One of the main alternatives for the weakly interacting massive particles (WIMP) dark matter scenario are the super-heavy X particles with masses larger than the weak scale by orders of magnitude. We assume the experimentally more plausible scenario of decaying superheavy dark matter (SHDM), which leads to the production secondary particles: electrons, positrons, gamma rays and neutrinos. The hypothetical X-particle has two main parameters: it’s mass MX and lifetime ?, which can be indirectly constrained by comparisons of predicted flux of secondary particles with the astrophysical observations. We present the limits on the SHDM parameters derived with the multi-messenger data from the Fermi-LAT, IceCube and other experiments.

This workshop is supported by RIKEN iTHEMS (RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program).

Organizers
Emiko Hiyama (Kyushu U./RIKEN)
Hiroshi Suzuki (Kyushu U.)
Tetsuo Hatsuda (RIKEN)

He will talk about theoretical study on complex network data, including clinical data.

His talk consists of two parts:
14:00-15:00: Detecting the tipping points of dynamic processes by dynamic network biomarkers
15:00-15:30: Coffee break
15:30-16:30: Predicting future dynamics of short-term time-series data by randomly distributed embedding

First part of his talk is for general audience.
In second part of his talk, he will explain mathematical basis of his study in detail.

Animals adapt to the environment for survival. Synaptic plasticity is considered a major mechanism underlying this process. However, the best-known form of synaptic plasticity, i.e., Hebbian plasticity that depends on pre- and post-synaptic activity, can surge coincident activity in model neurons beyond a physiological range. Our lab has explored how neural circuits learn about the environment by synaptic plasticity. The instability of Hebbian plasticity could be mitigated by a global factor that modulates its outcome. For example, TNF-alpha that mediates homeostatic synaptic scaling is released by glia, reflecting the activity level of surrounding neurons. I show that a specific interaction of Hebbian plasticity with this global factor accounts for the time course of adaptation to the altered environment (Toyoizumi et al. 2015). At a more theoretical level, I ask what is the optimal synaptic plasticity rule for achieving an efficient representation of the environment. A solution is the error-gated Hebbian rule, whose update is proportional to the product of Hebbian change and a specific global factor. I show that this rule, suitable also in neuromorphic devices, robustly extracts hidden independent sources in the environment (Isomura and Toyoizumi 2016, 2018, 2019). Finally, I introduce that synapses change by intrinsic spine dynamics, even in the absence of synaptic plasticity. I show that physiological spine-volume distribution and stable cell assemblies are both achieved when intrinsic spine dynamics are augmented in a model (Humble et al.2019).