# US 2019/0378622 A1 · A method for prediction of cortical spiking trains

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- Number: US 2019/0378622 A1
- Document ID: US20190378622A1
- Status: Published application
- Kind: A1
- Application number: 16/463,686
- Filed: 2016-11-28
- Published: 2019-12-12
- Earliest claimed: 2016-11-24
- Assignees: Zhejiang University
- Applicants: Zhejiang University
- Inventors: Gang PAN; Dong XING; Cunle QIAN; Yiwen WANG; Qiaosheng ZHANG; Yaoyao HAO; Yueming WANG; Xiaoxiang ZHENG; Zhaohui WU
- Web page: https://patentgrep.com/patent/US20190378622A1

## CPC

- G16H 50/50: for simulation or modelling of medical disorders
- A61B 5/4064: {Evaluating the brain (for intracranial pressure A61B5/031; for cerebral blood gases A61B5/14553; using EEG A61B5/369)}
- A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor}
- A61B 5/7221: {Determining signal validity, reliability or quality (preventing, reducing or removing noise induced by motion artefacts A61B5/7207; noise originating from a therapeutic or surgical apparatus A61B5/7217)}
- G06F 2111/10: Numerical modelling
- G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

The present invention is related to a method for prediction of cortical spiking neural signal, comprising the following steps: 1) pretreatment of spiking neural signal, 2) modeling of posterior cortical spiking neural signal generation probability, 3) model accuracy measurement, 4) model optimization with the help of numerical gradient descent and 5) prediction of posterior cortical spiking neural signal with iterative calculation. The prediction method aims to incorporate natural features of point process of spiking neural signal into the target for optimization of prediction model to improve model's capability in predicting neural spike trains.

## Claims

### Claim 1

A method for prediction of cortical spiking neural signal, characterized in that the method comprises the following steps:
1) pretreatment of cortical spiking neural signal channel: First, using time slot to divide original cortical spiking neural signal to complete discretization; screening out signal channels with extremely high or low spiking release rate among all cortical spiking neural signal channels;
2) modeling of posterior cortical spiking neural signal generation probability: using mutual information to obtain numerous anterior cortical signal channels with the maximum value of mutual information with each signal channel in posterior cortical region as the independent variable of the model; after that, setting sliding window in view of each time slot in all posterior cortical signal channels to be predicted to select input independent variable of the model; once the input independent variable of the model is obtained, proceeding with modeling of posterior cortical spiking neural signal generation probability with help of inhomogeneous Poisson point process generalized linear model;
3) model accuracy measurement: using the model in Step 2) to obtain the sequence for spiking release probability of spiking neural signal to be predicted in the posterior cortical region; using Discrete Time Rescaling Kolmogorov-Smirnov Statistics as the dimension for measurement of model effect to calculate model effect value;
4) model optimization with the help of numerical gradient descent: taking the model effect measurement value obtained in Step 3) as the target function for derivation to obtain the numerical gradient; proceeding with optimized calculation of optimal parameters with Quasi-Newton Method;
5) iterative calculation: executing Step 3) and 4) iteratively when calculating the optimal parameters until accuracy of the model corresponding to the optimized parameters is up to the local optima or upper limit of iterations; introducing final parameters into the model, and using this model to complete prediction of posterior cortical spiking neural signal.

### Claim 2 (depends on 1)

The method for prediction of cortical spiking neural signal according to claim 1, characterized in that discretization in the Step 1) is stated as follows: taking 10 milliseconds as the time slot width to divide original cortical spiking neural signal; recording the time slot with existence of spiking neural signal in the time slot as 1 or 0 on the contrary to complete discretization.

### Claim 3 (depends on 1)

The method for prediction of cortical spiking neural signal according to claim 1, characterized in that the screening method in the Step 1) is stated as follows: First, dividing each spiking neural signal channel into 50% training set, 20% validation set and 30% test set respectively as per time axis; checking if spiking release rate in three sets of the signal channel is between 2 Hz and 40 Hz; it will be deemed to be qualified through screening if this condition can be satisfied.

### Claim 4 (depends on 1)

The method for prediction of cortical spiking neural signal according to claim 1, characterized in that the method for obtainment independent variable of the model in the Step 2) is stated as follows: once mutual information value for all pairs of spiking neural signals of anterior and posterior cortical regions is calculated, selecting the first 8 anterior cortical signal channels with the highest mutual information value with each spiking neural signal in posterior cortical region as the independent variable of model for the posterior cortical signal channel; mutual information value is stated as follows:
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wherein, xi is the signal channel i of anterior cortical region; yj is the signal channel j of posterior cortical region; p(xt, yt) is the joint probability for simultaneous occurrence of event xt and yt; p(xt) or p(yt) refers to occurrence probability of event xt or yt respectively; as original cortical spiking neural signal has been discretized, value range of xt and yt is {0, 1}.

### Claim 5 (depends on 4)

The method for prediction of cortical spiking neural signal according to claim 4, characterized in that the method for selection of input independent variable for the model is stated as follows: in view of each time slot in the posterior cortical signal channel to be predicted, setting a sliding window with time span of 90 milliseconds to intercept the first 8 anterior cortical spiking signal channels with the highest mutual information value with it as the input independent variable for the model; the end of this sliding window is in flush with current time slot.

### Claim 6 (depends on 5)

The method for prediction of cortical spiking neural signal according to claim 5, characterized in that in the Step 2), inhomogeneous Poisson point process generalized linear model is used for modeling of generation probability of posterior cortical spiking neural signal; the specific form is stated as follows:
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wherein, n(t) refers to the number of spikes released in time slot t; in view of discretization to original cortical spiking neural signal, value range of n(t) is {0,1}; P(n(t)) refers to the probability for release of n(t) spikes in time slot t; λ(t|x(t)) refers to density function for conditional probability for release of spikes in time slot t; x(t) refers to anterior cortical spiking neural signal collected by the sliding window; Δ refers to the length of time slot;
then, linking up λ(t) and independent variable x(t), and using exponential function as the link function of generalized linear model; specific form is stated as follows:
λ(t)Δ=ex(t)Tθ (3)
wherein λ(t)=λ(t|x(t)).

### Claim 7 (depends on 6)

The method for prediction of cortical spiking neural signal according to claim 6, characterized in that in Step 3), Discrete Time Rescaling Kolmogorov-Smirnov Statistics are taken as the dimension for measurement of model effect to calculate model effect value; its specific form is stated as follows:
I. firstly, generating a new sequence q(t) on the basis of λ(t) to eliminate impact from sampling of discretization time; its specific form is stated as follows:
q(t)=−log(1−λ(t)Δ) (4)
II. then, cutting the sequence q(t) in reference to the spike train produced by the neural cell to be predicted; once completed, integrating value q(t) between two adjacent spiking signals, and correcting discrepancy incurred by discretization time treatment; the specific form is stated as follows:
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wherein, ti refers to the occurrence time of spiking signal i of the neural cell to be predicted; ti+1 refers to occurrence time of spiking signal i+1 of the neural cell to be predicted; δ(i) is a random variable collected in the following form:
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r(i) in Formula (6) is a random variable uniformly distributed within [0,1];
III. then, proceeding with scale transformation of ζ(i) to obtain the following formula:
z(i)=1−e−ζ(i) (7)
according to Kolmogorov-Smirnov Test Theory, the set {z(i)} produced by Formula (7) is to be in uniform distribution within [0,1];
IV. rearranging the set {z(i)} in a sequence from small value to big one, and plotting it to the coordinate system for comparison while ensuring uniform distribution within standard [0,1]; transversing axis refers to set {z(i)}; whereas vertical axis refers to standard uniform distribution;
taking the maximum value difference between the two on the vertical axis as the effect measurement value f(y|θ).

### Claim 8 (depends on 7)

The method for prediction of cortical spiking neural signal according to claim 7, characterized in that in Step 3), calculation process for each neural cell signal channel to be predicted is executed for 10 times for taking the mean value when calculating effect measurement value.

### Claim 9 (depends on 7)

The method for prediction of cortical spiking neural signal according to claim 7, characterized in that in Step 4) model optimization refers to the following fact: directly calculating numerical gradient of target function f(y|θ) through approximate derivation; the specific form for derivation of numerical gradient is stated as follows:
∂
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wherein, y refers to spiking neural signal channel to be predicted in posterior cortical region; θ refers to the vector of model parameter to be defined; f(y|θ) refers to the target function; θa and θb are represented as θia=θi+ϵ and θib=θi−ϵ on the dimension i; value of other dimensions is consistent with θ; here, value of ϵ is 1e-5.