Yusei Nishi

The paper investigates how input encoding and spike detection methods affect neural representations and reservoir computing performance in biological neural networks for multiclass time-series classification.

This study evaluates how neural architecture, network dynamics, and decoding methods affect handwritten digit classification using human iPSC-derived neuronal networks.

This paper develops a closed-loop biological neural network system that learns and generates temporal patterns through feedback control, demonstrating adaptability and reduced synchronization.