CN112890833A - Method for predicting stimulation modes of different colors based on pigeon electroencephalogram signals - Google Patents
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Abstract
The invention discloses a method for predicting stimulation modes of different colors based on pigeon electroencephalogram signals, which comprises the following steps: (a) designing a stimulation mode for applying color stimulation to the pigeons, and recording the brain electrical signals of the pigeons; (b) carrying out PCA dimension reduction on the recorded pigeon electroencephalogram signals; (c) extracting Spike release rates of external striatum neurons of the pigeons to different color stimulation modes, and selecting a proper neuron channel; (d) constructing an LSTM prediction model according to the extracted neuron Spike rate characteristics of the pigeons; (e) optimizing the parameters of the constructed LSTM prediction model, and obtaining network characteristic parameters; (f) predicting the neuron Spike release rate of the pigeon according to the optimized network characteristic parameters; (g) evaluating the performance of the prediction model by utilizing correlation analysis; the invention adopts the multi-electrode array to record a plurality of neuron signals of different depths in the pigeon brain area, and the data is sufficient and reliable.
Description
Technical Field
The invention relates to a prediction method, in particular to a prediction method for different color stimulation modes based on pigeon electroencephalogram signals.
Background
A pigeon is a visual animal whose behavior is guided mainly by visual information obtained from the outside; in practical studies, pigeons have become ideal materials for studying the structural and functional characteristics of the visual system, on one hand because they avoid the disadvantages of poor visual ability of animals such as rats and the like and high price of experimental primates, and on the other hand because pigeons have excellent visual system and the ability to perceive and distinguish color stimuli; the off-apical pathway is a key pathway for bird visual information processing, and the visual apical cap is an extremely important nucleus pulposus in the off-apical pathway, which receives color information from retinal ganglion cells, encodes and integrates the information, and then maps the information to the thalamic nucleus rotundus.
In the study of the visual system, several models of retinal ganglion cell firing have been proposed; such as linear-nonlinear models, generalized linear models (GMLs), and Integer and Fire (IF) models; some of these provide a suitable trade-off between retinal functional replication and biological similarity; recent studies have proposed hybrid models that can go beyond the previous model performance, such as fine-tuning biological models with genetic algorithms, or using Deep Learning (DL) techniques. These DL methods have been shown to have wide application in biomedical fields, such as diagnosis of alzheimer's disease, seizure detection, etc. This success comes from recent advances in the field of machine learning, as well as the development of new deep artificial neural networks, layers and structures (e.g., convolutional neural networks, LSTM, generative adversarial networks, etc.). These new properties help to address the gradient disappearance problem and perform better in some cases. Some of these features include training techniques such as: parameter sharing is adopted, and the calculation cost of each layer is reduced; several alternative methods of sigmoid and hyperbolic tangent activation functions, such as ReLU (rectified linear unit), leakyreu, prilu (parametric rectified linear unit), or ELU (exponential linear unit) functions. Furthermore, the use of a GPU addresses the high computational cost of these machine learning systems, reducing the time required to train and tune the program.
Among these advances, LSTM has proven to be a powerful tool in learning the long-term dependency information problem; but it has not been applied to predicting the Spike firing rate of neurons in the external striatum region of the pigeon brain for different color stimuli.
Disclosure of Invention
The invention aims to: aiming at the fact that the LSTM is not applied to the prediction of the Spike release rate of neurons in the external striation region of the pigeon brain to different color stimuli at present, the method for predicting the stimulation modes of different colors based on the pigeon brain electrical signals is provided, and the problems are solved.
The technical scheme of the invention is as follows:
a method for predicting stimulation modes of different colors based on pigeon brain electrical signals comprises the following steps:
(a) designing a stimulation mode for applying color stimulation to the pigeons, and recording the brain electrical signals of the pigeons;
(b) carrying out PCA dimension reduction on the recorded pigeon electroencephalogram signals;
(c) extracting Spike release rates of external striatum neurons of the pigeons to different color stimulation modes, and selecting a proper neuron channel;
(d) constructing an LSTM prediction model according to the extracted neuron Spike rate characteristics of the pigeons;
(e) optimizing the parameters of the constructed LSTM prediction model, and obtaining network characteristic parameters;
(f) predicting the neuron Spike release rate of the pigeon according to the optimized network characteristic parameters;
(g) and evaluating the performance of the prediction model by utilizing correlation analysis.
Further, the stimulation pattern in step (a) is: the color center point is stimulated under the gray background and the gray center point is stimulated under the color background, and the colors are respectively red, green and blue.
Further, the PCA dimension reduction in the step (b) is to perform principal component analysis on the recorded pigeon brain electrical signals to reduce the dimension of the neuron Spike issuing sequence.
Further, the pigeon in the step (c) has two depths of the brain striatum area, the depth of the electrode array implantation is 4500um and 5000um, 64 neuron signals are recorded in total, and the neuron signals of the channel 4 and the channel 23 in the two depths are selected.
Further, the optimizing of step (e) comprises: and calculating the input and output characteristic dimension, the number of hidden layers, the network structure and the network training structure parameters of the prediction model.
Further, in the step (g), correlation analysis is performed on the prediction result and the real neuron Spike emitting sequence, so as to evaluate the performance of the prediction model.
Compared with the prior art, the invention has the beneficial effects that:
1. a method for predicting stimulation modes of different colors based on pigeon brain electrical signals adopts a multi-electrode array to record a plurality of neuron signals of different depths in a pigeon brain area, and data are sufficient and reliable.
2. A pigeon brain electrical signal based prediction method for different color stimulation modes is used for carrying out correlation analysis on a prediction result and a real neuron Spike release sequence, so that the performance of a prediction model is evaluated, and the feasibility of an experimental method is proved to be high.
Drawings
FIG. 1 is a flow chart of a method for predicting stimulation patterns of different colors based on pigeon brain electrical signals;
FIG. 2 is a diagram of the neuron Spike distribution of the channel 4 in the prediction method for different color stimulation modes based on the pigeon brain electrical signals when the stimulation and the stimulation withdrawal are carried out under the condition that the implantation depths of the electrodes are the same;
fig. 3 is a diagram of the neuron Spike distribution of the channel 23 in the prediction method of different color stimulation modes based on the pigeon brain electrical signals when the stimulation is given and removed under the condition of the same electrode implantation depth;
fig. 4 is a Loss curve graph in the process of training a prediction model in a prediction method for different color stimulation modes based on pigeon brain electrical signals.
Reference numerals:
Detailed Description
It is to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The features and properties of the present invention are described in further detail below with reference to examples.
Referring to fig. 1-3, a method for predicting stimulation patterns of different colors based on pigeon brain electrical signals includes the following steps:
(a) designing a stimulation mode for applying color stimulation to the pigeons, and recording the brain electrical signals of the pigeons; the stimulation pattern in step (a) is: stimulating the color center point under the gray background and stimulating the gray center point under the color background, applying the color stimulus to the pigeon, and recording neuron signals of the pigeon brain when the stimulus is applied and the stimulus is removed; the stimulation point is positioned in the receptive field of the pigeon; the colors are red, green and blue respectively; the color sequence is randomly disordered, one frame of gray screen is added between two frames, 6 frames of stimulation are carried out, each frame is repeated for 60 times, each frame lasts for 30 frames, and 21630 frames are total; 3 sets of data were recorded, stimulation frame size 16 x 16 pixel values, and screen refresh rate 100 Hz.
(b) Carrying out PCA dimension reduction on the recorded pigeon electroencephalogram signals; and (c) PCA dimensionality reduction in the step (b) is to perform principal component analysis on the recorded pigeon brain electrical signals to reduce the dimensionality of a neuron Spike issuing sequence.
(c) Extracting Spike release rates of external striatum neurons of the pigeons to different color stimulation modes, and selecting a proper neuron channel; the extrabrain striatum area of the pigeon in the step (c) has two depths, the implantation depth of the electrode array is 4500um and 5000um, 64 neuron signals are recorded in total, and neuron signals of a channel 4 and a channel 23 in the two depths are selected; the channel 4 and the channel 23 are used for emitting the neuron Spike when the electrode is implanted at the same depth for stimulating and withdrawing stimulation, as shown in fig. 2 and 3.
(d) Constructing an LSTM prediction model according to the extracted neuron Spike rate characteristics of the pigeons; the Loss curve in the process of training the prediction model is shown in FIG. 4; setting parameters of a prediction model, inputting dimensions: [30, 37] output dimension [30, 1], structurally configured as: the long and short term memory layer comprises 300 hidden layers, two full-connection layers, a Dropout layer prediction model and an output layer.
(e) Optimizing the parameters of the constructed LSTM prediction model, and obtaining network characteristic parameters; the optimizing of step (e) comprises: and calculating the input and output characteristic dimension, the number of hidden layers, the network structure and the network training structure parameters of the prediction model.
(f) And predicting the neuron Spike release rate of the pigeon according to the optimized network characteristic parameters.
(g) Evaluating the performance of the prediction model by utilizing correlation analysis; and (g) carrying out correlation analysis on the prediction result and the real neuron Spike issuing sequence so as to evaluate the performance of the prediction model.
According to the analysis result, the accuracy of the prediction result of the constructed LSTM neuron signal Spike release rate prediction model reaches 66.96%, as shown in Table 1.
TABLE 1 prediction model results
The above-mentioned embodiments only express the specific embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for those skilled in the art, without departing from the technical idea of the present application, several changes and modifications can be made, which are all within the protection scope of the present application.
Claims (6)
1. A method for predicting stimulation modes of different colors based on pigeon brain electrical signals is characterized by comprising the following steps:
(a) designing a stimulation mode for applying color stimulation to the pigeons, and recording the brain electrical signals of the pigeons;
(b) carrying out PCA dimension reduction on the recorded pigeon electroencephalogram signals;
(c) extracting Spike release rates of external striatum neurons of the pigeons to different color stimulation modes, and selecting a proper neuron channel;
(d) constructing an LSTM prediction model according to the extracted neuron Spike rate characteristics of the pigeons;
(e) optimizing the parameters of the constructed LSTM prediction model, and obtaining network characteristic parameters;
(f) predicting the neuron Spike release rate of the pigeon according to the optimized network characteristic parameters;
(g) and evaluating the performance of the prediction model by utilizing correlation analysis.
2. The method according to claim 1, wherein the stimulation pattern in step (a) is selected from the group consisting of: the color center point is stimulated under the gray background and the gray center point is stimulated under the color background, and the colors are respectively red, green and blue.
3. The method according to claim 1, wherein the PCA dimension reduction in step (b) is to perform principal component analysis on the recorded pigeon brain electrical signals to reduce the dimension of the neuron Spike release sequence.
4. The method according to claim 1, wherein the pigeon in step (c) has an extrabrain striatal region with two depths, the electrode array is implanted at 4500um and 5000um depths, 64 neuron signals are recorded, and the neuron signals of channel 4 and channel 23 in the two depths are selected.
5. The method according to claim 1, wherein the optimization of step (e) comprises: and calculating the input and output characteristic dimension, the number of hidden layers, the network structure and the network training structure parameters of the prediction model.
6. The method according to claim 1, wherein the correlation between the prediction result and the real neuron Spike release sequence in step (g) is analyzed, so as to evaluate the performance of the prediction model.
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