Synchronization phenomena on complex networks,
from math to experiments- Special workshop for AIMR Advanced Target Projects Date：Monday, July 29, 2019, 13:30–18:20
Venue：TOKYO ELECTRON House of Creativity, 3F, Lecture Theater,
Katahira Campus, Tohoku University.
Access: http://www.tfc.tohoku.ac.jp/about us/contact and access.html
13:30〜14:10
Hideaki Yamamoto (AIMR, Tohoku University)
Engineering network synchrony in cultured cortical neurons using microfabricated scaﬀolds
In vitro experiments using dissociated neurons play irreplaceable roles in molecular and cellular
neuroscience. However, its application in systems-level studies has been limited due to the substantial
diﬀerence in the network organization from the actual brain, which results in the generation of atypicaldynamics consisting of highly synchronous burst firing. In this talk, I will discuss about how surface
engineering technology can be employed to prepare guidance cues for the network development of
cultured cortical neurons and how modulation of network structure at the mesoscopic scale influences
the network synchrony.
14:20〜15:00
Haru Negami (Graduate school of Engineering, The University of Tokyo)
Fatgraph models of protein-ligand complexes and their application to circadian rhythm
Living things evolve and adapt/ maladapt to the environment by gene mutations which cause
conformational alternation of proteins. Thus, it is important to elucidate the relationships between
the three dimensional structures of proteins and their physiological activities. Here, we focus on
the fatgraph model of proteins which is a topological method to understand the three dimensionalstructure. In this talk, we will show our improved fatgraph model for enzyme-substrate complex
and will discuss the eﬀect of mutations on oscillating behavior of circadian clock protein from theperspective of the model.
15:10〜15:50
Theoden I. Netoﬀ (Department of Biomedical Engineering, University of Minnesota)
Desynchronization of pathological oscillations in Parkinson’s disease with phasic stimulation
Deep brain stimulation is used to treat advanced stages of Parkinson’s disease. It is thought thatstimulation may desynchronize pathological neuronal oscillations to treat the disease. Topology ofthe neuronal networks within the basal ganglia and the dynamics of the neurons may result in this1

synchrony. Electrical stimulation can be used to desynchronize the neuronal population. In this talk,I will discuss diﬀerent methods by which we can desynchronize populations with minimal stimulationenergy.
16:00〜16:40
Jun-nosuke Teramae (Graduate School of Informatics, Kyoto University)
Biologically plausible learning based on dual stochasticity of the brain
Neurons and synapses of cortical circuit in vivo continuously show highly stochastic activities withconsuming large amount of metabolic energy of the brain even while behavioral responses of animals
are highly reproducible and reliable. These stochasticities have been studied independently so far andfunctional roles of them remain unclear. In this talk, I show that integration of these stochasticities
provide us an eﬃcient novel learning algorithm of the neural network that is fully consistent with biological observations of cortical circuit. The obtained algorithm seems as a natural confluence of two
representative streams of machine learning, the backpropagation learning and the Bayesian inference,
while it overcomes various known limitations of the existing algorithms. Unlike the backpropagation,for instance, the method is applicable to networks with arbitral topologies including recurrent networks, requires one type of computations that can be performed asynchronously and locally, and isfree from overfitting. We confirm validity of the algorithm by applying the method to various tasks
including supervised and unsupervised learning. An experimentally testable prediction of the modeland relationship between the algorithm and the reinforcement learning will also be discussed.
16:50〜17:30
Kiyoshi Kotani (Research Center for Advanced Science and Technology, The University of Tokyo)
Model-based analytical insights for multiscale neuronal oscillations
Local field potential in the brain is organized by interacting neurons and have functional roles
in cognition and/or attention. We have developed a framework of multiscale analysis of neuronal
network using modified theta model, which possesses voltage-dependent dynamics in an analyticallysuitable form. Origin of macroscopic oscillation for this model is investigated by introducing their
Fokker-Planck equation as well as Ott-Antonsen ansatz. In addition, adjoint equations of macroscopic
equations are useful to investigate properties of synchronization in macroscopic scale.
17:40〜18:20
Hayato Chiba (AIMR, Tohoku University)
Dynamics of neuronal oscillations on a random graph
We consider the dynamics of a large population of neurons defined on a random network. Synchronization of firing of neurons is known as the gamma oscillation. In this talk, we show that thegamma oscillation appears/disappears depending on the complexity of the network with the aid of
the generalized spectral theory.
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