Hideaki Yamamoto

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 study uses microfluidic platforms to investigate how damage to hub or peripheral connections in modular neuronal networks affects recovery, finding that hub damage leads to delayed recovery through alternative pathway formation.

This study uses human striatal-midbrain assembloids to show that α-synuclein pathology spreads from the striatum to midbrain dopaminergic neurons, causing functional impairment.

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.