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Date:
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January 11 (Wed) to 13 (Fri), 2017
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Place:
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Rusutsu Resort Hotel , Hokkaido, Japan |
Registration fee : 2,000 yen ( Student : free )
Schedule
| "Massive sensory processing in the brain - Data mining and computational modeling -" | |
| January 11 |
Special Session |
| 18:10-19:00 | Sliman J. Bensmaia (University of Chicago) |
| 19:10-20:00 | Daniel L. K. Yamins (Stanford University) |
| 20:10-21:00 | Masanori Murayama (RIKEN Brain Science Institute) |
| 21:00-23:00 | Poster session |
| January 12 |
Topic Session |
| 15:30-16:20 | Benedetto De Martino (University College London) |
| 16:30-17:20 | Yukio Nishimura (Kyoto University) |
| 17:30-18:20 | Toshiya Matsushima（Hokkaido University) |
| 20:00-22:00 | Poster session |
| January 13 |
Topic Session |
| 9:00-9:50 | Ichiro Tsuda（Hokkaido University） |
| 10:00-10:50 | Shinsuke Suzuki（Tohoku University） |
| 11:00-11:50 | Akihiro Funamizu(Cold Spring Harbor Laboratory) |
Abstracts and References：
Sliman J. Bensmaia
Associate Professor,
Department of Organismal Biology and Anatomy
Committee on Computational Neuroscience
Committee on Neurobiology
University of Chicago
The neural basis of tactile texture perception in somatosensory cortex
Our sense of touch endows us with an exquisite sensitivity to surface texture. One of the remarkable aspects of tactile texture processing is that it operates over 6 orders of magnitude in element sizes, from the smallest discernible elements (on the order of 10s or nanometers) to the largest elements that can fit on a fingertip, measured in tens of millimeters. This wide range of scales is accommodated by distributing information across three types of mechanoreceptive afferents, each sensitive to surface elements over different spatial scales. The coarsest textural features, those on the order of millimeters, are conveyed in the spatial pattern of activation in slowly adapting type 1 (SA1) afferents. Finer features, ranging in size from microns to nanometers, are conveyed in the temporal spiking patterns of rapidly adapting (RA) and Pacinian corpuscle associated (PC) afferents. PC afferents are especially notable for their ability to phase-lock to vibrations as high as 1000 Hz. It is unknown to what extent these peripheral streams of information are integrated to achieve a unitary sensory experience of texture. Human behavior is founded on the ability to identify meaningful entities in complex noisy data streams that constantly bombard the senses. For example, in vision, retinal input is transformed into rich object-based scenes; in audition, sound waves are transformed into words and sentences. In this talk, I will describe my work using computational models to help uncover how sensory cortex accomplishes these enormous computational feats.
Top-down control of sensory processing by higher cortical areas is essential for sensory perception. Despite its importance, little is known about the mechanisms executing this control. Recently, we identified a reverberating cortical circuit that underlies somatosensory perception in the mouse hindpaw. We revealed that 1) physiologically, the circuit consists of sensory input from the primary somatosensory cortex (S1) to secondary motor cortex (M2) that is converted into a feedback projection that returns from M2 to S1, that 2) anatomically, the circuit comprises a long-range recurrent intracortical connection between S1 and M2. Axons from M2 to S1 target both deep and superficial cortical layers in a bidirectional projection pattern that is characteristic of top-down projections. In this talk, I will introduce physiological roles of the top-down pathway in brain functions including perception and memory consolidation of tactile information.
As we navigate through life, we are constantly faced with choices that require us to assign and compare the values of different options or actions. Some of these value-based choices seem
relatively straightforward (‘what should I eat for lunch?’) and others less so (‘which job offer should I take?’). No matter how simple or complex these choices are, they are often accompanied by a sense of confidence in having made the right choice. I will show recent work done in my lab in which we dissociate behaviourally, computationally and neurally the value estimation (‘how much do I like something?’) from internal fluctuations in confidence (‘how sure am I?’). I will then show how an explicit and accurate representation of confidence impacts on behaviour shaping the quality of future value-based decisions. Finally I will present a paradigmatic example of dissociation between beliefs and actions in obsessive-compulsive disorder.
There is accumulating evidence to suggest that the brain represents the expected value or utility of options at the time of decision-making. However, much less is known about how it is that value signals are constructed. In the first part of my talk, I will discuss how valuations for food rewards are constructed in the brain. Using a food-based decision task combined with multivariate analysis of fMRI data, I will demonstrate that values of food items can be predicted from beliefs about constituent nutritive attributes of food, and that those attributes are represented in lateral orbitofrontal cortex (lOFC), suggesting a key role for lOFC in encoding the precursor representations subsequently used to compute integrated subjective values. In the second part, I will present a study trying to explore the contagions nature of human valuation under risk. Using fMRI combined with computational modeling of behavioral data, I will show that human preference for risk can be systematically altered by the act of observing others’ risk-related decisions. Furthermore, the contagious behavioral shift is implemented via a neural representation of risk in the striatum (caudate nucleus). These findings together provide a mechanistic account for how value is constructed in the brain. For actions under limited sensory observation, it is essential to infer environmental state using a dynamic model that takes action effects into account. This inference is optimally realized by dynamic Bayesian inference, e.g., Kalman filtering. It has been hypothesized that Bayesian inference is implemented in the cerebral cortex. Although neural network models of dynamic Bayesian inference have been proposed, they lacked direct experimental evidence. Here, in a goal-reaching task, we show that posterior parietal cortex (PPC) and adjacent posteromedial cortex (PM) implement the two fundamental factors of dynamic Bayesian inference: prediction of hidden state using an internal state transition model and updating it using sensory evidence. We optically imaged the activity of neurons in cortical layers 2, 3, and 5 of mice in an auditory virtual reality system. As mice approached a reward site, anticipatory licking increased even when sound cues were intermittently presented; this was disturbed by PPC silencing. Probabilistic population decoding revealed that neurons in PPC and PM represented goal distances during sound omission (prediction), especially in PPC layers 3 and 5. Prediction improved with observation of cue sounds (updating). Our results illustrate how cerebral cortex realizes mental simulation using an action-dependent dynamic model.
To investigate how these peripheral representations of texture transform as they ascend the somatosensory neuraxis, we scanned a large set of natural and artificial surfaces across the fingertip of Rhesus macaques while recording the responses evoked in single-units in primary somatosensory cortex (areas 3b, 1 and 2). Most cortical responses showed evidence of integration across multiple streams of afferent information. For example, many neurons were characterized by both complex spatial receptive fields (indicative of input from the spatial pattern of activation across SA1 afferents) as well strong at the offset of indentations (a signature of RA and PC input). In striking contrast to the main population however, we found a small subset of neurons that appeared to be driven solely by PC responses. In contrast to the rest of S1, these neurons preserved some of the finely timed spiking patterns found peripheral texture responses. S1 appears to use both convergence and segregation as strategies to extract texture information from peripheral representations.
References :
Spatial and temporal codes mediate the tactile perception of natural textures.
Daniel L. K. Yamins
Assistant Professor of Psychology and Computer Science, Stanford University
Investigator, Stanford Neurosciences Institute
Using Artificial-Intelligence-Driven Deep Neural Networks to Uncover Principles of Brain Representation and Organization
The core observation underlying my work is that optimizing neural networks to solve challenging real-world artificial intelligence (AI) tasks can yield predictive models of the cortical neurons that support these tasks. I will first describe how we leveraged recent advances in AI to train a neural network that approaches human-level performance on a challenging visual object recognition task. Critically, even though this network was not explicitly fit to neural data, it is nonetheless predictive of neural response patterns of neurons in multiple areas of the visual pathway, including higher cortical areas that have long resisted modeling attempts. Intriguingly, an analogous approach turns out be helpful for studying audition, where we recently found that neural networks optimized for word recognition and speaker identification tasks naturally predict responses in human auditory cortex to a wide spectrum of natural sound stimuli, and help differentiate poorly understood non-primary auditory cortical regions. Together, these findings suggest the beginnings of a general approach to understanding sensory processing the brain.
I'll give an overview of these results, explain how they fit into the historical trajectory of AI and computational neuroscience, and discuss future questions of great interest that may benefit from a similar approach.
References :
1. Hong H, Yamins DLK, Majaj NJ, DiCarlo JJ (2016) Explicit information for category-orthogonal object properties increases along the ventral stream. Nat Neurosci 19:613–622.
2. Yamins DL, DiCarlo JJ (2016a) Eight open questions in the computational modeling of higher sensory cortex. Current Opinion in Neurobiology 37:114–120.
3. Yamins DLK, DiCarlo JJ (2016b) Using goal-driven deep learning models to understand sensory cortex. Nat Neurosci 19:356–365.
4. Yamins DLK, Hong H, Cadieu CF, Solomon EA, Seibert D, DiCarlo JJ (2014) Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences 111:8619–8624.
Masanori Murayama
RIKEN Brain Science Institute (BSI),
Laboratory for Behavioral Neurophysiology
Top-Down Cortical Input for Perception and Memory Consolidation
References :
1. A Top-Down Cortical Circuit for Accurate Sensory Perception.
Benedetto De Martino
University College of London
Beliefs, Confidence and Behavioural Control
References :
1. De Martino, Fleming, Garrett, Dolan “Confidence in value-based choice” Nature Neuroscience 2013.
2. Folke, Jacobsen, Fleming, De Martino "Explicit representation of confidence informs future value-based decisions” Forthcoming in Nature Human Behaviour 2017.
Yukio Nishimura
Department of Neuroscience, Graduate school of Medicine, Kyoto University
Causal link between meso-limbic system and motor system
In competitive sports and rehabilitation, motivation might be a key issue for improving motor performance. However, the neural substrate that linking the motivation system and motor system remains largely unclear. It is generally thought that the meso-limbic system including nucleus accumbens (NAc) ventral tegmental area (VTA) regulates motivation-driven effort but is not involved in the direct control of movement. Recently, we clarified that the NAc has a causal role for motor control during recovery after spinal cord injury (Sawada et al., Sicence, 2015). In this workshop, the NAc up-regulates the activity of the motor cortex and is directly involved in demanding motor control. This idea might be similar to what has been described by Brodal, neurologist as “mental energy” in his self-observation after stroke.
1. Sawada M, Kato K, Kunieda T, Mikuni N, Miyamoto S, Onoe H, Isa T, Nishimura Y. Function of nucleus accumbens in motor control during recovery after spinal cord injury. SCIENCE, 2015:Vol. 350 no. 6256 pp. 98-101.
2. Nishimura Y, Perlmutter SI, Ryan WE, Fetz EE. Spike-timing dependent plasticity in primate corticospinal connections induced during free behavior. NEURON. 2013 Dec 4; 80(5):1301-9.
3. Nishimura Y, Onoe H, Onoe K, Morichika Y, Tsukada H, Isa T. Neural substrates for the motivational regulation of motor recovery after spinal-cord injury. PLoS One. 2011; 6(9):e24854. doi: 10.1371/journal.pone.0024854.
4. Nishimura Y, Onoe H, Morichika Y, Perfiliev S, Tsukada H, Isa T. Time-dependent central compensatory mechanisms of finger dexterity after spinal cord injury. SCIENCE. 2007; 318(5853):1150-5.
Toshiya Matsushima
Department of Biology, Faculty of Science, Hokkaido University
What is rational for animals?
References :
1. Kawamori, A., Matsushima, T. (2012) Sympatric divergence of risk sensitivity and diet menus in three species of tit. Animal Behaviour 84: 1001-1-12 (doi: 10.1016/j.anbehav.2012.07.026)
2. Amita, H., Matsushima T. (2014) Competitor suppresses neuronal representation of food reward in the nucleus accumbens / medial striatum of domestic chicks. Behavioral Brain Research 268: 139-149 (doi: 10.1016/j.bbr.2014.04.004)
3. Wen, C., Ogura, Y., Matsushima, T. (2016) Striatal and tegmental neurons code critical signals for temporal-difference learning of state value in domestic chicks. Frontiers in Neuroscience (Decision Neuroscience) (doi: 10.3389/fnins.2016.00476)
Ichiro Tsuda
Department of Mathematics, Faculty of Science, Hokkaido University
Finding Math Embedded into Brain Dynamics as Universal Mind
References:
1. Tsuda, I., Yamaguti, Y. and Watanabe, H. (2016) Self-organization with
constraints―A mathematical model for functional differentiation. Entropy 18, 74: 1-13.
2. Yamaguti, I. and Tsuda, I. (2015) Mathematical modeling for evolution of
heterogeneous modules in the brain. Neural Networks, 62, 3-10.
3. Tsuda, I. (2001) Toward an interpretation of dynamic neural activity in terms of
chaotic dynamical systems. Behavioral and Brain Sciences, 24(5), 793-847.
4. 津田一郎、(2016) 「脳のなかに数学を見る」（共立出版）
5. 津田一郎、(2015) 「心はすべて数学である」 （文藝春秋）
Shinsuke Suzuki
Frontier Research Institute for Interdisciplinary Sciences, Tohoku University
Neural representation of value: its basis and contagious nature
References :
1. Shinsuke Suzuki, Emily L. S. Jensen, Peter Bossaerts, John P. O’Doherty, "Behavioral contagion during learning about another agent's risk-preferences acts on the neural representation of decision risk", Proceedings of the National Academy of Sciences of the United States of America (PNAS), Vol. 113, pp. 3755-3760, 2016.
2. Shinsuke Suzuki, Ryo Adachi, Simon Dunne, Peter Bossaerts, John P. O’Doherty, "Neural mechanisms underlying human consensus decision-making", Neuron, Vol. 86, pp. 591-602, 2015.
3. Shinsuke Suzuki, Norihiro Harasawa, Kenichi Ueno, Justin L Gardner, Noritaka Ichinohe, Masahiko Haruno, Kang Cheng, Hiroyuki Nakahara, "Learning to simulate other's decisions", Neuron, Vol. 74, pp. 1125-1137, 2012.
Akihiro Funamizu
Zador Lab, Cold Spring Harbor Laboratory
Neural substrate of dynamic Bayesian inference in the cerebral cortex
References :
1. Akihiro Funamizu, Bernd Kuhn, Kenji Doya, "Neural substrate of dynamic Bayesian inference in the cerebral cortex.", Nature Neuroscience, 2016, doi:10.1038/nn.4390.
2. 船水章大，銅谷賢治，”予測―大脳新皮質のベイジアンフィルタ仮説” 生体の科学. 66 (1), pp.33-37, 2015.