Zobrazeno 1 - 10
of 200
pro vyhledávání: '"Takashi Morie"'
Publikováno v:
Communications Physics, Vol 7, Iss 1, Pp 1-11 (2024)
Abstract Reservoir computing (RC) can efficiently process time-series data by mapping the input signal into a high-dimensional space via randomly connected recurrent neural networks (RNNs), which are referred to as a reservoir. The high-dimensional r
Externí odkaz:
https://doaj.org/article/4654710115f44d0a89e300766818d74c
Publikováno v:
IEEE Access, Vol 10, Pp 43003-43012 (2022)
To solve a navigation task based on experiences, we need a mechanism to associate places with objects and recall them along the course of action. In a reward-oriented task, if the route to a reward location is simulated in mind after experiencing it
Externí odkaz:
https://doaj.org/article/07ff1015c4c74f42b2b8dad4d0089e7c
Publikováno v:
IEEE Access, Vol 9, Pp 2644-2654 (2021)
This paper proposes a time-domain analog calculations model based on a pulse-width modulation (PWM) approach for neural network calculations including weighted-sum or multiply-and-accumulate calculation and rectified-linear unit operation. We also pr
Externí odkaz:
https://doaj.org/article/14dbcdd0040748db8d39cd3aa3a87a8d
Publikováno v:
IEEE Access, Vol 8, Pp 212066-212078 (2020)
This study develops an intelligent system for home service robots mimicking human brain function that can manage common knowledge applicable to any environment and local knowledge reflecting its specific environment. Deep learning is effective for ac
Externí odkaz:
https://doaj.org/article/2e91667d3940481ea57507f1b54ef8e3
Publikováno v:
IEEE Access, Vol 8, Pp 204360-204377 (2020)
Boltzmann machines (BMs) are useful in various applications but are limited by their requirement to generate random numbers. In contrast, chaotic Boltzmann machines (CBMs) are neural networks that imitate the stochastic behavior of BMs with the chaot
Externí odkaz:
https://doaj.org/article/9668bc1f251d44dbaff8f7cecf7be4e9
Publikováno v:
PLoS ONE, Vol 13, Iss 3, p e0194049 (2018)
This paper proposes a shared synapse architecture for autoencoders (AEs), and implements an AE with the proposed architecture as a digital circuit on a field-programmable gate array (FPGA). In the proposed architecture, the values of the synapse weig
Externí odkaz:
https://doaj.org/article/7c93e1b872244f819a6630ecf1b2cd90
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 34:394-408
Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms but also energy-efficient computational models
Publikováno v:
IEEE Transactions on Circuits and Systems II: Express Briefs. 69:3640-3644
Publikováno v:
2022 IEEE International Symposium on Circuits and Systems (ISCAS).
Publikováno v:
2022 IEEE International Symposium on Circuits and Systems (ISCAS).