On-chip brain neuron group analysis method, simulation platform and related device

Abstract

The invention relates to the technical field of on-chip brains, and provides an on-chip brain neuron group analysis method, a simulation platform and a related device. Performing structural analysis on the neurodynamic model to obtain a potential neural structural relationship; according to each preset initial state, performing dynamic prediction and analysis on the neurodynamic model to obtain dynamic prediction information corresponding to each initial state; according to a preset stimulation parameter range, carrying out feasible region analysis on the neurodynamic model to obtain corresponding feasible region information; and taking the neural structure relationship, all dynamic prediction information and feasible region information as an analysis result of the target neuron group. The neuron group is simulated and analyzed through the neurodynamics model, so that the analysis operation of the neuron group is simplified, and the analysis efficiency of the neuron group is improved.

Description

Technical Field

[0001] The present invention relates to the field of brain-on-a-chip technology, and in particular to an analysis method, simulation platform and related devices for a neuron population of a brain-on-a-chip. Background Art

[0002] In vitro cultured brain-on-chips are three-dimensional cell aggregates developed from human embryonic stem cells or induced pluripotent stem cells. They have certain brain tissue structure and functional properties in vitro and can simulate certain aspects of the human brain. The development of these brain-on-chips provides new tools for neuroscience research and disease research.

[0003] Currently, for in vitro cultured brain-on-chips, although some platforms can observe and record the state of neuronal groups based on microelectrode arrays, calcium ion imaging and other technologies, they cannot analyze the activity process of neuronal groups. However, the activity analysis of neuronal groups is very necessary and has important significance for research directions such as drug screening, disease modeling, and disease exploration of brain-on-chips. Summary of the invention

[0004] In view of this, an object of the present invention is to provide an analysis method, simulation platform and related devices for a brain-on-a-chip neuron population.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention provides a method for analyzing a brain-on-chip neuron population, which is applied to a simulation platform, wherein the simulation platform pre-stores a neural dynamics model for simulating a target neuron population, and the method for analyzing a brain-on-chip neuron population comprises:

[0007] Performing structural analysis on the neural dynamics model to obtain potential neural structural relationships; the neural structural relationships represent connection relationships between neurons in the target neuron population;

[0008] According to each preset initial state, the neural dynamics model is dynamically predicted and analyzed to obtain dynamic prediction information corresponding to each initial state; the dynamic prediction information represents the relationship between the discharge activity of the target neuron group starting from the initial state and the change over time;

[0009] According to a preset stimulation parameter range, a feasible domain analysis is performed on the neural dynamics model to obtain corresponding feasible domain information; the feasible domain information represents the state change range of the target neuron population under the stimulation parameter range;

[0010] The neural structure relationship, all dynamic prediction information and the feasible domain information are used as the analysis results of the target neuron group.

[0011] In an optional embodiment, each neuron in the target neuron population has a corresponding number;

[0012] The step of performing structural analysis on the neural dynamics model to obtain potential neural structural relationships includes:

[0013] A connection matrix is ​​obtained from the model parameters of the neural dynamics model, and the connection matrix is ​​expressed as follows:

[0014]

[0015] Among them, w ij represents the connection strength between the i-th neuron and the j-th neuron in the target neuron population;

[0016] The eigenvalues ​​of the connection matrix are calculated, and the distribution of the eigenvalues ​​is analyzed to obtain a spectral distribution map, and the spectral distribution map is used as the neural structure relationship.

[0017] In an optional embodiment, the step of dynamically predicting and analyzing the neural dynamics model according to each preset initial state to obtain dynamic prediction information corresponding to each initial state includes:

[0018] For any of the initial states, using the neural dynamics model to make predictions based on the initial state to obtain multiple neural state sequences;

[0019] Performing signal analysis and dimensionality reduction operations on the multiple neural state sequences to obtain a dynamic behavior matrix, and using the dynamic behavior matrix as dynamic prediction information corresponding to the initial state;

[0020] Each of the initial states is traversed to obtain dynamic prediction information corresponding to each of the initial states.

[0021] In an optional embodiment, the step of using the neural dynamics model to predict based on the initial state to obtain multiple neural state sequences includes:

[0022] Using the neural dynamics model to make predictions based on the initial state to obtain a first neural state sequence, and using the first neural state sequence as a current neural state sequence;

[0023] Using the neural dynamics model to predict based on the current neural state sequence, to obtain a next neural state sequence;

[0024] Counting the total number of neural state sequences obtained, and comparing the total number with a preset value;

[0025] If the total number does not reach the preset value, after taking the next neural state sequence as a new current neural state sequence, repeatedly performing the step of using the neural dynamics model to predict based on the current neural state sequence to obtain the next neural state sequence;

[0026] If the total number reaches the preset value, all neural state sequences are acquired to obtain the multiple neural state sequences.

[0027] In an optional embodiment, the step of performing feasible domain analysis on the neural dynamics model according to a preset stimulation parameter range to obtain corresponding feasible domain information includes:

[0028] Based on the stimulation parameter range, generating a stimulation parameter sequence including a plurality of stimulation parameters;

[0029] According to each of the stimulation parameters, a stimulation operation is performed on the neural dynamics model to obtain a neural response sequence corresponding to each of the stimulation parameters;

[0030] A state analysis is performed based on the neural response sequence corresponding to each of the stimulation parameters to obtain a neural state vector corresponding to each of the stimulation parameters, and a set containing all neural state vectors is used as the feasible domain information.

[0031] In an optional embodiment, the step of performing a stimulation operation on the neural dynamics model according to each of the stimulation parameters to obtain a neural response sequence corresponding to each of the stimulation parameters includes:

[0032] taking the first stimulation parameter in the stimulation parameter sequence as the target stimulation parameter;

[0033] Using the neural dynamics model to make predictions based on a preset original state to obtain an original neural state sequence, and using the original neural state sequence as a current neural response sequence;

[0034] generating a target stimulation sequence based on the target stimulation parameters, and using the neural dynamics model to predict based on the target stimulation sequence and the current neural response sequence to obtain a next neural response sequence;

[0035] Using the next neural response sequence as the neural response sequence corresponding to the target stimulation parameter;

[0036] If the target stimulation parameter is not the last stimulation parameter of the stimulation parameter sequence, then after taking the next stimulation parameter of the target stimulation parameter as a new target stimulation parameter and taking the next neural response sequence as a new current neural response sequence, repeatedly executing the steps of generating a target stimulation sequence based on the target stimulation parameter, and performing prediction based on the target stimulation sequence and the current neural response sequence using the neural dynamics model to obtain a next neural response sequence;

[0037] If the target stimulation parameter is the last stimulation parameter of the stimulation parameter sequence, a neural response sequence corresponding to each stimulation parameter is obtained.

[0038] In an optional embodiment, the neural dynamics model is obtained in the following manner:

[0039] Acquire a plurality of neural state sequence samples, and split each of the neural state sequence samples into a first neural state sequence and a second neural state sequence of equal length;

[0040] Acquire multiple neural response sample groups; each of the neural response sample groups includes a stimulation sequence, an original neural state, and a true neural response sequence;

[0041] For each of the first neural state sequences, using the to-be-trained model to predict based on the first neural state sequence to obtain a third neural state sequence, thereby obtaining each third neural state sequence;

[0042] For each of the neural response sample groups, using the to-be-trained model to make predictions based on the stimulation sequence and the original neural state in the neural response sample group to obtain an estimated neural response sequence, thereby obtaining each estimated neural response sequence;

[0043] Based on all second neural state sequences, all third neural state sequences, all true neural response sequences and all estimated neural response sequences, the model to be trained is trained to obtain the neural dynamics model.

[0044] In a second aspect, the present invention provides an analysis device for a brain-on-chip neuron population, which is applied to a simulation platform, wherein the simulation platform pre-stores a neural dynamics model for simulating a target neuron population, and the analysis device for a brain-on-chip neuron population comprises:

[0045] A neural analysis module, used to perform structural analysis on the neural dynamics model to obtain potential neural structural relationships; the neural structural relationships represent the connection relationships between neurons in the target neuron population;

[0046] According to each preset initial state, the neural dynamics model is dynamically predicted and analyzed to obtain dynamic prediction information corresponding to each initial state; the dynamic prediction information represents the relationship between the discharge activity of the target neuron group starting from the initial state and the change over time;

[0047] According to a preset stimulation parameter range, a feasible domain analysis is performed on the neural dynamics model to obtain corresponding feasible domain information; the feasible domain information represents the state change range of the target neuron population under the stimulation parameter range;

[0048] The result acquisition module is used to use the neural structure relationship, all dynamic prediction information and the feasible domain information as the analysis result of the target neuron group.

[0049] In a third aspect, the present invention provides a simulation platform, including a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the analysis method of the on-chip brain neuron population described in any one of the aforementioned embodiments is implemented.

[0050] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for analyzing a neuron population of a brain-on-chip as described in any one of the aforementioned embodiments.

[0051] The analysis method, simulation platform and related device of the on-chip brain neuron population provided by the present invention perform structural analysis on the neural dynamics model to obtain potential neural structural relationships; the neural structural relationships represent the connection relationships between neurons in the target neuron population; according to each preset initial state, the neural dynamics model is dynamically predicted and analyzed to obtain dynamic prediction information corresponding to each initial state; the dynamic prediction information represents the relationship between the discharge activity of the target neuron population starting from the initial state and the time change; according to the preset stimulation parameter range, the neural dynamics model is analyzed in a feasible domain to obtain the corresponding feasible domain information; the feasible domain information represents the state change range of the target neuron population under the stimulation parameter range; the neural structural relationship, all dynamic prediction information and feasible domain information are used as the analysis results of the target neuron population. The neuron population is simulated and analyzed by the neural dynamics model, thereby simplifying the analysis operation of the neuron population and improving the analysis efficiency of the neuron population.

[0052] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0054] Figure 1 One of the flow charts of the method for analyzing a brain-on-a-chip neuron population provided by an embodiment of the present invention is shown;

[0055] Figure 2 One of the exemplary diagrams of the method for analyzing a brain-on-a-chip neuron population provided by an embodiment of the present invention is shown;

[0056] Figure 3 A second exemplary diagram showing a method for analyzing a brain-on-a-chip neuron population provided by an embodiment of the present invention;

[0057] Figure 4 A third example diagram showing a method for analyzing a brain-on-a-chip neuron population provided by an embodiment of the present invention;

[0058] Figure 5 The second flowchart of the method for analyzing a brain-on-a-chip neuron population provided by an embodiment of the present invention is shown;

[0059] Figure 6 A functional module diagram of an on-chip brain neuron population analysis device provided by an embodiment of the present invention is shown;

[0060] Figure 7 A block diagram of a simulation platform provided by an embodiment of the present invention is shown.

[0061] Icon: 100 - simulation platform; 110 - bus; 120 - processor; 130 - memory; 150 - I / O module; 170 - communication module; 300 - on-chip brain neuron population analysis device; 310 - neural analysis module; 330 - result acquisition module. DETAILED DESCRIPTION

[0062] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0063] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0064] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0065] See also Figure 1 , is a flow chart of the method for analyzing the on-chip brain neuron population provided in an embodiment of the present invention.

[0066] Step S202, structural analysis is performed on the neural dynamics model to obtain potential neural structural relationships; the neural structural relationships represent the connection relationships between neurons in the target neuron population.

[0067] It can be understood that the embodiment of the present invention pre-constructs a simulation platform, which pre-stores a neurodynamic model, and the neurodynamic model can be used to simulate the neuronal population in the on-chip brain cultured in vitro, i.e., the target neuronal population, so that the neuronal population can be analyzed based on the neurodynamic model.

[0068] In this embodiment, the target neuron population simulated by the neurodynamic model can be structurally analyzed by structural analysis of the neurodynamic model, that is, the potential neural structural relationship can be obtained. This neural structural relationship can reflect the network structure characteristics of the target neuron population and the connection relationship between the neurons therein.

[0069] Step S204, dynamically predict and analyze the neural dynamics model according to each preset initial state to obtain dynamic prediction information corresponding to each initial state; the dynamic prediction information represents the relationship between the discharge activity of the target neuron group starting from the initial state and the change over time.

[0070] In this embodiment, multiple different initial states can be pre-set to dynamically predict and analyze the neural dynamics model, that is, to analyze the dynamic evolution process of the target neuron population simulated by the neural dynamics model, and then obtain the dynamic prediction information corresponding to each initial state. The dynamic prediction information can reflect the law of the discharge activity of the target neuron population starting from the initial state over time.

[0071] Step S206, according to the preset stimulation parameter range, the feasible domain analysis is performed on the neural dynamics model to obtain corresponding feasible domain information; the feasible domain information represents the state change range of the target neuron population under the stimulation parameter range.

[0072] In this embodiment, a stimulation parameter range of a stimulation type can be pre-set based on analysis needs, and the stimulation type can be electrical stimulation, light stimulation or drug stimulation, etc., which is not limited in this embodiment of the present invention. Then, based on the stimulation parameter range, the feasible domain analysis of the neurodynamic model is performed, that is, the reaction of the target neuron group simulated by the neurodynamic model when it is stimulated with the corresponding intensity is analyzed, and the corresponding feasible domain information is obtained. This feasible domain information can reflect the state changes that occur when the target neuron group is stimulated within a certain range.

[0073] Step S208, taking the neural structure relationship, all dynamic prediction information and feasible domain information as the analysis result of the target neuron group.

[0074] In this embodiment, the neural structure relationship is obtained through structural analysis, dynamic prediction information is obtained through dynamic prediction and analysis, and feasible domain information is obtained through feasible domain analysis, that is, the analysis result of the target neuron group is obtained.

[0075] It can be seen that based on the above steps, the neural dynamics model is structurally analyzed to obtain the potential neural structural relationship; the neural structural relationship represents the connection relationship between the neurons in the target neuron population; according to each preset initial state, the neural dynamics model is dynamically predicted and analyzed to obtain the dynamic prediction information corresponding to each initial state; the dynamic prediction information represents the relationship between the discharge activity of the target neuron population starting from the initial state and the time change; according to the preset stimulation parameter range, the neural dynamics model is feasible domain analyzed to obtain the corresponding feasible domain information; the feasible domain information represents the state change range of the target neuron population under the stimulation parameter range; the neural structural relationship, all dynamic prediction information and feasible domain information are used as the analysis results of the target neuron population. By simulating and analyzing the neuron population through the neural dynamics model, the analysis operation of the neuron population is simplified and the analysis efficiency of the neuron population is improved.

[0076] Optionally, for step S202, an embodiment of the present invention provides a possible implementation method.

[0077] Step S202-1, obtaining a connection matrix from the model parameters of the neural dynamics model, the connection matrix is ​​expressed as follows:

[0078]

[0079] Among them, w ij Represents the connection strength between the i-th neuron and the j-th neuron in the target neuron population.

[0080] Step S202-3, calculating the eigenvalues ​​of the connection matrix, and analyzing the distribution of the eigenvalues ​​to obtain a spectral distribution map, and using the spectral distribution map as the neural structure relationship.

[0081] It is understandable that, since the neurodynamic model is used to simulate the target neuron population, some model parameters of the neurodynamic model can reflect the characteristics of the target neuron population. The embodiment of the present invention utilizes this point to perform structural analysis through the model parameters of the neurodynamic model.

[0082] In this embodiment, a connection matrix can be obtained from the model parameters of the neurodynamic model, and the connection matrix can reflect the connection strength between each neuron in the target neuron population. For example, if there is a connection between two neurons, the element at the corresponding position in the connection matrix is ​​a non-zero element. And this connection strength can also be understood as a weight. Based on the fact that neurons transmit signals through neurotransmitters, the size of the connection strength is the size of the weight, which determines the transmission strength of the signal between neurons.

[0083] Then, the eigenvalues ​​are calculated based on the connection matrix and the distribution of these eigenvalues ​​is analyzed. The distribution of these eigenvalues ​​is then displayed graphically, that is, a spectral distribution diagram is obtained and used as a neural structure relationship. It can be understood that based on the distribution of the eigenvalues ​​shown in the spectral distribution diagram, information such as the stability, robustness, and oscillation behavior of the target neuron population can be analyzed.

[0084] For ease of understanding, an example of a spectrum distribution diagram is provided in the embodiment of the present invention. Figure 2 . Among them, the horizontal coordinate in the spectral distribution diagram represents the real part of the eigenvalue, and the vertical coordinate represents the imaginary part of the eigenvalue. Based on the fact that the eigenvalues ​​in the spectral distribution diagram are mainly concentrated in the area where the real part is less than or equal to zero, this indicates that the target neuron population has good stability. Based on the compact distribution of the eigenvalues ​​in the spectral distribution diagram, this indicates that the target neuron population has a certain robustness to small disturbances. Based on the fact that the eigenvalues ​​in the spectral distribution diagram are mainly concentrated near the real axis and there is no significant imaginary part, this indicates that the target neuron population will not exhibit strong oscillatory behavior.

[0085] Optionally, for step S204, an embodiment of the present invention provides a possible implementation method.

[0086] Step S204-1, for any initial state, a neural dynamics model is used to make predictions based on the initial state to obtain multiple neural state sequences.

[0087] Step S204-3, performing signal analysis and dimensionality reduction operations on multiple neural state sequences to obtain a dynamic behavior matrix, and using the dynamic behavior matrix as dynamic prediction information corresponding to the initial state.

[0088] Step S204-5, traverse each initial state to obtain dynamic prediction information corresponding to each initial state.

[0089] In this embodiment, for each initial state, a neural dynamics model can be used to predict based on the initial state to obtain multiple neural state sequences. These multiple neural state sequences can be understood as the predicted activity state of the target neuron population in the future when it is active from the initial state.

[0090] Then, the multiple neural state sequences are subjected to signal analysis and dimensionality reduction operations. For example, the PCA (Principal Component Analysis) algorithm can be used to process the multiple neural state sequences, that is, to extract the main components and convert them into low-dimensional data such as 2D or 3D data, that is, to obtain the dynamic behavior matrix and use it as the dynamic prediction information corresponding to the initial state. In a similar manner, each initial state is processed to obtain the dynamic prediction information corresponding to each initial state.

[0091] It is understandable that in order to facilitate the intuitive display of dynamic prediction information, visualization operations can be performed based on the dynamic behavior matrix to display the spatial distribution of these main components. For example, analyzing whether its dynamic information presents topological structures such as limit cycle diagrams or attractor diagrams is convenient for analyzing the dynamic characteristics of neuron groups. For ease of understanding, the embodiment of the present invention provides an example low-dimensional manifold diagram for on-chip brain neural activity, please refer to Figure 3 . The three coordinate axes in the low-dimensional manifold example diagram represent the first principal component, the second principal component, and the third principal component. And the behavior trajectory displayed based on the low-dimensional manifold example diagram is closed and cyclical, which indicates that the discharge activity of the target neuron group changes periodically over time, and its discharge pattern is regular, that is, periodic discharge.

[0092] Optionally, for the process of using the neural dynamics model to make predictions based on the initial state to obtain multiple neural state sequences in step S204-1, an embodiment of the present invention provides a possible implementation method.

[0093] Step S204-1-1, using the neural dynamics model to make predictions based on the initial state, obtaining a first neural state sequence, and using the first neural state sequence as the current neural state sequence.

[0094] Step S204-1-3, using the neural dynamics model to predict based on the current neural state sequence to obtain the next neural state sequence.

[0095] Step S204-1-5, counting the total number of neural state sequences obtained, and comparing the total number with a preset value.

[0096] Step S204-1-7A, if the total number does not reach the preset value, the next neural state sequence is taken as the new current neural state sequence, and step S204-1-3 is repeated.

[0097] Step S204-1-7B, if the total number reaches a preset value, all neural state sequences are obtained to obtain multiple neural state sequences.

[0098] It can be understood that the neural dynamics model processes each initial state in a similar manner. For the sake of brevity, an initial state is used as an example to illustrate below.

[0099] In this embodiment, the neural dynamics model is first used to predict based on the initial state to obtain the first neural state sequence; then the neural dynamics model is used to predict based on the first neural state sequence to obtain the second neural state sequence; then the neural dynamics model is used to predict based on the second neural state sequence to obtain the third neural state sequence. That is, the neural dynamics model is continuously used to predict based on the nth neural state sequence to obtain the n+1th neural state sequence, and this cycle is repeated until the total number of neural state sequences obtained reaches a preset value, and multiple neural state sequences are obtained.

[0100] It can be understood that the embodiment of the present invention is to predict the next neural state sequence based on the neural state sequence output last time by giving an initial state and using the neural dynamics model to continuously predict based on the initial state, thereby being able to simulate the activity state of the target neuron group for a long period of time in the future.

[0101] Optionally, for step S206, an embodiment of the present invention provides a possible implementation method.

[0102] Step S206 - 1 : generating a stimulation parameter sequence including a plurality of stimulation parameters based on the stimulation parameter range.

[0103] Step S206-3, performing stimulation operation on the neural dynamics model according to each stimulation parameter to obtain a neural response sequence corresponding to each stimulation parameter.

[0104] Step S206-5, performing state analysis based on the neural response sequence corresponding to each stimulation parameter, obtaining the neural state vector corresponding to each stimulation parameter, and taking the set containing all neural state vectors as feasible domain information.

[0105] In this embodiment, based on a preset stimulation parameter range, multiple stimulation parameters may be generated at certain intervals based on the minimum value and the maximum value of the stimulation parameter range and the multiple stimulation parameters may be sorted, that is, a stimulation parameter sequence may be obtained.

[0106] Then, according to the stimulation parameter sequence, a stimulation parameter is selected in turn to stimulate the neural dynamics model to output the corresponding neural response sequence. That is, the activity state of the target neuron group after receiving the corresponding stimulation is simulated. Then, based on each stimulation parameter, the neural response sequence corresponding to each stimulation parameter can be obtained.

[0107] Based on the state analysis of the neural response sequence corresponding to each stimulation parameter, the neural state vector corresponding to each stimulation parameter is obtained. For example, the neural state vector may include the firing rate of three channels, where the firing rate is a parameter used to represent the frequency of neuron group activity, that is, the neural state vector represents the activity state of the neuron group in three dimensions. Obtaining the neural state vector corresponding to all stimulation parameters means obtaining the feasible domain information.

[0108] It is understandable that in order to facilitate the intuitive display of feasible domain information, visualization operations can be performed based on all the obtained neural state vectors to display the state change range of the neuron population. For ease of understanding, an example diagram is provided in the embodiment of the present invention, please refer to Figure 4 The three coordinate axes in this example diagram represent the firing rate of the first channel, the firing rate of the second channel, and the firing rate of the third channel, respectively. Different colors represent different stimulation types, and different color domains of the same color represent the feasible domain corresponding to different stimulation ranges under the same stimulation type, that is, the state change range of the neuron population.

[0109] Optionally, for step S206-3, an embodiment of the present invention provides a possible implementation method.

[0110] Step S206-3-1, taking the first stimulation parameter in the stimulation parameter sequence as the target stimulation parameter;

[0111] Step S206-3-3, using the neural dynamics model to make predictions based on the preset original state, obtaining an original neural state sequence, and using the original neural state sequence as the current neural response sequence.

[0112] Step S206-3-5, generating a target stimulation sequence based on the target stimulation parameters, and using the neural dynamics model to predict based on the target stimulation sequence and the current neural response sequence to obtain the next neural response sequence.

[0113] Step S206-3-7, taking the next neural response sequence as the neural response sequence corresponding to the target stimulation parameters.

[0114] Step S206-9A, if the target stimulation parameter is not the last stimulation parameter of the stimulation parameter sequence, then the next stimulation parameter of the target stimulation parameter is used as the new target stimulation parameter, and the next neural response sequence is used as the new current neural response sequence, and then step S206-3-5 is repeated.

[0115] Step S206-9B: if the target stimulation parameter is the last stimulation parameter of the stimulation parameter sequence, a neural response sequence corresponding to each stimulation parameter is obtained.

[0116] In this embodiment, the first stimulation parameter in the stimulation parameter sequence is first obtained, and the neural dynamics model is used to predict based on the preset original state to obtain the original neural state sequence. Then the first stimulation sequence is generated based on the first stimulation parameter. For example, if the first stimulation parameter is 5Hz and 10mV, the first stimulation sequence generated is (10, 10, 10, 10, 10), which represents an electrical stimulation pulse with a duration of 1 second and an intensity of 10mV every 200 milliseconds. And the neural dynamics model is used to predict based on the first stimulation sequence and the original neural state sequence to obtain the neural response sequence corresponding to the first stimulation sequence.

[0117] Then, a second stimulation sequence is generated based on the second stimulation parameter, and the neural response sequence corresponding to the second stimulation sequence and the first stimulation sequence is predicted using the neural dynamics model to obtain the neural response sequence corresponding to the second stimulation parameter. That is, the stimulation sequence generated by the nth stimulation parameter and the neural response sequence corresponding to the n-1th stimulation parameter are continuously predicted using the neural dynamics model to obtain the neural response sequence corresponding to the nth stimulation parameter, and this cycle is repeated until all stimulation parameters of the stimulation parameter sequence are traversed, and the neural response sequence corresponding to each stimulation parameter is obtained.

[0118] It can be understood that the embodiment of the present invention simulates multiple stimulation operations on the target neuron group by sequentially inputting multiple stimulation sequences into the neurodynamic model, and uses the neurodynamic model to predict the next neural response sequence based on the neural response sequence output last time and the stimulation sequence input this time. Thus, the response of the target neuron group after receiving the cumulative number of stimulations can be simulated.

[0119] Optionally, based on the above neural dynamics model, the embodiment of the present invention further provides an implementation method for obtaining the neural dynamics model, please refer to Figure 5 It should be understood that the embodiment of the present invention is to implement the above-mentioned brain-on-chip neuron population analysis method through the neural dynamics model after training the neural dynamics model according to the following steps.

[0120] Step S212: Acquire multiple neural state sequence samples, and split each neural state sequence sample into a first neural state sequence and a second neural state sequence of equal length.

[0121] Step S214, obtaining multiple neural response sample groups; each neural response sample group includes a stimulation sequence, an original neural state, and a real neural response sequence.

[0122] Step S216: for each first neural state sequence, the model to be trained is used to predict based on the first neural state sequence to obtain a third neural state sequence, thereby obtaining each third neural state sequence.

[0123] Step S218, for each neural response sample group, using the model to be trained to make predictions based on the stimulation sequence and the original neural state in the neural response sample group to obtain an estimated neural response sequence, thereby obtaining each estimated neural response sequence.

[0124] Step S220, based on all second neural state sequences, all third neural state sequences, all true neural response sequences and all estimated neural response sequences, the model to be trained is trained to obtain a neural dynamics model.

[0125] In this embodiment, the real activity state of the neuron population in the in vitro cultured brain-on-chip can be recorded first, and multiple neural state sequence samples can be obtained. Each neural state sequence sample is split, that is, the first half of the neural state sequence sample is used as the first neural state sequence, and the second half is used as the second neural state sequence, so that the first neural state sequence and the second neural state sequence of the same length are obtained. That is, the time length represented by the first neural state sequence is equal to that represented by the second neural state sequence.

[0126] Then, multiple stimulation sequences can be set based on different stimulation types. For each stimulation sequence, the neuron population in the in vitro cultured brain-on-chip is stimulated using the stimulation sequence, and the state of the neuron population before stimulation is recorded to obtain the original neural state, and the activity state of the neuron population after stimulation for a period of time is recorded to obtain the real neural response sequence, and then a neural response sample group consisting of the stimulation sequence, the original neural state and the real neural response sequence is obtained, that is, each neural response sample group is obtained.

[0127] Then, the model to be trained is used to predict based on each first neural state sequence to obtain each third neural state sequence; and the model to be trained is used to predict based on the stimulation sequence and the original neural state in each neural response sample group to obtain each estimated neural response sequence. Finally, based on all second neural state sequences, all third neural state sequences, all real neural response sequences and all estimated neural response sequences, the model to be trained is trained to obtain a neural dynamics model.

[0128] It is understandable that the model to be trained can adopt the structure of encoder-recurrent neural network-decoder. The recurrent neural network RNN ​​(Recurrent Neural Network) can process sequence data and has a memory function. Among them, the encoder is composed of two layers of neural networks and is used to encode the activity state of the neuron group to obtain the initial hidden state; the recurrent neural network is used to generate multi-step hidden states based on the initial hidden state prediction and stimulus signal; the encoder is used to decode the multi-step hidden state to output the state sequence of the neuron group.

[0129] Moreover, during the model training process, the recurrent neural network mainly learns the state transition rule f with biological information prior, that is, based on the Dale principle to constrain the weight connection relationship of the recurrent neural network. The weight connection relationship can be classified into multiple excitatory neuron groups or multiple inhibitory neuron groups in a masked manner. The constraints of the weight connection relationship are as follows:

[0130] (1) Non-negative constraints on the input weight matrix Win and the output weight matrix Wout. That is, all elements in the input weight matrix Win (the weight matrix from the input layer to the hidden layer of the recurrent neural network) are not negative, and all elements in the output weight matrix Wout (the weight matrix from the hidden layer to the output layer of the recurrent neural network) are not negative. This restriction can be achieved by the ReLU (Rectified Linear Unit) activation function.

[0131] (2) Iteration constraints of the cyclic connection weight matrix Wrec (i.e., the connection matrix mentioned above) inside the hidden layer:

[0132]

[0133] in, represents the connection matrix after iteration; W rec represents the connection matrix before iteration; [W rec ] + ReLU activation is used to activate the connection matrix W before iteration rec Calculation is performed to satisfy the non-negative constraint; D represents a diagonal matrix, the elements on the diagonal of the diagonal matrix are 1 and -1, and the ratio between the total number of elements that are 1 and the total number of elements that are -1 is 4:1.

[0134] The Dale principle states that a neuron usually releases only one type of neurotransmitter, either excitatory or inhibitory. 1 is used to represent excitability and -1 is used to represent inhibition. The role of the diagonal matrix D is to limit the ratio of excitatory neurons to inhibitory neurons in the simulated neuron population to 4:1. In this way, through non-negative constraints and the Dale principle, the model can simulate excitatory and inhibitory neural connections, which helps to improve the model's predictive ability.

[0135] In order to execute the corresponding steps in the above embodiments and various possible methods, an implementation method of an on-chip brain neuron population analysis device is provided below. Figure 6 , is a functional module diagram of the on-chip brain neuron population analysis device provided in an embodiment of the present invention. It should be noted that the basic principle and technical effect of the on-chip brain neuron population analysis device 300 provided in this embodiment are the same as those in the above embodiment. For the sake of brief description, the corresponding contents in the above embodiment may be referred to for the parts not mentioned in this embodiment. The on-chip brain neuron population analysis device 300 includes:

[0136] A neural analysis module 310 is used to perform structural analysis on the neural dynamics model to obtain potential neural structural relationships; the neural structural relationships represent the connection relationships between neurons in the target neuron population;

[0137] According to each preset initial state, the neural dynamics model is dynamically predicted and analyzed to obtain dynamic prediction information corresponding to each initial state; the dynamic prediction information represents the relationship between the discharge activity of the target neuron group starting from the initial state and the change over time;

[0138] According to the preset stimulation parameter range, the feasible domain analysis of the neural dynamics model is performed to obtain the corresponding feasible domain information; the feasible domain information represents the state change range of the target neuron population under the stimulation parameter range;

[0139] The result acquisition module 330 is used to use the neural structure relationship, all dynamic prediction information and feasible domain information as the analysis result of the target neuron group.

[0140] Optionally, the neural analysis module 310 is further used to obtain a connection matrix from model parameters of the neural dynamics model, where the connection matrix is ​​expressed as follows:

[0141]

[0142] Among them, w ij It represents the connection strength between the ith neuron and the jth neuron in the target neuron population; the eigenvalues ​​of the connection matrix are calculated, and the distribution of the eigenvalues ​​is analyzed to obtain a spectral distribution map, and the spectral distribution map is used as the neural structure relationship.

[0143] Optionally, the neural analysis module 310 is also used to: for any initial state, use the neural dynamics model to make predictions based on the initial state to obtain multiple neural state sequences; perform signal analysis and dimensionality reduction operations on the multiple neural state sequences to obtain a dynamic behavior matrix, and use the dynamic behavior matrix as dynamic prediction information corresponding to the initial state; traverse each initial state to obtain dynamic prediction information corresponding to each initial state.

[0144] Optionally, the neural analysis module 310 is also used to: use the neural dynamics model to make predictions based on the initial state to obtain a first neural state sequence, and use the first neural state sequence as the current neural state sequence; use the neural dynamics model to make predictions based on the current neural state sequence to obtain the next neural state sequence; count the total number of neural state sequences obtained, and compare the total number with a preset value; if the total number does not reach the preset value, use the next neural state sequence as the new current neural state sequence, and repeat the process of using the neural dynamics model to make predictions based on the current neural state sequence to obtain the next neural state sequence; if the total number reaches the preset value, obtain all neural state sequences to obtain multiple neural state sequences.

[0145] Optionally, the neural analysis module 310 is also used to: generate a stimulation parameter sequence including multiple stimulation parameters based on the stimulation parameter range; perform stimulation operations on the neural dynamics model according to each stimulation parameter to obtain a neural response sequence corresponding to each stimulation parameter; perform state analysis based on the neural response sequence corresponding to each stimulation parameter to obtain a neural state vector corresponding to each stimulation parameter, and use the set containing all neural state vectors as feasible domain information.

[0146] Optionally, the neural analysis module 310 is also used to: use the first stimulation parameter in the stimulation parameter sequence as the target stimulation parameter; use the neural dynamics model to predict based on the preset original state to obtain the original neural state sequence, and use the original neural state sequence as the current neural response sequence; generate the target stimulation sequence based on the target stimulation parameter, and use the neural dynamics model to predict based on the target stimulation sequence and the current neural response sequence to obtain the next neural response sequence; use the next neural response sequence as the neural response sequence corresponding to the target stimulation parameter; if the target stimulation parameter is not the last stimulation parameter of the stimulation parameter sequence, then use the next stimulation parameter of the target stimulation parameter as the new target stimulation parameter, and after using the next neural response sequence as the new current neural response sequence, repeat the process of generating the target stimulation sequence based on the target stimulation parameter, and use the neural dynamics model to predict based on the target stimulation sequence and the current neural response sequence to obtain the next neural response sequence; if the target stimulation parameter is the last stimulation parameter of the stimulation parameter sequence, then obtain the neural response sequence corresponding to each stimulation parameter.

[0147] See also Figure 7 , is a block diagram of a simulation platform provided by an embodiment of the present invention. The simulation platform 100 includes a bus 110 , a processor 120 , a memory 130 , an I / O module 150 and a communication module 170 .

[0148] The bus 110 is a circuit that connects the above elements to each other and transmits signals between the above elements.

[0149] The processor 120 can receive commands from the above-mentioned other elements (such as the memory 130, the I / O module 150, the communication module 170, etc.) through the bus 110, can interpret the received commands, and can perform calculations or data processing according to the interpreted commands. The processor 120 can be an integrated circuit chip with signal processing capabilities. The processor 120 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0150] The memory 130 may store commands or data received from the processor 120 or other components (e.g., the I / O module 150, the communication module 170, etc.) or commands or data generated by the processor 120 or other components. The memory 130 may be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), or an electrically erasable read-only memory (EEPROM).

[0151] The I / O module 150 may receive commands or data input from a user via input-output means (e.g., a sensor, a keyboard, a touch screen, etc.), and may transmit the received commands or data to the processor 120 or the memory 130 via the bus 110. It is also used to display various information (e.g., multimedia data, text data) received, stored, and processed from the above elements, and may display videos, images, data, etc. to the user.

[0152] The communication module 170 may be used to communicate signals or data with other devices.

[0153] Understandably, Figure 7 The structure shown is only a schematic diagram of the structure of the simulation platform 100. The simulation platform 100 may also include Figure 7 More or fewer components as shown, or with Figure 7 Different configurations shown. Figure 7 Each component shown in the figure can be implemented by hardware, software or a combination thereof.

[0154] The simulation platform provided by the embodiment of the present invention may be a personal computer, a tablet computer, a netbook, etc., and the embodiment of the present invention is not limited to this.

[0155] The memory in the simulation platform provided by the embodiment of the present invention stores a computer program, and when the processor executes the computer program, the method for analyzing the on-chip brain neuron population disclosed by the embodiment of the present invention is implemented.

[0156] An embodiment of the present invention further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method for analyzing the on-chip brain neuron population disclosed in the embodiment of the present invention is implemented.

[0157] In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, program segment or a part of the code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0158] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0159] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for analyzing a brain-on-a-chip neuron population, characterized in that: Applied to a simulation platform, the simulation platform pre-stores a neural dynamics model for simulating a target neuron population, and the analysis method of the on-chip brain neuron population includes: Performing structural analysis on the neural dynamics model to obtain potential neural structural relationships; the neural structural relationships represent connection relationships between neurons in the target neuron population; According to each preset initial state, the neural dynamics model is dynamically predicted and analyzed to obtain dynamic prediction information corresponding to each initial state; the dynamic prediction information represents the relationship between the discharge activity of the target neuron group starting from the initial state and the change over time; According to a preset stimulation parameter range, a feasible domain analysis is performed on the neural dynamics model to obtain corresponding feasible domain information; the feasible domain information represents the state change range of the target neuron population under the stimulation parameter range; The neural structure relationship, all dynamic prediction information and the feasible domain information are used as the analysis results of the target neuron group.

2. The method for analyzing brain-on-a-chip neuron population according to claim 1, characterized in that: Each neuron in the target neuron population has a corresponding number; The step of performing structural analysis on the neural dynamics model to obtain potential neural structural relationships includes: A connection matrix is ​​obtained from the model parameters of the neural dynamics model, and the connection matrix is ​​expressed as follows: Among them, w ij represents the connection strength between the i-th neuron and the j-th neuron in the target neuron population; The eigenvalues ​​of the connection matrix are calculated, and the distribution of the eigenvalues ​​is analyzed to obtain a spectral distribution map, and the spectral distribution map is used as the neural structure relationship.

3. The method for analyzing a brain-on-a-chip neuron population according to claim 1, characterized in that: The step of dynamically predicting and analyzing the neural dynamics model according to each preset initial state to obtain dynamic prediction information corresponding to each initial state includes: For any of the initial states, using the neural dynamics model to make predictions based on the initial state to obtain multiple neural state sequences; Performing signal analysis and dimensionality reduction operations on the multiple neural state sequences to obtain a dynamic behavior matrix, and using the dynamic behavior matrix as dynamic prediction information corresponding to the initial state; Each of the initial states is traversed to obtain dynamic prediction information corresponding to each of the initial states.

4. The method for analyzing a brain-on-a-chip neuron population according to claim 3, characterized in that: The step of using the neural dynamics model to predict based on the initial state to obtain multiple neural state sequences includes: Using the neural dynamics model to make predictions based on the initial state to obtain a first neural state sequence, and using the first neural state sequence as a current neural state sequence; Using the neural dynamics model to predict based on the current neural state sequence, to obtain a next neural state sequence; Counting the total number of neural state sequences obtained, and comparing the total number with a preset value; If the total number does not reach the preset value, after taking the next neural state sequence as a new current neural state sequence, repeatedly performing the step of using the neural dynamics model to predict based on the current neural state sequence to obtain the next neural state sequence; If the total number reaches the preset value, all neural state sequences are acquired to obtain the multiple neural state sequences.

5. The method for analyzing a brain-on-a-chip neuron population according to claim 1, characterized in that: The step of performing feasible domain analysis on the neural dynamics model according to the preset stimulation parameter range to obtain corresponding feasible domain information includes: Based on the stimulation parameter range, generating a stimulation parameter sequence including a plurality of stimulation parameters; According to each of the stimulation parameters, a stimulation operation is performed on the neural dynamics model to obtain a neural response sequence corresponding to each of the stimulation parameters; A state analysis is performed based on the neural response sequence corresponding to each of the stimulation parameters to obtain a neural state vector corresponding to each of the stimulation parameters, and a set containing all neural state vectors is used as the feasible domain information.

6. The method for analyzing a brain-on-a-chip neuron population according to claim 5, characterized in that: The step of performing stimulation operation on the neural dynamics model according to each stimulation parameter to obtain a neural response sequence corresponding to each stimulation parameter includes: taking the first stimulation parameter in the stimulation parameter sequence as the target stimulation parameter; Using the neural dynamics model to make predictions based on a preset original state to obtain an original neural state sequence, and using the original neural state sequence as a current neural response sequence; generating a target stimulation sequence based on the target stimulation parameters, and using the neural dynamics model to predict based on the target stimulation sequence and the current neural response sequence to obtain a next neural response sequence; Using the next neural response sequence as the neural response sequence corresponding to the target stimulation parameter; If the target stimulation parameter is not the last stimulation parameter of the stimulation parameter sequence, then after taking the next stimulation parameter of the target stimulation parameter as a new target stimulation parameter and taking the next neural response sequence as a new current neural response sequence, repeatedly executing the steps of generating a target stimulation sequence based on the target stimulation parameter, and performing prediction based on the target stimulation sequence and the current neural response sequence using the neural dynamics model to obtain a next neural response sequence; If the target stimulation parameter is the last stimulation parameter of the stimulation parameter sequence, a neural response sequence corresponding to each stimulation parameter is obtained.

7. The method for analyzing a brain-on-a-chip neuron population according to claim 1, characterized in that: The neural dynamics model is obtained as follows: Acquire a plurality of neural state sequence samples, and split each of the neural state sequence samples into a first neural state sequence and a second neural state sequence of equal length; Acquire multiple neural response sample groups; each of the neural response sample groups includes a stimulation sequence, an original neural state, and a true neural response sequence; For each of the first neural state sequences, using the to-be-trained model to predict based on the first neural state sequence to obtain a third neural state sequence, thereby obtaining each third neural state sequence; For each of the neural response sample groups, using the to-be-trained model to make predictions based on the stimulation sequence and the original neural state in the neural response sample group to obtain an estimated neural response sequence, thereby obtaining each estimated neural response sequence; Based on all second neural state sequences, all third neural state sequences, all true neural response sequences and all estimated neural response sequences, the model to be trained is trained to obtain the neural dynamics model.

8. An analysis device for a brain-on-a-chip neuron population, characterized in that: Applied to a simulation platform, the simulation platform pre-stores a neural dynamics model for simulating a target neuron population, and the analysis device of the brain-on-chip neuron population includes: A neural analysis module, used to perform structural analysis on the neural dynamics model to obtain potential neural structural relationships; the neural structural relationships represent the connection relationships between neurons in the target neuron population; According to each preset initial state, the neural dynamics model is dynamically predicted and analyzed to obtain dynamic prediction information corresponding to each initial state; the dynamic prediction information represents the relationship between the discharge activity of the target neuron group starting from the initial state and the change over time; According to a preset stimulation parameter range, a feasible domain analysis is performed on the neural dynamics model to obtain corresponding feasible domain information; the feasible domain information represents the state change range of the target neuron population under the stimulation parameter range; The result acquisition module is used to use the neural structure relationship, all dynamic prediction information and the feasible domain information as the analysis result of the target neuron group.

9. A simulation platform, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the method for analyzing a brain-on-chip neuron population according to any one of claims 1 to 7 is implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the method for analyzing a brain-on-a-chip neuron population according to any one of claims 1 to 7.

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