28 citations · 46 across the 12 of their papers we have counts for
5 papers · 1 filter
Backpropagation through Soft Body: Investigating Information Processing in Brain-Body Coupling Systems
Hiroki Tomioka, Katsuma Inoue, Yasuo Kuniyoshi +1
Animals achieve sophisticated behavioral control through dynamic coupling of the brain, body, and environment. Accordingly, the co-design approach, in which both the controllers an…
Training Spiking Neural Networks via Augmented Direct Feedback Alignment
Yongbo Zhang, Katsuma Inoue, Mitsumasa Nakajima +3
Spiking neural networks (SNNs), the models inspired by the mechanisms of real neurons in the brain, transmit and represent information by employing discrete action potentials or sp…
How noise affects memory in linear recurrent networks
JingChuan Guan, Tomoyuki Kubota, Yasuo Kuniyoshi +1
The effects of noise on memory in a linear recurrent network are theoretically investigated. Memory is characterized by its ability to store previous inputs in its instantaneous st…
Exploiting Chaotic Dynamics as Deep Neural Networks
Shuhong Liu, Nozomi Akashi, Qingyao Huang +2
Chaos presents complex dynamics arising from nonlinearity and a sensitivity to initial states. These characteristics suggest a depth of expressivity that underscores their potentia…
Physical Deep Learning with Biologically Plausible Training Method
Mitsumasa Nakajima, Katsuma Inoue, Kenji Tanaka +3
The ever-growing demand for further advances in artificial intelligence motivated research on unconventional computation based on analog physical devices. While such computation de…