This invention involves use of temporal or spatio/spector-temporal data (SSTD) for early classification of outputs that are results of spatio-temporal patterns of data. Classification models are based on spiking neural networks (SNN) suitable to learn and classify SSTD. The invention may predict early events in many applications, i.e. engineering, bioinformatics, neuroinformatics, predicting response to treatment of neurological and brain disease, ecology, environment, medicine, and economics, among others. The invention involves a method and system for personalized modelling of SSTD and early prediction of events based on evolving spiking neural network reservoir architecture (eSNNr). The system includes a spike-time encoding module to encode continuous value input information into spike trains, a recurrent 3D SNNr and an eSSN as an output classification module.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method of creating an evolving spiking neural network personalized model Mx for use in determining a prognostic outcome specific to a subject x represented as input data SSTDx, wherein the input data of x comprises a number of static and dynamic variables in relation to a scenario of interest and wherein the model Mx comprises a spiking neural network reservoir and a spiking neural network classifier, the method comprising: extracting from a global dataset D of samples also having the same static and dynamic variable features relating to the scenario as the data of x, and for which an outcome is known, a subset Kx having static variable features that most closely accord to the static variable features of the subject x, and wherein the method further comprises: encoding the data into spike sequences, wherein the spike sequences represent individual input patterns; constructing the spiking neural network reservoir; mapping the input variables into reservoir input neurons located according to the spatial location of the input variables, or according to the temporal similarity of the input variables; training the spiking neural network reservoir to learn the spike sequences; constructing the spiking neural network classifier; and training the spiking neural network classifier to recognise and classify similar input spike sequences related to a future event at an earlier time point of prediction. 2. The method of claim 1, wherein the spiking neural network reservoir is a three dimensional spiking neural network reservoir. 3. The method of claim 1, wherein the step of mapping the input variables into the reservoir comprises mapping the temporal input variables into spatially located input neurons in the reservoir. 4. The method of claim 1, wherein the step of mapping input variables into the reservoir comprises mapping spatio-temporal brain data, including EEG, fMRI and other brain data, using a standard brain structural template. 5. The method of claim 1, wherein the spiking neural network classifier is also trained, when recalled on new data, to classify spike sequences before the entire spike sequence has propagated through the spiking neural network reservoir. 6. A computer-implemented method of optimizing a model Mx suitable for use in data analysis and determining a prognostic outcome at a future time point specific to a subject x, represented as input data SSTDx, wherein the SSTDx comprises a number of static and dynamic (temporal) variable features Vx in relation to a scenario of interest for which there is a global dataset D of samples also having the same features relating to the scenario as the subject x and the data SSTDx, and for which an outcome is known, the method comprising: selecting suitable samples Kx from within the global dataset D having static variable features that most closely accord to the static variable features of the subject x and the related data SSTDx; creating an evolving spiking neural network personalized model Mx for predicting an outcome to x, the model Mx being created based on the dynamic variable features of the samples Kx; applying an optimisation procedure to increase the accuracy of the prediction while reducing the time point of future event prediction; and generating a prediction of the prognostic outcome of subject x using model Mx. 7. The method of claim 6, wherein the method also comprises: determining what number and a subset Vx of variable features will be used in assessing the outcome for the subject x; ranking the Vx variable features within the neighbourhood Dx in order of importance to the outcome of x and obtaining a weight vector Wx for all variable features Vx; 8. The method of claim 7, wherein the step of applying an optimisation procedure comprises: testing the accuracy of the model Mx for each sample from Dx; storing both the accuracy of the model Mx for each sample from Dx and the corresponding model parameters; repeating the steps of determining Kx and/or Vx while performing an optimisation procedure to optimise Kx and/or Vx, to determine their optimal values, before repeating the steps of creating the model Mx, testing and storing the accuracy and model parameters of the model Mx for each sample from Dx. 9. The method of claim 1, wherein the subject is an individual. 10. A computer-implemented method of generating a prediction of stroke occurrence for an individual using the method of claim 1. 11. A computer-implemented method of classifying unclassified SSTD brain data (STBD) using the method of claim 1. 12. The method of claim 11, wherein the method comprises: transforming the STBD into spike sequences; mapping the brain data input variables into the 3D spiking neural network reservoir; creating a brain data classification model, the model comprising a spiking neural network reservoir and a classifier, the spiking neural network reservoir configured to spatially map the brain data and to provide spiking activity patterns to the classifier; training the brain data classification model using archive brain data, the archive brain data comprising STBD with known classifications, wherein training the brain data classification model comprises the steps of: providing the archive brain data as input to the spiking neural network reservoir to train the spiking neural network reservoir to provide similar spiking activity patterns for similar input spike sequences to the classifier; and providing the archive brain data with corresponding known classifications as input to the spiking neural network reservoir, the known classifications used to train the classifier to recognise and classify similar spiking activity patterns; and using the brain data classification model to classify the unclassified brain data. 13. A computer-implemented method of generating a prediction of an ecological event based on spatio-temporal data using the method of claim 1. 14. A computer-implemented method of controlling a state of an apparatus using brain signals using the method of claim 1. 15. The method of claim 14, wherein the method comprises: receiving brain data corresponding to a desired state of the apparatus; mapping the data into the spiking neural network reservoir; learning of the data into the spiking neural network reservoir; classifying the brain data using the trained model and a spiking neural network classifier; determining the desired state of the apparatus based on the classified brain data; sending data indicative of the desired state of the apparatus to a controller, wherein the controller is configured to control the apparatus in accordance with the desired state of the apparatus. 16. The method of claim 14 wherein the method of controlling a state of an apparatus is applied to a method of rehabilitation, further comprising the steps of: acquiring brain data from the patient, the patient being equipped with one or more rehabilitation actuator(s) adapted to induce movement of a body part, the brain data corresponding to a desired movement of the body part; mapping the data into the spiking neural network reservoir; learning of the data into the spiking neural network reservoir; classifying the brain data; controlling the rehabilitation actuator(s) using brain activity to produce the desired movement of the body part; and if necessary, repeating the method until the patient improves his/her brain signals and body activity to produce the desired movement without the rehabilitation actuator. 17. The use of an evolving spiking neural network personalized model Mx in a computer-implemented method of determining a prognostic outcome specific to a subject x represented as input data SSTDx, wherein the input data of x comprises a number of static and dynamic variable features in relation to a scenario of interest, wherein the evolving spiking neural network personalised model Mx is created by extracting from a global dataset D of samples also having the same static and dynamic variable features relating to the scenario as the data of x, and for which an outcome is known, a subset Kx having static variable features that most closely accord to the static variable features of the subject x. 18. A computer-readable medium having stored thereon instructions which, when executed on a computer, cause the computer to perform the method of claim 1. 19. A computer system suitably programmed to execute the method steps of claim 1. 20. A method for the analysis of a 3D spiking neural network reservoir after training with temporal or spatio-temporal data that utilises the method of claim 1.