Search Results for author: Yusuke Sakemi

Found 6 papers, 0 papers with code

Sparse-firing regularization methods for spiking neural networks with time-to-first spike coding

no code implementations24 Jul 2023 Yusuke Sakemi, Kakei Yamamoto, Takeo Hosomi, Kazuyuki Aihara

The training of multilayer spiking neural networks (SNNs) using the error backpropagation algorithm has made significant progress in recent years.

Learning Reservoir Dynamics with Temporal Self-Modulation

no code implementations23 Jan 2023 Yusuke Sakemi, Sou Nobukawa, Toshitaka Matsuki, Takashi Morie, Kazuyuki Aihara

In this paper, to improve the learning ability of RC, we propose self-modulated RC (SM-RC), which extends RC by adding a self-modulation mechanism.

Time Series Time Series Analysis

Timing-Based Backpropagation in Spiking Neural Networks Without Single-Spike Restrictions

no code implementations29 Nov 2022 Kakei Yamamoto, Yusuke Sakemi, Kazuyuki Aihara

That was not seen in conventional SNNs with single-spike restrictions on time-to-fast-spike (TTFS) coding.

Effects of VLSI Circuit Constraints on Temporal-Coding Multilayer Spiking Neural Networks

no code implementations18 Jun 2021 Yusuke Sakemi, Takashi Morie, Takeo Hosomi, Kazuyuki Aihara

As SNNs are continuous-state and continuous-time models, it is favorable to implement them with analog VLSI circuits.

Quantization

Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time

no code implementations11 Jun 2020 Yusuke Sakemi, Kai Morino, Timothée Leleu, Kazuyuki Aihara

Reservoir computing (RC) is a machine learning algorithm that can learn complex time series from data very rapidly based on the use of high-dimensional dynamical systems, such as random networks of neurons, called "reservoirs."

Edge-computing Time Series +1

A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design

no code implementations8 Jan 2020 Yusuke Sakemi, Kai Morino, Takashi Morie, Kazuyuki Aihara

We also propose several techniques to improve the performance on a recognition task, and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST dataset.

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