Abstract
The paper proposes a new method for deep learning and knowledge discovery in a brain-inspired Spiking Neural Networks (SNN) architecture that enhances the model’s explainability while learning from streaming spatiotemporal brain data (STBD) in an incremental and on-line mode of operation. This led to the extraction of spatiotemporal rules from SNN models that explain why a certain decision (output prediction) was made by the model. During the learning process, the SNN created dynamic neural clusters, captured as polygons, which evolved in time and continuously changed their size and shape. The dynamic patterns of the clusters were quantitatively analyzed to identify the important STBD features that correspond to the most activated brain regions. We studied the trend of dynamically created clusters and their spike-driven events that occur together in specific space and time. The research contributes to: (1) enhanced interpretability of SNN learning behavior through dynamic neural clustering; (2) feature selection and enhanced accuracy of classification; (3) spatiotemporal rules to support model explainability; and (4) a better understanding of the dynamics in STBD in terms of feature interaction. The clustering method was applied to a case study of Electroencephalogram (EEG) data, recorded from a healthy control group (n = 21) and opiate use (n = 18) subjects while they were performing a cognitive task. The SNN models of EEG demonstrated different trends of dynamic clusters across the groups. This suggested to select a group of marker EEG features and resulted in an improved accuracy of EEG classification to 92%, when compared with all-feature classification. During learning of EEG data, the areas of neurons in the SNN model that form adjacent clusters (corresponding to neighboring EEG channels) were detected as fuzzy boundaries that explain overlapping activity of brain regions for each group of subjects.
Highlights
Spiking Neural Networks (SNN) are the third generation of artificial neural networks (ANN) and compared to perceptron-type neuron, they encompass the time component while accumulating the neuron’s inputs and generating temporal outputs
Compared to other artificial neural networks in machine learning (ML) systems. They have been successfully applied to various domains for classification and prediction of outcomes in temporal or spatiotemporal datasets such as classification of cognitive states using Electroencephalogram (EEG) [6,7,8,9], event-related potential (ERP) [10,11,12], and functional Magnetic Resonance Imaging (MRI) [13,14,15,16]
The paper proposes a methodology for deep learning of dynamic spatiotemporal pattern and knowledge discovery and improved explainability of spiking neural networks by modelling the dynamic patterns created during unsupervised learning with streaming spatiotemporal EEG data
Summary
Compared to other artificial neural networks in machine learning (ML) systems They have been successfully applied to various domains for classification and prediction (prognosis and diagnosis) of outcomes in temporal or spatiotemporal datasets such as classification of cognitive states using Electroencephalogram (EEG) [6,7,8,9], event-related potential (ERP) [10,11,12], and functional Magnetic Resonance Imaging (MRI) [13,14,15,16]. In clinical applications of ML, along with the accuracy of classification/prediction of health states, the ML explainability is of crucial importance This refers to the degree to which an end-user (clinical practitioner) comprehends the reason of a certain decision (classifier outcome). The proposed brain-inspired SNN (BISNN) architecture NeuCube [18] allowed to “open the black box” and even to extract spatiotemporal rules [19,20]
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