Yuichi Katori

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

This paper introduces Predictive E-prop, a biologically inspired model that trains recurrent spiking neural networks using gradient-based prediction error minimization, achieving self-supervised learning of complex time-series signals.

This paper models how modular structure and spike-timing-dependent plasticity influence recovery in damaged neuronal networks.

This study examines how e-prop learning performs in recurrent spiking neural networks under varying connectivity and noise levels, finding that sparsity improves learning by enhancing activation diversity.