In silico modeling of reservoir-based predictive coding in biological neuronal networks on microelectrode arrays
Yuya Sato, Hideaki Yamamoto, Yoshitaka Ishikawa, Takuma Sumi, Yuki Sono, Shigeo Sato, Yuichi Katori, Ayumi Hirano‐Iwata · Japanese Journal of Applied Physics · 2024
Abstract Reservoir computing and predictive coding together yield a computational model for exploring how neuronal dynamics in the mammalian cortex underpin temporal signal processing. Here, we construct an in-silico model of biological neuronal networks grown on microelectrode arrays and explore their computing capabilities through a sine wave prediction task in a reservoir-based predictive coding framework. Our results show that the time interval between stimulation pulses is a critical determinant of task performance. Additionally, under a fixed feedback latency, pulse amplitude modulation is a favorable encoding scheme for input signals. These findings provide practical guidelines for future implementation of the model in biological experiments.