Yusuke Sakemi

Orcid: 0000-0002-0274-9491

According to our database1, Yusuke Sakemi authored at least 12 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
Chaos-based reinforcement learning with TD3.
CoRR, 2024

Can Timing-Based Backpropagation Overcome Single-Spike Restrictions in Spiking Neural Networks?
Proceedings of the International Joint Conference on Neural Networks, 2024

Revealing Functions of Extra-Large Excitatory Postsynaptic Potentials: Insights from Dynamical Characteristics of Reservoir Computing with Spiking Neural Networks.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2024, 2024

2023
A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design.
IEEE Trans. Neural Networks Learn. Syst., 2023

Sparse-firing regularization methods for spiking neural networks with time-to-first spike coding.
CoRR, 2023

Learning Reservoir Dynamics with Temporal Self-Modulation.
CoRR, 2023

Optimal Excitatory and Inhibitory Balance for High Learning Performance in Spiking Neural Networks with Long-Tailed Synaptic Weight Distributions.
Proceedings of the International Joint Conference on Neural Networks, 2023

2022
Robustness of Spiking Neural Networks Based on Time-to-First-Spike Encoding Against Adversarial Attacks.
IEEE Trans. Circuits Syst. II Express Briefs, 2022

Timing-Based Backpropagation in Spiking Neural Networks Without Single-Spike Restrictions.
CoRR, 2022

A Spiking Neural Network with Resistively Coupled Synapses Using Time-to-First-Spike Coding Towards Efficient Charge-Domain Computing.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2022

2021
Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks.
CoRR, 2021

2020
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time.
CoRR, 2020


  Loading...