Real-time Neural Connectivity Inference with Presynaptic Spike-driven Spike Timing-Dependent Plasticity

• Published in: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) Vol. 2023; pp. 1 - 4
• Main Authors: Kim, Daeyoung, Choi, Jihyeok, Cheon, Mingyu, Jeong, YeonJoo, Kim, Jaewook, Kwak, Joon Young, Park, Jong-Keuk, Lee, Suyoun, Kim, Inho, Park, Jongkil
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2023
• Subjects: Action Potentials - physiology; Artificial Intelligence; Correlation; Correlation coefficient; Memristors; Neural Networks, Computer; Neuromorphics; Neuronal Plasticity - physiology; Neurons; Neurons - physiology; Real-time systems; Sensitivity and specificity
• ISSN: 2694-0604
• DOI: 10.1109/EMBC40787.2023.10341017

Brain-like artificial intelligence in electronics can be built efficiently by understanding the connectivity of neuronal circuitry. The concept of neural connectivity inference with a two-dimensional cross-bar structure memristor array is indicated in recent studies; however, large-scale implementation is challenging owing to device variations and the requirement of online parameter adaptation. This study proposes a neural connectivity inference method with one-dimensional spiking neurons using spike timing-dependent plasticity and presynaptic spike-driven spike timing-dependent plasticity learning rules, designed for a large-scale neuromorphic system. The proposed learning process decreases the number of spiking neurons by half. We simulate 12 ground-truth neural networks comprising one-dimensional eight and 64 neurons. We analyze the correlation between the neural connectivity of the ground truth and spiking neural networks using the Matthews correlation coefficient. In addition, we analyze the sensitivity and specificity of inference. Validation using the presynaptic spike-driven spike timing-dependent plasticity learning rule implies a potential approach for large-scale neural network inference with real hardware realization of large-scale neuromorphic systems.