CN114781258A - Method for constructing digital twin zebra fish nervous system based on calcium wave imaging signals - Google Patents
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Abstract
The invention provides a method for constructing a digital twin zebra fish nervous system based on a calcium wave imaging signal, which comprises the following steps: step 1, estimating probability distribution of neuron connection between different brain areas of zebra fish based on experimental data, and generating a network connection structure. And 2, calculating a neuron excitation inhibition index based on the zebra fish calcium wave imaging signal to obtain neuron type information. And 3, constructing a zebra fish neural network model based on the connection data and the neuron type information, and defining parameters needing assimilation. And 4, assimilating the parameters needing assimilation by using the cluster Kalman filtering until the correlation between the calcium wave data generated by the system and the experimental data is high enough. And 5, performing simulation test by using the assimilated system parameters, and repeating the step 4 if the correlation between the calcium wave signals generated by the system and the experimental data is weak until the conditions in the step 4 are met. And 6, taking the iterated assimilation parameters as system parameters to complete the construction of the zebra fish neuron system.
Description
Technical Field
The invention relates to the technical field of brain science research, in particular to a method for constructing a digital twin zebra fish nervous system based on a calcium wave imaging signal.
Background
Data assimilation algorithms are widely used for estimating system parameters and other anti-problems, such as a digital twin system for constructing a biological neural network based on observed signals. However, when the fine granularity of the signal reaches the level of a single neuron and the number of neurons is large, the methods are high in complexity and difficult to directly implement, and when the model structure cognition of the system is limited and the sampling frequency of the observed signal is low, the challenge of assimilation is huge.
The model animal zebra fish has nearly one hundred thousand neurons, can only observe calcium wave sequences of part of the neurons at present, and lacks sufficient knowledge on the precise connection structure of the neurons, the types of the neurons and modeling analysis of other unobserved neurons. Moreover, the obtained calcium wave sequence has low sampling frequency, so that the solving difficulty of inverse problems such as inverse system parameters and the like is high.
Based on experimental data and kinetic analysis, mathematical models of various neurons are proposed at present, from a simpler integrated excitation model to a more complex multi-chamber model. Although a more complex neuron model can reflect real neural activity better, the efficiency of large-scale simulation is extremely low due to the complexity of the neuron model, and a large calculation overhead is brought to data assimilation by the large parameter space of the neuron model. In addition, uncertainty also exists in the current modeling of a mechanism of generating calcium waves by zebra fish neuron discharge, and further difficulty is brought to assimilation. Furthermore, modeling the effect of dopamine modulating neurons on the system that lack the observed data also has large uncertainties.
The calcium signal reconstruction whole nervous system model based on the zebra fish partial neurons also provides a great challenge for the existing assimilation algorithm. Due to the nonlinearity of the model, a cluster Kalman Filter (EnKF, Ensemble Kalman Filter) is generally used as the data assimilation algorithm. However, in this case, the EnKF has a high computational complexity (the number of neurons and the number of dimensions of the state quantity of each neuron are in a cubic relationship), and it is necessary to reduce the complexity of the assimilation algorithm as much as possible while ensuring the assimilation effect, otherwise the computational overhead required for assimilation is not easily affordable.
The core problem of the patent is to obtain a calcium wave imaging time sequence (calcium fluorescence signal) of part of neurons of the zebra fish, and based on the calcium wave imaging and biological neuron models, a digital neuron system for generating similar calcium wave sequences is realized by using a numerical method, wherein unknown model parameters are approximated by a data assimilation algorithm. From the above discussion, this problem has the following difficulties: (1) the sampling frequency of the calcium wave signal is low (2Hz) and contains noise, (2) only part of the calcium wave signals of the neurons are recorded, (3) the connection structure, the type and the like of the neurons are difficult to observe, and (4) the computational complexity of simulating and assimilating a large-scale neuron system is high.
Disclosure of Invention
The invention is provided to solve the above problems, and aims to provide a method for constructing a digital twin zebra fish nervous system based on a calcium wave imaging signal.
The invention provides a method for constructing a digital twin zebra fish nervous system based on a calcium wave imaging signal, which is characterized by comprising the following steps of: step 1, estimating probability distribution of neuron connection between different brain regions of zebra fish based on experimental data, and randomly generating a network connection structure according to specified connectivity so as to obtain connection data; step 2, calculating neuron excitation inhibition indexes based on the zebra fish calcium wave imaging signals, and dividing the type of each neuron to obtain neuron type information; step 3, constructing a zebra fish neural network model by adopting a leakage integration excitation model and a four-channel synaptic connection model based on connection data and neuron type information, then superposing a dopamine regulation and control mechanism model and a calcium wave model on the model, and defining parameters needing assimilation in the system; step 4, using the adjusted Kalman filtering cluster to assimilate the parameters to be assimilated until the correlation between the calcium wave data generated by the system and the experimental data is high enough during assimilation, and obtaining the assimilated system parameters; step 5, carrying out simulation test by using the assimilated system parameters, observing the correlation between the calcium wave signals generated by the system and the experimental data, if the correlation is weaker, repeatedly executing the step 4 until the correlation between the calcium wave signals generated by the system and the experimental data is high enough during simulation, and obtaining the iterated assimilating parameters; and 6, taking the iterated assimilation parameters as system parameters to complete the construction of the zebra fish neuron system.
The method for constructing the digital twin zebra fish nervous system based on the calcium wave imaging signals, provided by the invention, can also have the following characteristics: in step 1, a probability distribution of connection of each neuron in each brain region to neurons in the same brain region and other brain regions is defined, and an adjacency list is generated according to a specified degree of connection.
In the method for constructing the digital twin zebra fish nervous system based on the calcium wave imaging signal, the method can also have the following characteristics: in step 2, the type of neuron is estimated based on the sign of the correlation between the calcium wave imaging signals between neurons.
In the method for constructing the digital twin zebra fish nervous system based on the calcium wave imaging signal, the method can also have the following characteristics: in step 3, the calcium wave model converts the neuron electrical signals into calcium signals.
In the method for constructing the digital twinned zebra fish nervous system based on the calcium wave imaging signal, the method can also have the following characteristics: in step 3, parameters which do not participate in assimilation are estimated by combining experimental data and priori knowledge, and the parameters which do not participate in assimilation are unknown system parameters.
In the method for constructing the digital twin zebra fish nervous system based on the calcium wave imaging signal, the method can also have the following characteristics: in step 4, the data assimilation process is as follows: and (3) reversely deducing the value of an unknown parameter in the model by the calcium wave data, and adjusting the initialization and assimilation hyper-parameter setting of the system according to an assimilation result until the correlation between the calcium wave data generated by the system and the experimental data is high enough during assimilation.
Action and Effect of the invention
According to the method for constructing the digital twin zebra fish nervous system based on the calcium wave imaging signal, the specific steps are as follows: step 1, estimating probability distribution of neuron connection between different brain regions of zebra fish based on experimental data, and randomly generating a network connection structure according to specified connectivity so as to obtain connection data; step 2, calculating neuron excitation inhibition indexes based on the calcium wave imaging signals of the zebra fish, and dividing the type of each neuron to obtain neuron type information; step 3, constructing a zebra fish neural network model by adopting a leakage integration excitation model and a four-channel synaptic connection model based on connection data and neuron type information, then superposing a dopamine regulation mechanism model and a calcium wave model on the model, and defining parameters needing assimilation in the system; step 4, using the adjusted Kalman filtering cluster to assimilate the parameters to be assimilated until the correlation between the calcium wave data generated by the system and the experimental data is high enough during assimilation, and obtaining the assimilated system parameters; step 5, carrying out simulation test by using the assimilated system parameters, observing the correlation between the calcium wave signals generated by the system and the experimental data, if the correlation is weaker, repeatedly executing the step 4 until the correlation between the calcium wave signals generated by the system and the experimental data is high enough during simulation, and obtaining the iterated assimilating parameters; and 6, taking the iterated assimilation parameters as system parameters to complete the construction of the zebra fish neuron system.
Therefore, the method is mainly used for modeling and simulating the time sequence of calcium wave imaging of the model animal zebra fish and carrying out data assimilation by combining a neuron network model to estimate system parameters so as to construct a digital twin zebra fish nervous system with the dynamic behavior close to that of real zebra fish, can be used for high-efficiency simulation biological experiments such as brain area ablation, drug stimulation and the like, and is verified with the existing experimental results. The quality of the assimilation results is measured by the correlation of the calcium wave sequences generated by the system simulation with experimental observations.
In addition, the invention can utilize the calcium signal experimental data of the zebra fish to efficiently model and assimilate so as to reconstruct the nervous system of the zebra fish and ensure that the assimilation system is close to a real system. Specifically, the complexity of a cluster Kalman filtering algorithm can be reduced, so that the method has feasibility on a large-scale nervous system; the simulation of the zebra fish nervous system can be efficiently realized by means of a graph neural network library; and a better data assimilation effect on the zebra fish nervous system based on limited observation data can be realized.
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Fig. 1 is a flow chart of a method for constructing a digital twin zebra fish nervous system based on a calcium wave imaging signal in an embodiment of the invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the following embodiments specifically describe the construction method of the digital twinborn zebra fish nervous system based on the calcium wave imaging signal in combination with the accompanying drawings.
In the embodiment, a method for constructing a digital twinned zebra fish nervous system based on a calcium wave imaging signal is provided.
Fig. 1 is a flow chart of a method for constructing a digital twin zebra fish nervous system based on a calcium wave imaging signal in an embodiment of the invention.
As shown in fig. 1, the method for constructing a digital twinned zebra fish nervous system based on a calcium wave imaging signal according to the embodiment includes the following steps:
and step S1, estimating the probability distribution of neuron connection between different brain areas of the zebra fish based on experimental data, randomly generating a network connection structure according to the specified connectivity, and obtaining connection data.
In this embodiment, the experimental data is data of the connection strength of each brain region of the zebra fish. In order to determine the connection structure of the neural network during simulation and assimilation, the probability distribution of the connection of the neurons between different brain areas is estimated based on experimental data, and the network connection structure is randomly generated, namely the probability distribution of the connection of each neuron to the same brain area and other brain areas can be defined for each neuron in each brain area, so that the connection degree of each neuron can be specified to generate network connection through one random simulation.
And step S2, calculating a neuron excitation inhibition index based on the calcium wave imaging signals of the zebra fish, and dividing the type of each neuron to obtain neuron type information.
Since the kind of each neuron (excitatory neuron or inhibitory neuron) is unknown, in order to determine the excitatory or inhibitory kind of a neuron, each neuron is an index of an inhibitory neuron given based on the calcium wave signal of each neuron calcium wave and its peripheral neurons, and the neuron kind is classified based on the experimentally measured excitatory or inhibitory ratio, that is, the kind of neuron is estimated from the sign of the correlation of calcium wave signals between neurons.
The following criteria are defined: and calculating the proportion of the number of each neuron which is less than a threshold value in relation to the calcium signal of the adjacent neuron to the total number of the adjacent neurons. The neighbor neurons of one neuron are the top k nearest neighbors in its spatial position (position information of each neuron is needed). And dividing the types of each neuron according to the ratio of excitatory neurons to inhibitory neurons in each brain region given by experimental data.
And step S3, constructing a zebra fish neural network model based on the connection data and the neuron type information, then superposing a dopamine regulation mechanism model and a calcium wave model on the model, and defining parameters needing assimilation in the system.
In this embodiment, a simpler leakage Integrated and Fire model (leak Integrated and Fire model) is selected to model the neuron soma of zebra fish, and a four-channel dopamine model is used to model synaptic connections between neurons. For the calcium wave model, based on the research of calcium signal fluorescence imaging, a simpler linear autoregressive model is selected. To take into account the effects of non-observed neurons on the system, a dopamine-regulated neuron firing threshold mechanism was constructed and the relevant parameters were assimilated. To ensure the feasibility of assimilation, the number of parameters required for assimilation was minimized.
The specific process is as follows: and constructing a zebra fish neural network model containing the parameters by adopting a leakage integration excitation model and a four-channel synaptic connection model based on the connection data and the neuron type information. And then a dopamine regulation mechanism model and a calcium wave model for converting the electrical signals of the neurons into calcium signals are superposed on the system. Parameters needing assimilation are defined, and unknown system parameters which do not participate in assimilation are estimated by combining experimental data and priori knowledge.
And step S4, carrying out data assimilation on the system by using the adjusted cluster Kalman filtering, namely, reversely deducing the value of the unknown parameter in the model from the experimental calcium wave data. And adjusting the initialization, assimilation hyper-parameters and other settings of the system according to the assimilation result until the correlation between the calcium wave generated by the system and the experimental data is high enough during assimilation, so as to obtain the assimilated system parameters.
In the embodiment, a traditional cluster Kalman filtering algorithm is improved, a scheme of dividing a system into a plurality of subsystems for assimilation is provided by combining a special structure of a neural network power system, the complexity of calculation is reduced from the third power related to the number of neurons to the linearity related to the number of the neurons, and parallelization is performed by means of a message transfer mechanism provided by a graph neural network calculation library, so that a large-scale analog assimilation numerical experiment can be efficiently performed.
And step S5, performing simulation test by using the system parameters obtained by the assimilation, observing the correlation between the calcium wave signals generated by the system and the experimental data, if the correlation is weaker, repeatedly executing the step S4 to reconfigure assimilation, and performing a new assimilation experiment until the system obtained by the simulation can generate a calcium wave sequence with high enough correlation with the experimental data, so as to obtain the iterated assimilation parameters.
And step S6, taking the assimilation parameter iterated finally as a system parameter to complete the construction of the zebra fish neuron system.
Effects and effects of the embodiments
According to the method for constructing the digital twinned zebra fish nervous system based on the calcium wave imaging signal, the specific steps are as follows: step 1, estimating probability distribution of neuron connection between different brain regions of zebra fish based on experimental data, and randomly generating a network connection structure according to specified connectivity so as to obtain connection data; step 2, calculating neuron excitation inhibition indexes based on the calcium wave imaging signals of the zebra fish, and dividing the type of each neuron to obtain neuron type information; step 3, constructing a zebra fish neural network model by adopting a leakage integration excitation model and a four-channel synaptic connection model based on connection data and neuron type information, then superposing a dopamine regulation and control mechanism model and a calcium wave model on the model, and defining parameters needing assimilation in the system; step 4, using the adjusted Kalman filtering cluster to assimilate the parameters to be assimilated until the correlation between the calcium wave data generated by the system and the experimental data is high enough during assimilation, and obtaining the assimilated system parameters; step 5, carrying out simulation test by using the assimilated system parameters, observing the correlation between the calcium wave signals generated by the system and the experimental data, if the correlation is weaker, repeatedly executing the step 4 until the correlation between the calcium wave signals generated by the system and the experimental data is high enough during simulation, and obtaining the iterated assimilating parameters; and 6, taking the iterated assimilation parameters as system parameters to complete the construction of the zebra fish neuron system.
Therefore, the embodiment is mainly used for modeling and simulating the time sequence of calcium wave imaging of the model animal zebra fish and carrying out data assimilation by combining a neural network model to estimate system parameters, so as to construct a digital twin zebra fish nervous system with the dynamic behavior close to that of a real zebra fish, and the digital twin zebra fish nervous system can be used for high-efficiency simulation biological experiments, such as brain area ablation, drug stimulation and the like, and is mutually verified with the existing experimental results. The quality of the assimilation results is measured by the correlation of the calcium wave sequences generated by the system simulation with experimental observations.
In order to efficiently model and assimilate by using experimental data of calcium signals of zebra fish to reconstruct a nervous system of the zebra fish and ensure that the assimilation system is close to a real system. Specifically, the complexity of the clustering Kalman filtering algorithm can be reduced, so that the clustering Kalman filtering algorithm has feasibility on a large-scale nervous system; the simulation of the zebra fish nervous system can be efficiently realized by means of a graph neural network library; and a better data assimilation effect on the zebra fish nervous system based on limited observation data can be realized.
The above embodiments are preferred examples of the present invention, and are not intended to limit the scope of the present invention.
Claims (6)
1. A construction method of a digital twin zebra fish nervous system based on a calcium wave imaging signal is characterized by comprising the following steps:
step 1, estimating probability distribution of neuron connection between different brain areas of zebra fish based on experimental data, and randomly generating a network connection structure according to specified connectivity so as to obtain connection data;
step 2, calculating neuron excitation inhibition indexes based on the calcium wave imaging signals of the zebra fish, and dividing the type of each neuron to obtain neuron type information;
step 3, constructing a zebra fish neural network model by adopting a leakage integration excitation model and a four-channel synaptic connection model based on the connection data and the neuron type information, then superposing a dopamine regulation and control mechanism model and a calcium wave model on the model, and defining parameters needing assimilation in the system;
step 4, carrying out data assimilation on the parameters needing assimilation by using the adjusted cluster Kalman filtering until the correlation between the calcium wave data generated by the system and the experimental data is high enough during assimilation, and obtaining the assimilated system parameters;
step 5, carrying out simulation test by using the assimilated system parameters, observing the correlation between a calcium wave signal generated by a system and the experimental data, if the correlation is weaker, repeatedly executing the step 4 until the correlation between the calcium wave signal generated by the system and the experimental data is high enough during simulation, and obtaining the iterated assimilative parameters;
and 6, taking the iterated assimilation parameters as system parameters to complete the construction of the zebra fish neuron system.
2. The method for constructing the digital twin zebrafish nervous system based on the calcium wave imaging signals as claimed in claim 1, wherein:
in step 1, for each neuron in each brain region, a probability distribution of its connection to neurons of the same brain region and other brain regions is defined, and an adjacency list is generated according to a specified degree of connection.
3. The method for constructing the digital twin zebrafish nervous system based on the calcium wave imaging signal according to claim 1, wherein:
wherein, in step 2, the type of the neuron is estimated based on the sign of the correlation of the calcium wave imaging signals between the neurons.
4. The method for constructing the digital twin zebrafish nervous system based on the calcium wave imaging signals as claimed in claim 1, wherein:
in step 3, the calcium wave model converts the electrical signal of the neuron into a calcium signal.
5. The method for constructing the digital twin zebrafish nervous system based on the calcium wave imaging signal according to claim 1, wherein:
wherein, in the step 3, the parameters which do not participate in assimilation are estimated by combining experimental data and prior knowledge,
the parameters not participating in assimilation are unknown system parameters.
6. The method for constructing the digital twin zebrafish nervous system based on the calcium wave imaging signals as claimed in claim 1, wherein:
in step 4, the data assimilation process is as follows: and (3) reversely deducing the value of an unknown parameter in the model by the calcium wave data, and adjusting the initialization and assimilation hyper-parameter setting of the system according to the assimilation result until the correlation between the calcium wave data generated by the system and the experimental data is high enough during assimilation.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116776941A (en) * | 2023-06-19 | 2023-09-19 | 浙江大学 | Neuron coding model parameter estimation method and device based on two-photon calcium imaging data |
| CN117095824A (en) * | 2023-10-19 | 2023-11-21 | 之江实验室 | Dopamine dynamic coupling method, device and equipment based on twin brain simulation model |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116776941A (en) * | 2023-06-19 | 2023-09-19 | 浙江大学 | Neuron coding model parameter estimation method and device based on two-photon calcium imaging data |
| CN116776941B (en) * | 2023-06-19 | 2024-04-26 | 浙江大学 | Neuron coding model parameter estimation method and device based on two-photon calcium imaging data |
| CN117095824A (en) * | 2023-10-19 | 2023-11-21 | 之江实验室 | Dopamine dynamic coupling method, device and equipment based on twin brain simulation model |
| CN117095824B (en) * | 2023-10-19 | 2024-04-16 | 之江实验室 | Dopamine dynamic coupling method, device and equipment based on twin brain simulation model |
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