no code implementations • 24 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.
no code implementations • 23 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.
no code implementations • 29 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.
no code implementations • 18 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.
no code implementations • 11 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."
no code implementations • 8 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.