第18回 冬のワークショップ

身体制御と自己表象- "Body control and self representation "

日程:2018年1月9日(火)-11日(木)

会場:
ルスツリゾート(北海道蛇田郡留寿都村字泉川13)
  http://www.rusutsu.co.jp/
会場地図:
ノースウイング コンベンションホール 18番ホール 

スケジュール:

 1月9日(火)-11日(木)
身体制御と自己表象 "Body control and self representation"
9日 スペシャルセッション
18:10 - 19:00 H.Henrik Ehrsson (Karolinska Institutet)
19:10 - 20:00 谷淳(沖縄科学技術大学院大学)
20:10 - 21:00 Jose M.Carmena (University of California-Berkeley)
21:00-23:00 ポスターセッション
10日 トピックセッション
15:30 - 16:20 Antonia Hamilton (University College London)
16:30 - 17:20 亀田達也(東京大学)
17:30 - 18:20 横井惇(国立研究開発法人情報通信研究機構 脳情報通信融合研究センター)
20:00-23:00 ポスターセッション
11日 トピックセッション
9:00 - 9:50 大泉匡史(株式会社アラヤ)
10:00 - 10:50 瀧山健 (東京農工大学)
11:00 - 11:50 野村洋(北海道大学)




Abstracts and References:


H.Henrik Ehrsson
Department of Neuroscience, Karolinska Institutet


Multisensory mechanisms of body ownership

When we look down at our body, we immediately perceive that it belongs to us. The question of how we experience ownership of a body distinct from the external world is a fundamental problem in psychology and neuroscience. But how does the brain actually identify its own body? I will describe how cognitive neuroscientists have recently begun to address this fundamental question. A key idea is that parts of the body are distinguished from the external world by the patterns they produce of correlated information from different sensory modalities (vision, touch and muscle sense). These correlations are hypothesized to be detected by neuronal populations that integrate multisensory information from the space near the body. We have recently used a combination of functional magnetic resonance imaging, electrocorticography and human behavioral experiments to present experimental evidence in support of these predictions. To change the feeling of body ownership, perceptual illusions were used where healthy individuals experienced that a rubber hand was their own, that a mannequin was their body (“body-swap illusion”), or, that they are outside their physical body and looking at it from the perspective of another individual (“out-of-body illusion”).
The accumulated evidence suggests that the feeling of limb ownership is produced by the integration of visual, tactile and proprioceptive signals by populations of multisensory neurons in the premotor cortex and the posterior parietal cortices. This integration process obeys precise temporal and spatial congruency principles, is limited to the space near the body, and operates in body-part-centered reference frames. The unified percept of owning an entire body, as opposed to fragmented parts, additionally requires the engagement of active neuronal populations in the premotor cortex that integrates multisensory information across body segments. Finally, the sense that our body is being located at a specific place in the world requires the integration of information about body ownership and self-location. Recent fMRI results suggest that the posterior cingulate cortex has a key role in this process.
By clarifying how the normal brain produces a sense of ownership of one’s body, we can learn to project ownership onto artificial bodies and simulated virtual ones; and even make two people have the experience of swapping bodies with one another. This could have ground-breaking applications in the fields of virtual reality and neuro-prosthetics.

References :
1. Collins, K.L., Guterstam, A., Cronin, J.A., Olson, J.D., *Ehrsson, H.H., and *Ojemann, J.G. (2017) Ownership of an Artificial Limb Induced by Electrical Brain Stimulation. Proc Natl Acad Sci USA, 114(1):166-171
2. Guterstam, A., Björnsdotter, M., Gentile, G., & Ehrsson, H.H. (2015) Posterior cingulate cortex integrates the senses of self-location and body ownership. Current Biology 25(11):1416-25
3. Gentile, G., Guterstam, A., Brozzoli, C., Ehrsson, H.H. (2013) Disintegration of multisensory signals from the real hand reduces default limb self-attribution: an fMRI study. Journal of Neuroscience 33(33):13350-66.
4. Petkova, V.I., Björnsdotter, M., Gentile, G., Jonsson, T., Li, T.Q., & Ehrsson, H.H. (2011) From part to whole-body ownership in the multisensory brain. Current Biology, 21 1-5
5. Ehrsson, H.H. (2007) The experimental induction of out-of-body experiences. Science 317(5841):1048


谷淳
Jun Tani
沖縄科学技術大学院大学
Okinawa Institute of Science and Technology


Emergentist Account for Non-Reductive Consciousness: From Neurorobotics Study

This talk proposes that the mind is comprised of emergent phenomena, which appear via intricate and often conflictive interactions between top-down intentional processes involved in proactively acting on the external world and bottom-up recognition processes involved in receiving the resultant perceptual reality. This view has been tested via a series of neurorobotics experiments employing predictive coding principles implemented in “deep” recurrent neural network (RNN) models conducted in my lab for more than 20 years. These experimental results suggest that phenomena of consciousness as well as free will can be structurally accounted by circular causality inevitably emerged in the enactment loop. Finally, I discuss whether consciousness is still a hard problem or not.

References :
1. J. Tani: “Exploring Robotic Minds: Actions, Symbols, and Consciousness as Self-Organizing Dynamic Phenomena.”, New York: Oxford University Press, 2016.
(https://global.oup.com/academic/product/exploring-robotic-minds-9780190281069?cc=kr&lang=en&)
2. J. Tani: Exploring Robotic Minds by Predictive Coding Principle. The Newsletter of the Technical Committee on Cognitive and Developmental Systems. 14(1) , 4-5. 2017. (http://goo.gl/dyrg6s)

Jose M.Carmena
University of California-Berkeley


Understanding the Neural Basis of Skill Learning Using Brain-Machine Interfaces

We are interested in how skills are learned and consolidated in the brain. We approach this problem using a brain-machine interface (BMI) skill learning paradigm. In addition to holding great therapeutic potential as assistive and rehabilitation technology, BMIs provide also a powerful framework for examining basic neuroscience questions, especially those related to the neural correlates of learning behavior as it offers researchers the unique opportunity to directly control the causal relationship between neuronal activity and behavioral output. In particular, we focus on the question of how neuroplasticity relates to the acquisition and consolidation of skills. The importance of this question is paramount as it impacts both brain function and dysfunction. In this talk I will present recent work from our laboratory using electrophysiology and imaging techniques in awake behaving primates and rodents, showing that neuroplasticity facilitates consolidation of neuroprosthetic motor skill in a way that resembles that of natural motor learning, and that cortico-striatal plasticity is necessary for neuroprosthetic skill learning. In addition, we examine the question of how a task-relevant neural population explores and consolidates spatiotemporal patterns supporting neuroprosthetic skill learning? Motor learning studies have found that subjects produce high initial variability in both movements and neural activity which decreases with training, resulting in the consolidation of particular movements and activity patterns. By modeling private and shared input signals which produce independent and coordinated activity patterns, we studied how changes in input signals sculpt generated activity. Our findings describe neuroprosthetic skill learning as a process of spatiotemporal neural pattern consolidation whereby the strengthening of task-relevant input signals coordinates initially variable, high-dimensional activity. A greater understanding of the neural substrates of neuroprosthetic skill learning can provide insight into the mechanisms of natural sensorimotor learning as well as help guide the development of neurobiologically-informed neuroprosthetic systems designed to aid people suffering from devastating neurological conditions.

References :
1. Athalye V.R., Ganguly K., Costa R.M.*, and Carmena J.M.* (2017) Emergence of coordinated neural dynamics underlies neuroprosthetic learning and skillful control. Neuron 93(4):955-970.e5. doi: 10.1016/j.neuron.2017.01.016.
2. Orsborn A.L., Moorman H.G., Overduin S.A., Shanechi M.M., Dimitrov D.F. and Carmena J.M. (2014) Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82, pp. 1380-1393.
3. Clancy K.B.*, Koralek A.C.*, Costa R.M., Feldman D.E. and Carmena J.M. (2014) Volitional modulation of optically recorded calcium signals during neuroprosthetic learning. Nature Neuroscience 17(6), pp. 807-810.
4. Koralek A.C.*, Jin X.*, Long J.D., Costa R.M. and Carmena J.M. (2012) Corticostriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature 483(7389), pp. 331-335.
5. Ganguly K., Wallis J.D. and Carmena J.M. (2011) Reversible large-scale modification of cortical networks during neuroprosthetic control. Nature Neuroscience 14, pp. 662-667.
6. Ganguly K. and Carmena J.M. (2009) Emergence of a stable cortical map for neuroprosthetic control. PLoS Biology 7(7), e1000153.doi:10.1371/journal.pbio.1000153


Antonia Hamilton
Institute of Cognitive Neuroscience, University College London


Mechanisms of social interaction

Imitating other people and being imitated is a visuomotor process with important consequences for our social relationships. The mechanisms by which basic visuomotor functioning is employed in the service of social interaction remains unknown. This talk will describe work from my lab over the last few years which uses virtual reality and near-infrared spectroscopy to probe the neural and cognitive mechanisms of imitating and being imitated. We find evidence that imitation can act as a communicative process, sending social signals to others, but that these signals are not always received in a clear fashion. This work emphasises the importance of using new ecologically valid methods, and outlines future directions for the field.

References :


亀田達也
Tatsuya Kameda
東京大学大学院人文社会系研究科 社会心理学研究室
Department of Social Psychology, The University of Tokyo


Does “Ought” have Empirical Grounds in “Be”? Adaptive/Neural Bases of Distributive Justice

Distributive justice concerns the moral principles by which we seek to allocate resources fairly among diverse members of a society. Although the concept of fair allocation is one of the fundamental building blocks for societies, there is no clear consensus on how to achieve socially just allocations. I first consider evolutionary/adaptive bases of egalitarian social-allocation, which is found robustly across many hunter-gatherer societies as well as in some sectors of modern societies. Through anthropological data, evolutionary computer simulations, and behavioral experiments, I argue that the egalitarian social-allocation may be an adaptive device to reduce statistical risk involved in resource acquisition in natural environments collectively. I then extend this reasoning toward a more micro direction, investigating neuro-cognitive commonalities of distributive judgments and risky decisions. We explore the hypothesis that people’s allocation decisions for others are closely related to economic decisions for oneself at behavioral, cognitive and neural levels, via a concern about the minimum, worst-off position. In a series of experiments using attention-monitoring and brain-imaging techniques, we investigated this “maximin” concern (maximizing the minimum possible payoff) via responses in two seemingly disparate tasks: third-party distribution of rewards for others, and choosing gambles for self. The experiments revealed three robust results: (1) participants’ distributive choices closely matched their risk preferences — “Rawlsians” who maximized the worst-off position in distributions for others avoided riskier gambles for themselves, while “utilitarians” who favored the largest-total distributions preferred riskier but more profitable gambles; (2) across such individual choice-preferences, however, participants generally showed the greatest spontaneous attention to information about the worst possible outcomes in both tasks; and (3) this robust concern about the minimum outcomes was correlated with activation of the right temporo-parietal junction (RTPJ), the region associated with perspective-taking. The results provide convergent evidence that social distribution for others is psychologically linked to risky decision-making for self, drawing on common cognitive-neural processes with spontaneous perspective-taking of the worst-off position. I conclude my talk by discussing how social neuroscience and social sciences can create better collaborations toward mutually more beneficial, reciprocal ends.

References :
1. Kameda, T., Takezawa, M., & Hastie, R. (2005). Where do norms come from? The example of communal-sharing. Current Directions in Psychological Science, 14, 331-334.
2. Kameda, T., Takezawa, M., Ohtsubo, Y., & Hastie, R. (2010). Are our minds fundamentally egalitarian? Adaptive bases of different socio-cultural models about distributive justice. In M. Schaller, S. J., Heine, A. Norenzayan, T. Yamagishi, & T. Kameda (Eds.), Evolution, culture, and the human mind (pp. 151-163). New York: Psychology Press.
3. Kameda, T., Inukai, K., Higuchi, S., Ogawa, A., Kim, H., Matsuda, T., & Sakagami, M. (2016). Rawlsian maximin rule operates as a common cognitive anchor in distributive justice and risky decisions. Proceedings of the National Academy of Sciences of the USA, 113(42), 11817-11822.
4. 亀田達也(著)(2017). 『モラルの起源―実験社会科学からの問い』岩波新書.


横井惇
Atsushi Yokoi
国立研究開発法人情報通信研究機構 脳情報通信融合研究センター
Center for Information and Neural Networks, NICT, Japan


Hierarchical representation of sequences of finger movements in human neocortex

The hierarchical view of action sequencing, first proposed by Lashley (1951), has been supported by a large body of behavioural evidence. Yet its neuronal underpinnings remain unclear. In this talk, I will present the results of two functional magnetic resonance imaging (fMRI) experiments in which we used novel multivariate fMRI analysis techniques to ask whether and how complex sequences of finger movements are represented over the cortical motor areas in the human brain. The first experiment addresses the question whether there is genuine sequence encoding in primary motor cortex (M1). In the second experiment, we asked how complex sequences (8 sequences, 11 digits each) are hierarchically encoded in higher motor areas, such as premotor, supplementary motor areas, or parietal regions. Finally, I will also discuss possible coexistence of hierarchical and non-hierarchical sequence representation.

References :
1. Lashley, K. S. The problem of serial order in behavior. In L. A. Jeffress (Ed.), Cerebral mechanisms in be- havior. New York: Wiley, 1951.
2. Yokoi A., Arbuckle S.A., and Diedrichsen J. Does human primary motor cortex represent sequences of finger movements? bioRxiv. 2017. DOI: https://doi.org/10.1101/157438
3.Diedrichsen J., Yokoi A., and Arbuckle S.A. Pattern component modeling: A flexible approach for understanding the representational structure of brain activity patterns. Neuroimage. (in press), DOI: http://dx.doi.org/10.1016/j.neuroimage.2017.08.051


大泉匡史
Masafumi Oizumi
株式会社アラヤ
ARAYA inc.


An information theoretic approach to the boundary problem of consciousness

When two people, say Alice and Bob, are talking, Alice cannot be conscious of what Bob is conscious of. Alice's consciousness does not include Bob's consciousness, and vice versa. There seems to be a clear boundary between Alice’s and Bob's consciousness. That is, two independent conscious entities, namely Alice and Bob, exist even though they are "connected" by talking with each other. On the other hand, inside Alice's (or Bob’s) brain, the opposite situation happens. In Alice’s brain, there are two brains, i.e., left brain and right brain, which potentially could have their own independent consciousness if they were not connected by corpus callosum. This is actually the case in split brain patients whose corpus callosum is cut. However, in the normal brain, two independent consciousness of left brain and right brain do not exist. What exists is one unified consciousness, namely Alice (or Bob), which includes both left brain and right brain. There is no clear boundary between the left brain and right brain's consciousness. What is the critical difference between the two cases? In one case, there is a boundary but in the other case, there is no boundary. What physical mechanisms determine the boundaries of consciousness? Can we predict the position of such boundaries in a complex brain network? In this talk, I will discuss the boundary problem of consciousness based on Integrated Information Theory (IIT). IIT is an attempt to mathematically quantify consciousness from the viewpoint of information and integration, which are considered to be the essential properties of consciousness. I will explain the theoretical predictions of IIT on the boundary problem and discuss how we should test them by experiments.

References :
1. Oizumi M, Tsuchiya N, Amari S (2016) Unified Framework for Information Integration Based on Information Geometry. PNAS, 113, 14817-14822.
2. Oizumi M, Albantakis L, Tononi G (2014). From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0. PLoS Comp Biol, 10, e1003588.
3. 大泉匡史 (2014) 意識の統合情報理論.Clinical Neuroscience, 32, 905-912, 2014.
4. 大泉匡史, 土谷尚嗣 (2012) 温度計に意識はあるか? -- 意識レベルの定量化へ向けた理論と実践.LiSA, 19, 352-359.


瀧山健
Ken Takiyama
東京農工大学大学院 工学研究院
Tokyo University of Agriculture and Technology,Department of Engineering


Towards a unified theory of human motor learning

Diverse features of motor learning have been reported in numerous studies, but no single theoretical framework concurrently accounts for these features. Here, we propose models for motor learning to explain these features in a unified way by extending a motor primitive framework (ref. Thoroughman & Shadmehr, 2000, Nature). First, I explain about our computational model for uni-manual arm reaching movements [1]. The model assumes that the recruitment pattern of motor primitives is determined by the predicted movement error of an upcoming movement (prospective error). I demonstrate that this model has a strong explanatory power to reproduce a wide variety of motor-learning-related phenomena that have been separately explained by different computational models. Second, I explain about our model for uni- and bi-manual arm reaching movements [2]. The model assumes that the activities of motor primitives are “balanced” between the two types of arm reaching movements. I demonstrate that this model can simultaneously reproduce features of motor learning in uni- and bi-manual arm reaching movements, which were separately explained by different computational models. Finally, I introduce our ongoing studies; 1. data-driven approach to discussing motor control of whole-body motion for understanding motor-pattern-invariant mechanisms of motor learning [3] and 2. comparison of motor learning ability across young, elderly, stroke patients, and Parkinson patients for understanding neural correlates of motor learning.

References :
1. K. Takiyama, M. Hirashima, D. Nozaki, Prospective errors determine motor learning, Nature Communications, 6, 5925: 1-12 (2015)
2. K. Takiyama, Y. Sakai, A balanced motor primitive framework can simultaneously explain motor learning in unimanual and bimanual movements, Neural networks, 86, 80-89 (2017)
3. D. Furuki, K. Takiyama, Detecting the relevance of each motion component in whole-body motion to performance, the XXVI Congress of the International Society of Biomechanics (ISB), (2017)


野村洋
Hiroshi Nomura
北海道大学大学院薬学研究院 薬理学研究室
Department of Pharmacology, Graduate School of Pharmaceutical Sciences, Hokkaido University


Central histamine reactivates weak memory engrams and restores forgotten memories

Even after memories fade over long time, the lost memories may persist latently in the brain and sometimes reappear spontaneously. Reinforcement of positive modulators for retrieval of long-term memory may recover the forgotten items. However, how the retrieval of long-term memory is modulated is less understood than short-term memory. Thus, a method that promotes the retrieval of forgotten long-term memories has not been well established. Histamine in the central nervous system is implicated in learning and memory. Histamine H3 receptors inhibit the presynaptic release of histamine and other neurotransmitters and negatively regulate histamine synthesis. Because histamine H3 receptors are constitutively active, their inverse agonists upregulate histamine release. Therefore, histamine H3 receptor inverse agonists may enhance learning and memory. In this study, we show that histamine H3 receptor inverse agonists enhance retrieval of long-term memory and recover the forgotten memory in mice and humans. These findings indicate that activation of histamine receptor signaling in the PRh boosts reactivation of weak memory engrams and restores the apparently forgotten memories.

References :
1. Nomura H, Hara K, Abe R, Hitora-Imamura N, Nakayama R, Sasaki T, Matsuki N, Ikegaya Y. Memory formation and retrieval of neuronal silencing in the auditory cortex. Proc Natl Acad Sci U S A. 2015 Aug 4;112(31):9740-4.
2. Nakayama D, Iwata H, Teshirogi C, Ikegaya Y, Matsuki N, Nomura H. Long-delayed expression of the immediate early gene Arc/Arg3.1 refines neuronal circuits to perpetuate fear memory. J Neurosci. 2015 Jan 14;35(2):819-30.
3. Nakayama D, Baraki Z, Onoue K, Ikegaya Y, Matsuki N, Nomura H. Frontal association cortex is engaged in stimulus integration during associative learning. Curr Biol. 2015 Jan 5;25(1):117-23.


 
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