Applications of Spiking Neural Network to Predict Software Reliability

Mohanty, Ramakanta; Priyadarshini, Aishwarya; Desai, Vishnu Sai; Sirisha, G. N. V. G.

doi:10.1007/978-981-10-7566-7_16

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…SNNs, as computational models, encompass spiking neurons as processing components, interconnected by biologically feasible learning algorithms [32]- [34]. SNNs inspired by the brain have found utility across diverse domains, including but not limited to forecasting [35], simulation of the impact of mindfulness on individuals with depression [36], real-world data classification, image recognition, odor recognition, motor control, trajectory tracking, and more. In 2014, Kasabov introduced an SNN architecture called Neucube [37], designed to facilitate effective learning, modeling, and classification of spatiotemporal brain data (STBD).…”

Section: Introductionmentioning

confidence: 99%

Depression Identification Using EEG Signals via a Hybrid of LSTM and Spiking Neural Networks

Sam,

Boostani,

Hashempour

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Depression severity can be classified into distinct phases based on the Beck depression inventory (BDI) test scores, a subjective questionnaire. However, quantitative assessment of depression may be attained through the examination and categorization of electroencephalography (EEG) signals. Spiking neural networks (SNNs), as the third generation of neural networks, incorporate biologically realistic algorithms, making them ideal for mimicking internal brain activities while processing EEG signals. This study introduces a novel framework that for the first time, combines an SNN architecture and a long short-term memory (LSTM) structure to model the brain's underlying structures during different stages of depression and effectively classify individual depression levels using raw EEG signals. By employing a brain-inspired SNN model, our research provides fresh perspectives and advances knowledge of the neurological mechanisms underlying different levels of depression. The methodology employed in this study includes the utilization of the synaptic time dependent plasticity (STDP) learning rule within a 3-dimensional braintemplate structured SNN model. Furthermore, it encompasses the tasks of classifying and predicting individual outcomes, visually representing the structural alterations in the brain linked to the anticipated outcomes, and offering interpretations of the findings. Notably, our method achieves exceptional accuracy in classification, with average rates of 98% and 96% for eyes-closed and eyes-open states, respectively. These results significantly outperform state-of-the-art deep learning methods.

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