Time TableTime TableOpening10:00 - 10:15WelcomeMasashi YanagisawaProgram Coordinator, Ph.D. Program in Humanics/Director, WPI-IIIS, University of TsukubaOpening Address 1Akira UkawaWPI Program DirectorOpening Address 2Masaaki TakahashiManaging Director, Daiichi Sankyo Foundation ofLife ScienceSession for WPI-IIIS | Chair: Arisa Hirano (WPI-IIIS, University of Tsukuba)10:15 - 10:50Neural mechanisms for learningfrom a small sample10:50 - 11:25Searching for Neural Correlates ofSakiko HonjohSleepinessMitsuo KawatoATR Brain Information CommunicationResearch Laboratory GroupPage 4WPI-IIIS, University of TsukubaPage 536th Takamine Conference17th Takamine Memorial Daiichi Sankyo Prize Lecture | Chair: Takao Saruta (Keio University)11:25 - 12:10Toward the Mysteries of SleepMasashi YanagisawaWPI-IIIS, University of TsukubaPage 6Center for Information and NeuralNetworks, National Institute ofInformation and CommunicationsTechnologyPage 7National Institute of GeneticsPage 812:10 - 12:30 Lunch PreparationPart 1 | Chair: Masashi Yanagisawa (WPI-IIIS, University of Tsukuba)Ben Seymour12:30 - 13:05An engineering approach to pain13:05 - 13:40Function and connectivity of avisual circuit underlying optic flow Fumi Kuboprocessing in zebrafish13:40 - 14:10 Tea BreakPart 2 | Chair: Masashi Yanagisawa (WPI-IIIS, University of Tsukuba)14:10 - 14:55Sleep: A worm’s eye viewHenrik BringmannPhilipps University of MarburgPage 914:55 - 15:30CryoEM analysis of the GPCRneurotensin receptor 1-G proteincomplexHideaki KatoThe University of TokyoPage 1015:30 - 15:40BreakPoster Session| Chair: Masayuki Matsumoto (University of Tsukuba)15:40 - 16:50Data Blitz and Poster Presentation16:50 - 17:05Photo / BreakSession for Ph.D. Program in Humanics | Chair: Jun Izawa (University of Tsukuba)17:05 - 17:40Creating NeurologicalRehabilitation with Brain-Machine Junichi UshibaInterfacesKeio UniversityPage 1117:40 - 18:15Artificial Intelligence and BrainScienceOkinawa Institute of Science andTechnology Graduate UniversityPage 1218:15Closing18:30 - 20:00Reception

2Kenji DoyaInformation1. No outlets are available nearby audience seats2. No smoking in symposium venues: please smoke at the designated area on each floor3. Wireless LAN is available in the foyer.

Free lunch will be provided for pre-registered participants.

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3Session forWPI-IIIS10:15 - 10:50Neural mechanisms for learning from a smallsampleABSTRACTMitsuo KawatoATR Brain InformationCommunication ResearchLaboratory Group

4Deep neural networks have been remarkably useful forimage classification. Combined with reinforcement learningalgorithms, deep neural networks have outperformedhuman experts in “Go”. To achieve such successes, millionsof images, and tens of millions of games have been utilizedas training data sets in the supervised learning or trainingtrials in the reinforcement learning. Meanwhile, in the2015 DARPA robotics challenge final competition, manyhumanoid robots fell while walking on sand, going up stairs,turning bulbs, or getting out of a car. A small number ofhumanoids completed all the tasks, but they were extremelyslower than humans. By age 5, human infants are able toexecute all of the above tasks more quickly and reliably thanhumanoid robots developed by world premier researchers.
What could be the reasons of this dramatic contrastbetween success and failure for simulated versus real-worldtasks by artificial intelligence? In the simulated video gamesand “Go”, the degrees of freedom of the controlled systemwere relatively small, there were no hidden variables,and state transitions were deterministic without noise andperfectly described by simple rules. Thus, the computersimulations were exactly correct without errors. For the finalreason, tens of millions of simulated games are generated bysoftware players, and they can be used efficiently for DeepQlearning. In contrast, for a humanoid robot in the real world,many physical processes, including contact and friction, aredifficult to model. Thus, a large number of training sampleis unavailable. Brains resolve this difficult issue of learningfrom a small sample. I show a persuasive example of thiswithout genetic prewiring. I propose that synchronization ofneural firing, information communication between differentareas, and metacognition are useful neural mechanisms forthis characteristics.Session forWPI-IIIS10:50 - 11:25Searching for neural correlates of sleepinessABSTRACTSakiko HonjohWPI-IIIS, University of TsukubaSleep is a tightly regulated biological process as shown bythe fact that humans spend one-third of their lives asleep.
As we stay awake longer, a “sleep need” accumulates inthe brain and we feel sleepier and sleepier, resulting in amarked decline in our cognitive ability. Since accumulated“sleep need” subsequently promotes deep NREM sleep,slow wave, NREM sleep-specific brain waves are currentlythe best marker for sleep need. However, the molecularidentities of “sleep need” and how it promotes deep NREMsleep remain unclear. Therefore, to better understand theneural mechanisms underlying prolonged, wakefulnessinduced sleepiness, we recorded neural activities inmultiple brain regions across sleep/wake cycles in freelybehaving mice. We quantified firing rates of each neuron,slow wave activity during NREM sleep, and estimated “sleepneed” at an individual level based on previous wake/sleephistory. Our analyses revealed that thalamic neurons showa consistent switch-like behavior, with the highest and thelowest firing in wake and in NREM sleep, respectively. Incontrast, cortical neuron activity was heterogeneous andthe cortex possesses a diverse population of wake-active,NREM sleep–active, and REM sleep-active neurons. Notall neurons showed a vigilance state-dependent firing rateand our analyses also revealed that a minor cortical neuronpopulation shows significant correlation between firing rateand estimated sleep need.

536th TakamineConference11:25 - 12:10Toward the Mysteries of SleepABSTRACTMasashi YanagisawaWPI-IIIS, University of Tsukuba6Although sleep is a ubiquitous behavior in animal specieswith well-developed central nervous systems, manyaspects in the neurobiology of sleep remain mysterious.
Our discovery of orexin, a hypothalamic neuropeptideinvolved in the maintenance of wakefulness, has triggeredan intensive research examining the exact role of theorexinergic and other neural pathways in the regulationof sleep/wakefulness. The orexin receptor antagonistsuvorexant, which specifically block the endogenouswaking system, has been approved as a new drug totreat insomnia. Also, since the sleep disorder narcolepsycataplexy is caused by orexin deficiency, orexin receptoragonists are expected to provide mechanistic therapyfor narcolepsy; they will likely be also useful for treatingexcessive sleepiness due to other etiologies.
Despite the fact that the executive neurocircuitry andneurochemistry for sleep/wake switching has beenincreasingly revealed in recent years, the mechanism forhomeostatic regulation of sleep, as well as the neuralsubstrate for "sleepiness" (sleep need), remains unknown. Tocrack open this black box, we have initiated a large-scaleforward genetic screen of sleep/wake phenotype in micebased on true somnographic (EEG/EMG) measurements.
We have so far screened >8,000 heterozygous ENUmutagenized founders and established a number ofpedigrees exhibiting heritable and specific sleep/wakeabnormalities. By combining linkage analysis and the nextgeneration whole exome sequencing, we have molecularlyidentified and verified the causal mutation in several ofthese pedigrees. Biochemical and neurophysiologicalanalyses of these mutations are underway. Since thesedominant mutations cause strong phenotypic traits, weexpect that the mutated genes will provide new insights intothe elusive pathway regulating sleep/wakefulness. Indeed,through a systematic cross-comparison of the Sleepymutants and sleep-deprived mice, we have recently foundthat the cumulative phosphorylation state of a specific set ofmostly synaptic proteins may be the molecular substrate ofsleep need.
References
1.F unato et al. Forward-genetics analysis of sleep inrandomly mutagenized mice. Nature 539: 378-383, 2016
2.Wang et al. Quantitative phosphoproteomic analysis ofthe molecular substrates of sleep need. Nature, 558: 435–439, 201836th TakamineConference12:30 - 13:05An engineering approach to painABSTRACTBen SeymourCenter for Information andNeural Networks, NationalInstitute of Information andCommunications TechnologyAbstract: Pain is often considered intangibly subjective,and neuroscience theory has so far failed to capture thecore essence of pain as a sensory and emotional percept.
Since noxious stimulation usually leads to the perceptionof pain, pain has traditionally been thought of as sensorynociception, but its variability and sensitivity to a broadarray of cognitive and motivational factors have ledto a common view that it is inherently imprecise andintangibly subjective. However the core function of pain ismotivational - to direct both short and long-term behaviouraway from harm. Here I will show how an engineeringapproach allows us to reframe pain in terms of signalprocessing and control theory, and present a reinforcementlearning model of pain. This offers a mechanisticunderstanding of how the brain supports pain perceptionand behaviour, illustrating the underlying computationalarchitecture of the pain system in the brain. Importantly, itprovides an explanation as to why pain is tuned by multiplefactors and necessarily supported by a distributed networkof brain regions, and so recasts pain as a precise andobjectifiable control signal.
Bio: Ben Seymour is Principal Investigator at the Centerfor Information and Neural Networks (National instituteof Information and Communications Technology) and theComputational and Biological Learning Lab at CambridgeUniversity Department of Engineering. He received hisundergraduate degrees in molecular biology and medicineat Manchester University, and PhD in neuroscience atUCL. He does interdisciplinary research at the interfaceof neuroscience and engineering, focusing on the systemsand computational neuroscience of pain and learning, andapplied research in clinical neuroengineering.

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36th TakamineConference13:05 - 13:40Function and connectivity of a visual circuitunderlying optic flow processing in zebrafishABSTRACTFumi KuboNational Institute of GeneticsA wide range of animals use global image motion toactively stabilize their position or gaze by compensatorymovements, such as the optomotor or optokineticresponses. In zebrafish, the main visual area that processoptic flow information is the pretectum. Previous studieshave shown that pretectal neurons distinguish differentoptic flow patterns, such as rotation and translation, to driveappropriate compensatory behaviors. To elucidate criticalneuroanatomical features that underlie this sensorimotortransformation, we have combined functional imagingand morphological reconstruction of single cells. Usinga function-guided inducible morphological analysis(FuGIMA) that we developed, we identified a feed forwardpretectum-premotor circuit in which “simple” directionselective information is combined to produce “complex”functional responses and transmitted to premotor centersin the hindbrain. Furthermore, using a whole-brainelectron microscopy stack of larval zebrafish from whichwe recorded the responses of pretectal neurons, we aredetermining the interconnectivity of pretectal neurons madeby their direct synaptic connections. These approaches willdirectly test and refine our circuit model for the binocularoptic flow computation in the pretectum.
References:Kramer A, Wu Y, Baier H, Kubo F. (2019) Neuronalarchitecture of a visual center that processes optic flow.
Neuron 103, 118-132.
Kubo F, Hablitzel B, Dal Maschio M, Driever W, Baier H,Arrenberg AB. (2014) Functional architecture of an opticflow responsive area that drives horizontal eye movementsin zebrafish. Neuron 81, 1344-59.

836th TakamineConference14:10 - 14:55Sleep: A worm’s eye viewABSTRACTHenrik BringmannPhilipps University of Marburg,GermanySleep is conserved from jellyfish to humans and can thusbe studied in simple model systems. Perhaps the simplestyet molecularly accessible model system that sleeps is C.
elegans. How and why does a simple animal such as “theworm” sleep? The answers to this question can shed lighton the fundamental reasons for how and why we sleep. Weare applying a combination of genetics, functional imaging,optogenetics, and physiological analysis to find out howand why C. elegans sleeps. We showed that C. elegansrequires a single sleep-active neuron called RIS to inducesleep. RIS is controlled by upstream circuits that measureand translate wakefulness into sleep. Cellular stress activatesRIS through EGFR signaling and through the stress-sensingALA neuron, thus increasing sleep. Without sleep, larvaeshow an increased progression of aging phenotypes, thatdecreases the rate of survival. Thus, sleep in C. elegans isultimately simplified: It requires a single sleep-active neuronthat is controlled by upstream circuits and responds to sleepneed. Sleep appears to serve basic functions that includecounteracting the progression of aging phenotypes.

936th TakamineConference14:55 - 15:30CryoEM analysis of the GPCR neurotensinreceptor 1-G protein complexABSTRACTHideaki KatoThe University of Tokyo10G protein-coupled receptor (GPCR) family is one of thelargest membrane receptor protein families in human, andthey are involved in almost every physiological process.
The neurotensin receptor 1 (NTSR1) is a GPCR involvedin regulation of blood pressure, body temperature, weight,and response to pain. NTSR1 couples to multiple G-proteinsubtypes (i.e. Gs, Gi/o, Gq/11, and G12/13), but themolecular details of G-protein activation remain unknown.
In this symposium, I will present 3Å structures of the humanNTSR1 in complex with the heterotrimeric Gi1 protein intwo distinct conformations (C and NC state). While theC-state complex is similar to recently reported GPCR-Gi/o complexes, the G protein in the NC state is rotated by~45 degrees relative to the receptor. NTSR1 in the NC stateexhibits features of both active and inactive conformations,suggesting that the structure represents an intermediatealong the G-protein activation pathway. This structuralinformation, complemented by molecular dynamicssimulations and functional studies, provide insights into thecomplex process of G-protein activation.Session for Ph.D.
Program in Humanics17:05 - 17:40Creating Neurological Rehabilitationwith Brain-Machine InterfacesABSTRACTJunichi UshibaKeio UniversityStroke is a major disease in aging societies. Annually, 5million people worldwide are left permanently disabledby strokes, placing a burden on family and community.
According to WHO, stroke burden DALY is set toincrease by 160% between 1990 and 2020. Under suchcircumstances, how will technology support our lives andlivelihood? It had been believed that the brain cannotrecover once it sustains a structural stroke injury, butrecent treatment aided by advanced technologies witha brain-computer interface (BCI), electrical/magneticcortical stimulation, and robotics has succeeded in guidingfunctional reorganization of brains damaged by stroke.
After the reorganization, brains gain the neural activityof the spared cortico-spinal-muscular pathways, andreanimates paralyzed limbs. A recent challenge with datadriven approach identifies optimal brain regions of careto maximize therapeutic effects in these treatments. Datadriven approach is also used to select and adopt the mostefficient therapeutics for each individual patient. In thesesenses, computational approaches for describing neuraldynamics and recovery process are becoming important tobridge the gaps between fundamental sciences and realworld clinics. Creating computational syntax as a commonlanguage will help understanding brain function andneurological rehabilitation entirely. In this talk, I will discussthe present state and the future prospects of technologyaided neurological rehabilitation in an aging society.

11Session for Ph.D.
Program in Humanics17:40 - 18:15Artificial Intelligence and Brain ScienceABSTRACTHuman brain functions have long served as the targets ofdevelopment of artificial intelligent systems. Findings inneuroscience have provided guidance at multiple levelsin the designs of machine learning architectures andalgorithms. Today’s neuroscience, in turn, necessitatesapplications of artificial intelligence and machine learningalgorithms for making sense of huge datasets.
This lecture reviews examples of co-evolution of AI andbrain science and consider how they can help each otherfor further progress.

Kenji DoyaOkinawa Institute of Science andTechnology Graduate University12MEMO