range of the neuron signal is determined from the derivative data.

For example, by determining adjacent reference film potential data in the derivative data, it can be determined whether or not there is a pulse position in the adjacent reference film potential data.

For another example, each reference membrane electric potential data in the derivative data is subjected to neighborhood point acquisition, and whether the reference membrane electric potential data is in the single pulse distribution time range or not is judged according to the neighborhood points.

For another example, each reference membrane electric potential data in the derivative data is subjected to neighborhood point acquisition, and whether the reference membrane electric potential data is in the continuous pulse distribution time range or not is judged according to the neighborhood points.

In this embodiment, by performing derivation processing on the membrane potential data, association can be generated between discrete membrane potential data, and then at least one of the pulse position, the single pulse firing time range, and the continuous pulse firing time range of the neuron signal can be determined from the associated derivative data, so that the extraction speed and accuracy of extracting the pulse position, the single pulse firing time range, and the continuous pulse firing time range from the neuron signal can be improved.

The applicant finds that, in long-term research, when a neuron gives a pulse, the membrane potential data corresponding to a moment is a maximum value in a certain period of time before and after the moment. And neurons firing pulses, the firing time range of a single pulse can also be collected, as well as the firing of successive pulses. The pulse data has important significance for further modeling of a neuron model, simulation of electrophysiological properties of the neuron and research of a pulse neural network.

Therefore, the accuracy of the pulse position, the single pulse firing time range, and the continuous pulse firing time range of the neuron signal is also important. Based on this, the present application provides the following examples for illustration:

referring to fig. 4, fig. 4 is a schematic flowchart of another embodiment of a processing method of a neuron signal provided by the present application. The method comprises the following steps:

step 41: neuronal signals are acquired.

Step 42: and acquiring adjacent first membrane electric potential data and second membrane electric potential data in the neuron signal.

Wherein the second film potential data is subsequent to the first film potential data.

Step 43: the difference value of the first film potential data and the second film potential data is used as reference film potential data.

Step 44: derivative data is derived based on a plurality of reference membrane potential data.

Steps 41 to 44 have the same or similar technical solutions as those of the above embodiments, and are not described herein.

In this embodiment, the step 43 may be subtracting the first film potential data from the second film potential data to obtain a first difference value, and using the first difference value as the reference film potential data corresponding to the second film potential data.

Step 45: adjacent first reference film potential data and second reference film potential data in the derivative data are acquired.

Wherein the second reference film potential data is subsequent to the first reference film potential data.

Since the derivative data may approximately represent the neuron signal, the derivative data may be used to determine the pulse position.

Step 46: if the first reference film potential data is larger than the second reference film potential data and larger than 0 and the second reference film potential data is smaller than 0, the time corresponding to the first reference film potential data is taken as the pulse position.

It is understood that if the first reference membrane potential data is greater than the second reference membrane potential data, it indicates that the membrane potential data on the neuron signal corresponding to the first reference membrane potential data is greater than the membrane potential data corresponding to the second reference membrane potential data.

And, the reference membrane potential data in the derivative data is the difference between adjacent membrane potential data in the neuron signal, which indicates that two situations, greater than 0 or less than 0, may occur in the reference membrane potential data in the derivative data.

If the reference membrane potential data is greater than 0, it indicates that the membrane potential data on the neuron signal corresponding to the reference membrane potential data is greater than the membrane potential data at a time immediately before the time on the neuron signal.

The first reference membrane potential data is larger than the second reference membrane potential data and larger than 0, and the second reference membrane potential data is smaller than 0, which indicates that the membrane potential data on the neuron signal corresponding to the first reference membrane potential data is larger than the membrane potential data on the neuron signal at the later moment of the moment. The timing at which the pulse is delivered can be determined, and the timing corresponding to the first reference membrane potential data can be determined as the pulse position.

In other embodiments, the determination may be made by:

and converting the reference film electric potential data meeting the first preset condition into a first preset value, and converting the reference film electric potential data meeting the second preset condition into a second preset value. For example, the conversion is performed by the following formula.

Wherein S (j) is a function expression obtained by transforming the derivative data,and j represents the acquisition time of the corresponding reference membrane potential data, namely the acquisition time of the membrane potential data in the neuron.

As can be understood from the above equation, the time corresponding to s (j) ═ 1 is the pulse position, that is, the neuron has performed pulse release at this time.

In an application scenario, reference is made to fig. 2, 5 and 6:

fig. 5 is a schematic diagram of the pulse positions determined by using the method of the present embodiment, and the pulse positions of fig. 5 can be compared with the neuron signals of fig. 2 to obtain an image as shown in fig. 6. Therefore, it can be determined that the error between the two is the basis, and if the error satisfies the threshold, the pulse position obtained from fig. 5 can be used for modeling of the neuron model, simulation of the electrophysiological properties of the neuron, and research of the impulse neural network.

In the embodiment, the pulse position in the neuron signal can be accurately determined by comparing the magnitude between the reference membrane potentials in the derivative data and the positive and negative of the reference membrane potentials, so that the accuracy and comprehensiveness of pulse position acquisition are improved, and the pulse position acquired by the method can more accurately reflect the activity of the neuron.

In some embodiments, after the pulse position is obtained in the above embodiments, the single pulse emitting time range can be further determined according to the pulse position. Specifically, referring to fig. 7, the method includes:

step 71: and converting the value of the reference membrane electric potential data corresponding to the pulse position into a first preset value.

Step 72: and converting the value of the reference membrane electric potential data which does not correspond to the pulse position into a second preset value.

Step 73: derivative data is obtained based on the plurality of first preset values and the plurality of second preset values.

In some embodiments, the conversion may be performed according to the formula:

at this time, the derivative data becomes a value of 0 or 1, which facilitates subsequent calculation.

Step 74: and acquiring target reference membrane potential data in the derivative data, and a first number of reference membrane potential data in the left neighborhood and a second number of reference membrane potential data in the right neighborhood of the target reference membrane potential data to obtain a plurality of second membrane potential data segments.

Wherein the first number and the second number are the same, so that data balance between the left and right of the target reference membrane potential data can be ensured. The number of reference film potential data in the second film potential data segment is odd.

Wherein the first number may be 10 to 20. Such as 12, 15, 17 or 19 may be selected.

It is to be understood that there are a plurality of reference membrane potential data in the derivative data, and the selection of the target reference membrane potential data is also performed sequentially, i.e., the next target reference membrane potential data is adjacent to the current target reference membrane potential data.

Step 75: an average value of all the reference film potential data in each of the second film potential data pieces is determined as a value of the target reference film potential data.

The target reference film potential data, the first number of reference film potential data, and the second number of reference film potential data in each second film potential data segment are summed, and then the average is calculated. The average value may represent a value of any one of the reference film potential data in the second film potential data segment, that is, may be a value of the target reference film potential data.

Step 76: a single-pulse firing time range is determined from the values of the plurality of target reference membrane potential data.

In some embodiments, a time range corresponding to a plurality of target reference membrane potential data continuously satisfying a preset condition may be determined as a single-pulse-firing time range.

If the calculation is performed according to the value of the derivative data being 0 or 1, the value of the target reference membrane potential data in step 55 will be equal to 0 or not equal to 0.

When equal to 0, it means that the reference film potential data in the second film potential data segment does not correspond to the pulse position, and when not equal to 0, it means that the reference film potential data in the second film potential data segment corresponds to the pulse position.

For example, the following steps are carried out:

for example, the conductive data includes 100 pieces of reference film potential data, the 7 th reference film potential data is 1, and the rest are 0. The first number is 10, the following results will occur.

In executing step 55, the value of the target reference film potential data is the following case:

a value of the first target reference film potential data is not equal to 0, a value of the second target reference film potential data is not equal to 0, a value of the third target reference film potential data is not equal to 0, a value of the fourth target reference film potential data is not equal to 0, a value of the fifth target reference film potential data is not equal to 0, a value of the sixth target reference film potential data is not equal to 0, a value of the seventh target reference film potential data is not equal to 0, and values of the eighth to ninety target reference film potential data are equal to 0.

The time range corresponding to the first to seventh target reference film potential data is determined as the single-pulse emitting time range.

It will be appreciated that the single pulse delivery time range may represent the entire process of preparation before the pulse is delivered to decay after the pulse is delivered.

In this embodiment, a left-right neighborhood manner is adopted for target reference membrane potential data to determine a membrane potential data segment, and the mean value of the reference membrane potential data in the segment is used as a value of the target reference membrane potential, so that each piece of reference membrane potential data has a mean value, and a range where the mean value of a preset value is located is met, so that the target reference membrane potential data can be determined as a single-pulse issuing range.

Referring to fig. 8, fig. 8 is a schematic flowchart of another embodiment of a processing method of a neuron signal provided by the present application. The method comprises the following steps:

step 801: neuron signals are acquired, the neuron signals comprising membrane potential data.

Step 802: and acquiring adjacent first membrane electric potential data and second membrane electric potential data in the neuron signal.

Wherein the second film potential data is subsequent to the first film potential data.

Step 803: the difference value of the first film potential data and the second film potential data is used as reference film potential data.

Steps 801-803 have the same or similar technical solutions as those in the above embodiments, and are not described herein again.

Step 804: and carrying out binarization operation on the plurality of reference film potential data.

Step 805: and converting the value of the reference film electric potential data meeting the first preset condition into a first preset value.

Step 806: and taking the value of the reference film electric potential data meeting the second preset condition as a second preset value.

Through the binarization operation, the noise of the reference membrane electric potential data can be removed, so that the accuracy of subsequent calculation is improved.

In some embodiments, steps 804-806 may be expressed by the following equation:

wherein the first predetermined value is 0, the second predetermined value is the reference membrane potential data itself, and A and B are predetermined conditions. Alternatively, A may be-0.5 and B may be 0.5.

Step 807: derivative data is obtained based on the plurality of first preset values and the plurality of second preset values.

If according to the above formula, the derivative data becomes 0 and corresponds to a set of values of the reference film potential data satisfying the second preset condition.

Step 808: and acquiring target reference membrane potential data in the derivative data, and a first number of reference membrane potential data in the left neighborhood and a second number of reference membrane potential data in the right neighborhood of the target reference membrane potential data to obtain a plurality of second membrane potential data segments.

Wherein the first number and the second number are the same, so that data balance between the left and right of the target reference membrane potential data can be ensured. The number of reference film potential data in the second film potential data segment is odd.

Wherein the first number may be 5 to 20. Such as 7, 8, 9, 10, 12, 15, 17 or 19 may be selected.

It is to be understood that there are a plurality of reference membrane potential data in the derivative data, and the selection of the target reference membrane potential data is also performed sequentially, i.e., the next target reference membrane potential data is adjacent to the current target reference membrane potential data.

Step 809: an average value of all the reference film potential data in each of the second film potential data pieces is determined as a value of the target reference film potential data.

Step 810: a single-pulse firing time range is determined from the values of the plurality of target reference membrane potential data.

Wherein, the steps 807 and 810 are the same as or similar to the technical solutions of the above embodiments, and are not described herein again.

In an application scenario, reference is made to fig. 2, 9 and 10:

fig. 9 shows the single pulse emitting time range determined by the method of this embodiment, and the pulse positions in fig. 9 can be compared with the neuron signals in fig. 2 to obtain the image shown in fig. 10. Thus, it can be seen from fig. 10 that the single pulse firing time range obtained by the present method matches the data in the neuron signal of fig. 2. The single-pulse firing time range obtained in fig. 9 can be used for modeling of neuron models, simulation of electrophysiological properties of neurons, and research of spiking neural networks.

In this embodiment, a left-right neighborhood mode is adopted for target reference membrane potential data to determine a membrane potential data segment, and the mean value of the reference membrane potential data in the segment is used as a value of the target reference membrane potential, so that each piece of reference membrane potential data has a mean value, and a range where the mean value of a preset value is located is met, so that a single-pulse issuing range can be determined, a single-pulse issuing time range can be accurately determined, the accuracy and the comprehensiveness of single-pulse issuing time range acquisition are improved, and the single-pulse issuing time range acquired by the method can more accurately reflect the activity of neurons.

Referring to fig. 11, fig. 11 is a schematic flowchart of a processing method of a neuron signal according to another embodiment of the present disclosure. The method comprises the following steps:

step 101: target reference membrane potential data in the derivative data, a first number of reference membrane potential data in a left neighborhood of the target reference membrane potential data, and a second number of reference membrane potential data in a right neighborhood of the target reference membrane potential data are obtained to obtain a plurality of first membrane potential data segments.

The derivative data in this embodiment is obtained according to the technical solution in any of the above embodiments, and details are not described here.

For example, the derivative data is obtained according to the following formula:

if according to the above formula, the derivative data becomes 0 and corresponds to a set of values of the reference film potential data satisfying the second preset condition.

In some embodiments, the derivative data may also be found according to the following formula:

if the above formula is followed, the derivative data becomes a set of 0 and 1.

Step 102: determining an average value of all reference film potential data in each first film potential data segment as a first numerical value of target reference film potential data in each first film potential data segment in order from left to right; wherein the first value of the target reference film potential data participates in the calculation of the next first film potential data segment.

In some embodiments, after obtaining the average value, the average value is determined, and if the average value is smaller than a set threshold, the average value is changed to 0 and then used as the first value of the target reference film potential data in each first film potential data segment. For example, the threshold values are set to 0.0001 and 0.0002.

In an application scenario, since the electronic device is used for calculation, the minimum decimal stored in the hardware limit may be used as the set threshold due to the hardware limit.

For example, in the case where no value exists for the reference film potential data other than the first value in the continuous first film potential data segment, the first value participating in the calculation from the beginning will decrease rapidly by a multiple of the number of reference film potential data in the first film potential data segment. Meanwhile, the case where no value of the reference film potential data exists in the continuous first film potential data segment other than the first value corresponds to the case of no pulse.

In other embodiments, a second value of the target reference film potential data within the first number of ranges of the left-neighboring domain will participate in the calculation of the current first film potential data segment.

Steps 101 and 102 are illustrated:

and then, carrying out average calculation on a second first film electric potential data segment, wherein the second first film electric potential data segment comprises the target reference film electric potential data in the first film electric potential data segment, and when carrying out average calculation on the second first film electric potential data segment, the target reference value participates in calculation by using the first value.

Therefore, when the calculation is performed in the order from left to right, the target reference value in each first film potential data segment which is already calculated on the left side obtains a first numerical value, and when the calculation is performed on the subsequent first film potential data segment, the first numerical values of the target reference film potential data in the first number range of the left adjacent domain participate in the calculation of the current first film potential data segment.

In this way, a range of time can be issued in accordance with the acquisition of successive pulses in the left-to-right direction.

It is understood that since the value of the reference film potential data is 0 or a positive or negative number, when the average value calculation is performed for the first film potential data segment, the average value may appear to be 0. When the value is 0, it means that no pulse is generated at that time, and the pulse is in the pulse delivery time range.

But there is a omission problem in this way because the reference film electric potential data of the right neighborhood is omitted because of the calculation participating in the next first film electric potential data piece as the reference film electric potential data of the left neighborhood, and therefore, step 103 is executed.

Step 103: determining an average value of all reference film potential data in each first film potential data segment as a second numerical value of the target reference film potential data in each first film potential data segment in the order from right to left; wherein the second value of the target reference film potential data participates in the calculation of the next first film potential data segment.

In some embodiments, after obtaining the average value, the average value is determined, and if the average value is smaller than the set threshold, the average value is changed to 0 and then used as the second value of the target reference film potential data in each of the first film potential data segments.

Step 103 is similar to step 102, except that the calculation order at this time is from right to left, in such a manner that it can be determined whether or not the reference membrane potential data of the right neighborhood is membrane potential data in the continuous pulse delivery time range.

In other embodiments, a second value of the target reference film potential data within a second number of ranges in the right neighborhood will participate in the calculation of the current first film potential data segment.

And then, carrying out average calculation on a second first film electric potential data segment, wherein the second first film electric potential data segment comprises the target reference film electric potential data in the first film electric potential data segment, and when carrying out average calculation on the second first film electric potential data segment, the target reference value is calculated by using the second numerical value.

When calculation is performed according to the sequence from right to left, the target reference value in each first film potential data segment which is calculated on the right side obtains a second numerical value, and when the next first film potential data segment is calculated, the first numerical value of the target reference film potential data in the second numerical range of the left adjacent domain participates in the calculation of the current first film potential data segment.

In this way, a range of time can be issued in accordance with the acquisition of successive pulses in the right-to-left direction.

It is understood that since the value of the reference film potential data is 0 or a positive or negative number, when the average value calculation is performed for the first film potential data segment, the average value may appear to be 0. When the value is 0, it means that no pulse is generated at that time, and the pulse is in the pulse delivery time range.

Step 104: the continuous pulse firing time range is determined based on the plurality of first values and the plurality of second values.

And determining the time range corresponding to the first numerical value or the second numerical value which meets the preset bar as the continuous pulse sending time range.

In some embodiments, the determination of the continuous pulse firing time range may be made according to the following equation.

First, a first numerical value is obtained by the following equation:

wherein，A function corresponding to a first numerical value is expressed, H denotes a first number, L denotes a length or a number of reference film potential data, P denotes a total number of reference film potential data in the first film potential data segment, P is 2H +1,in solving forWhen, according to k₁The value of 1 … N is taken from left to right, i.e., 0. Wherein, inLess than the set threshold, willThe corresponding value is changed to 0 and then taken asI.e. the first value.

Then, a plurality of second values are obtained using the following equation:

wherein the content of the first and second substances,and a function corresponding to a second value is represented, H represents a second quantity, and L represents the length or quantity of the reference film potential data. P denotes the total number of reference film potential data in the first film potential data segment, P is 2H +1,in solving forWhen, according to k₂N, N-1, … 1, 0, i.e. from right to left. Wherein, inLess than the set threshold, willThe corresponding value is changed to 0 and then taken asI.e. the second value.

Then is obtainedAndthen, since the value sequence of the two is opposite, the two need to be unified in sequence, for example, from right to left or from left to right, the order can be obtainedAndthen the following formula is calculated:

in this case, the time corresponding to d (k) 1 is the continuous pulse delivery time range.

In an application scenario, the description is made with reference to fig. 2, 12 and 13:

fig. 12 shows the pulse positions of fig. 12 and the neuron signals of fig. 2, which can be compared to obtain the image shown in fig. 13, wherein the pulse positions are determined by using the method of the present embodiment. Thus, it can be seen from fig. 13 that the single pulse firing time range obtained by the present method matches the data in the neuron signal of fig. 2. The single-pulse firing time range obtained in fig. 13 can be used for modeling of neuron models, simulation of electrophysiological properties of neurons, and study of spiking neural networks.

In this embodiment, a left-right neighborhood mode is adopted for target reference membrane potential data to determine membrane potential data segments, the mean value of the reference membrane potential data in the segments is used as the value of the target reference membrane potential, and the value of the target reference membrane potential is involved in the calculation of the next membrane potential data segment, so that the time corresponding to pulse distribution can be correlated, and further the continuous pulse distribution time range can be determined, the accuracy of collecting the continuous pulse distribution time range can be improved, and the continuous pulse distribution time range can be determined in a left-to-right and right-to-left mode, so that the collected continuous pulse distribution time range is more comprehensive, and the activity of neurons can be reflected more.

Referring to fig. 14, fig. 14 is a schematic structural diagram of an embodiment of a processing apparatus for a neuron signal provided by the present application. The processing device 140 comprises a processor 141 and a memory 142 coupled to the processor 141, wherein the memory 142 stores a computer program, and the processor 141 is configured to execute the computer program to implement the following method:

acquiring a neuron signal, wherein the neuron signal comprises membrane potential data; performing derivation processing on the membrane potential data to obtain derivative data; at least one of a pulse position, a single pulse firing time range, and a continuous pulse firing time range of the neuron signal is determined from the derivative data.

It can be understood that, the processor 141 in this embodiment may also implement the method of any one of the embodiments, and the specific implementation steps thereof may refer to the embodiments described above, which are not described herein again.

It is understood that the processing device 140 may also include a communication interface (not shown) that is coupled to the processor 141 and is configured to couple to the patch clamp. The patch clamp can be an in-vivo patch clamp and is used for signal acquisition of neurons of a target organism. For example, the target organism may be a mouse, a rabbit, etc.

Referring to fig. 15, fig. 15 is a schematic structural diagram of an embodiment of a computer-readable storage medium provided in the present application. The computer-readable storage medium 150 stores a computer program 151, said computer program 151, when executed by a processor, implementing the method of:

acquiring a neuron signal, wherein the neuron signal comprises membrane potential data; performing derivation processing on the membrane potential data to obtain derivative data; at least one of a pulse position, a single pulse firing time range, and a continuous pulse firing time range of the neuron signal is determined from the derivative data.

It is understood that the computer-readable storage medium 150 in this embodiment is applied to the processing device 140, and specific implementation steps thereof may refer to the above embodiments, which are not described herein again.

In addition, the raw data in fig. 2, 5, 6, 9, 10, and 13 are the neuron signals in the present application.

Provided are a processing method and a processing device for a neuron signal and a readable storage medium. The method obtains derivative data by carrying out derivation processing on membrane potential data, on one hand, the pulse position in a neuron signal can be accurately determined by comparing the size between reference membrane potentials in the derivative data and the positive and negative of the reference membrane potentials, on the other hand, a membrane potential data segment is determined by adopting a left-right neighborhood mode on target reference membrane potential data in the derivative data, and the average value of the reference membrane potential data in the segment is used as the value of the target reference membrane potential, so that each piece of reference membrane potential data has an average value and can be determined as a single-pulse distribution range when meeting the range of the average value of a preset value, on the other hand, the membrane potential data segment is determined by adopting the left-right neighborhood mode on the target reference membrane potential data in the derivative data, and the average value of the reference membrane potential data in the segment is used as the value of the target reference potential, and the value of the target reference membrane potential is involved in the calculation of the next membrane potential data segment, so that the time corresponding to pulse distribution can be correlated, and the continuous pulse distribution time range can be further determined.

In summary, the processing method, the processing apparatus and the readable storage medium for neuron signals provided by the present application can improve the accuracy of collecting the pulse position, the single pulse release range and the continuous pulse release time range, so that the collected pulse position, the single pulse release range and the continuous pulse release time range are more comprehensive, and the activity of neurons can be reflected, thereby facilitating further modeling of neuron models, simulation of electrophysiological properties of neurons, and research on a pulse neural network.

In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other manners. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated units in the other embodiments described above may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.