Designing spontaneous behavioral switching via chaotic itinerancy

Chaotic itinerancy is a frequently observed phenomenon in high-dimensional and nonlinear dynamical systems, and it is characterized by the random transitions among multiple quasi-attractors. Several studies have revealed that chaotic itinerancy has been observed in brain activity, and it is considered to play a critical role in the spontaneous, stable behavior generation of animals. Thus, chaotic itinerancy is a topic of great interest, particularly for neurorobotics researchers who wish to understand and implement autonomous behavioral controls for agents. However, it is generally difficult to gain control over high-dimensional nonlinear dynamical systems. Hence, the implementation of chaotic itinerancy has mainly been accomplished heuristically. In this study, we propose a novel way of implementing chaotic itinerancy reproducibly and at will in a generic high-dimensional chaotic system. In particular, we demonstrate that our method enables us to easily design both the trajectories of quasi-attractors and the transition rules among them simply by adjusting the limited number of system parameters and by utilizing the intrinsic high-dimensional chaos. Finally, we quantitatively discuss the validity and scope of application through the results of several numerical experiments.Comment: 15 pages, 6 figures and 1 supplementary figure. Our supplementary videos are available in https://drive.google.com/drive/folders/10iB23OMHQfFIRejZstoXMJRpnpm3-3H5?usp=sharin

Signatures of self-organized criticality in spontaneous walking behavior of Porcellio scaber

The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P6

Inhibition of ATR protein kinase activity by schisandrin B in DNA damage response

ATM and ATR protein kinases play a crucial role in cellular DNA damage responses. The inhibition of ATM and ATR can lead to the abolition of the function of cell cycle checkpoints. In this regard, it is expected that checkpoint inhibitors can serve as sensitizing agents for anti-cancer chemo/radiotherapy. Although several ATM inhibitors have been reported, there are no ATR-specific inhibitors currently available. Here, we report the inhibitory effect of schisandrin B (SchB), an active ingredient of Fructus schisandrae, on ATR activity in DNA damage response. SchB treatment significantly decreased the viability of A549 adenocarcinoma cells after UV exposure. Importantly, SchB treatment inhibited both the phosphorylation levels of ATM and ATR substrates, as well as the activity of the G2/M checkpoint in UV-exposed cells. The protein kinase activity of immunoaffinity-purified ATR was dose-dependently decreased by SchB in vitro (IC50: 7.25 μM), but the inhibitory effect was not observed in ATM, Chk1, PI3K, DNA-PK, and mTOR. The extent of UV-induced phosphorylation of p53 and Chk1 was markedly reduced by SchB in ATM-deficient but not siATR-treated cells. Taken together, our demonstration of the ability of SchB to inhibit ATR protein kinase activity following DNA damage in cells has clinical implications in anti-cancer therapy

Kinesthetic Sensing Exploiting the Active Interaction between the Environment and an Ostrich-Neck-inspired Manipulator

The 11th International Symposium on Adaptive Motion of Animals and Machines. Kobe University, Japan. 2023-06-06/09. Adaptive Motion of Animals and Machines Organizing Committee.Poster Session P1

A Coupled Spintronics Neuromorphic Approach for High-Performance Reservoir Computing

The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information-processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information-processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high-dimensional dynamics of coupled spin-torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information-processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information-processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle-based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses