Keynote Speakers

Spike-timing-dependent synaptic plasticity in motor-sensor-motor loops

Klaus Pawelzik ,Institute for Theoretical Physics Hochschulring 18 University of Bremen .

In the song bird (’backward’) mappings from sensory representations to motor areas recently were proposed that would ’postdict’ the motor activations during singing. Such sensor-motor mappings represent inverse models of the motor-sensor-loop passing through the world and thereby can e.g. explain the impressive imitation capabilities of song birds [1]. The neurobiological mechanisms that might generate, fine tune and continuously adapt such inverse models, however, are not known.Here we show that spike timing dependent plasticity (STDP) of the inhibitory synapses is sufficient for the self-organisation of the inverse model in a simple closed loop motor-sensor-motor system [2]. Similar to the case of forward models,

where  predictable, self-generated inputs become suppressed [3], the proposed mechanism generates sparse motor activities by cancelling predictable fluctuations of the neurons’ excitabilities. Our results show that inverse mappings can be learned with an elementary and biologically plausible learning rule and thus could underly imitation learning. In our presentation we will discuss also the potential relevance of this mechanism for the operation state of recurrent networks as e.g. cortex [4].

[1] Hahnloser, R. & Ganguli, S. (2013). Vocal learning with inverse models. In S. Panzeri & P. Quiroga(Eds.), Principles of neural coding. CRC Taylor and Francis.
[2] Haas, J. S., Nowotny, T., and Abarbanel, H. D. (2006). Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. Neurophysiol., 96:3305-3313.
[3] Bell CC, Han VZ, Sugawara Y, Grant K. Synaptic plasticity in a cerebellum-like structure depends on temporal order. Nature. 1997;387:278281.
[4] Okun M., Lampl I.(2008) Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nature Neurosci. 11(5), 535-7.


Klaus Pawelzik is Professor of Theoretical Physics and Theoretical Biophysics and Vice-Director of the Centre for Cognitive Sciences (ZKW) at the University of Bremen/Germany. His research interests include Nonlinear Dynamics, Complex Systems, Theoretical Neuroscience, Neuroprosthetics, Robotics, and Econophysics. Klaus Pawelzik received his PhD in Theoretical Physics from the Johann-Wolfgang-Goethe Universitšt in Frankfurt/Main in 1990. In the years 2000-2005 he was speaker of the section "Computational Neuroscience" of the German Neuroscience Society (NWG) and he serves as an editor of the open access journal Frontiers in Computational Neuroscience.


The Human Connectome Project:Progress and Perspectives

 David Van Essen ,Washington University in St. Louis, USA .

Recent advances in noninvasive neuroimaging have set the stage for the systematic exploration of human brain circuits in health and disease. One such effort is the Human Connectome Project (HCP), which will characterize brain circuitry and its variability in a large population of healthy adults. This talk will review recent progress by a consortium of investigators at Washington University, University of Minnesota, University of Oxford, and 7 other institutions, who are engaged in a 5-year project to characterize the human connectome in 1,200 individuals (twins and their non-twin siblings). Information about structural and functional connectivity is being acquired using diffusion MRI and resting-state fMRI, respectively. Additional modalities include task-evoked fMRI and MEG/EEG, plus extensive behavioral testing

and genotyping. Each of these methods is powerful, yet faces significant technical limitations that are important to characterize and be mindful of when interpreting neuroimaging data.
Advanced visualization and analysis methods developed by the HCP enable characterization of brain circuits in individuals and group averages at high spatial resolution and at the level of functionally distinct brain parcels. Comparisons across subjects will reveal aspects of brain circuitry which are related to particular behavioral capacities and which are heritable or related to specific genetic variants. Data from the HCP is being made freely available to the neuroscience community via a user-friendly informatics platform. Altogether, the HCP will provide invaluable information about the healthy human brain and its variability and will set the stage for characterizing abnormal brain connectivity in a variety of brain disorders and diseases.


David C. Van Essen is Alumni Endowed Professor in the Anatomy & Neurobiology Department at Washington University in St. Louis. Along with Kamil Ugurbil, he is Principal Investigator of the Human Connectome Project, a $30 million NIH grant to map brain circuitry in a large population of healthy adults using cutting-edge neuroimaging methods. Van Essen’s physiological and anatomical studies of macaque visual cortex provide many insights into functional specialization within this distributed hierarchical system. He has pioneered the use of surface-based atlases for visualizing and analyzing cortical structure, function, development, and connectivity and for making comparisons across studies and across species. His tension-based theory of morphogenesis accounts for how and why the cortex gets its folds. His studies of human cerebral cortex provide insights regarding normal variability, abnormalities in specific diseases, and patterns of cortical development. He has served as Editor-in-Chief of the Journal of Neuroscience, founding chair of the OHBM, and President of the Society for Neuroscience. He is a fellow of the AAAS and has received the Raven Lifetime Achievement Award from the St. Louis Academy of Sciences and the Krieg Cortical Discoverer Award from the Cajal Club.


The Sigma Cognitive Architecture

Paul S. Rosenbloom ,University of Southern California (USC) .

A cognitive architecture provides a hypothesis about the fixed mechansims, and their integration, underlying intelligent behavior, whether in natural or artificial systems. The overall goal with Sigma is to leverage graphical models – with their ability to uniformly yield state-of-the-art algorithms across symbol, probability and signal processing – in moving significantly beyond today's state of the art in providing, and tightly integrating together, the capabilities required for virtual humans (and intelligent agents/robots). Sigma is a mixed (statistical+relational) hybrid (discrete+continuous) architecture that has so far demonstrated key aspects of memory and learning, decision making and problem solving, perception and imagery, multiagent reasoning and theory of mind, and natural language


Paul S. Rosenbloom is Professor of Computer Science at the University of Southern California and a project leader at USC’s Institute for Creative Technologies. He was a key member of USC’s Information Sciences Institute for two decades, leading new directions activities over the second decade, and finishing his time there as Deputy Director. Earlier he was on the faculty at Carnegie Mellon University (where he received his PhD in computer science) and Stanford University (where he received his BS in mathematical sciences). His research concentrates on cognitive architectures – models of the fixed structure underlying minds, whether natural or artificial – and on understanding the nature, structure and stature of computing as a scientific domain. He was a co-developer of the Soar architecture, and is currently leading the effort to create Sigma, a new breed of cognitive architecture based on graphical models. He is also a AAAI Fellow and the author of On Computing: The Fourth Great Scientific Domain (MIT Press, 2012).


Is Our Sensing Compressed?

David Cai ,Shanghai Jiaotong University .

Along the early stages of many sensory pathways, significant downstream reductions occur in the numbers of neurons transmitting stimuli. To understand how much information is lost in such a reduction, we establish a conceptual framework using theoretical and computational methods. In our work, we reveal a hidden linear structure intrinsic to this nonlinear neuronal network dynamics and show that there is a potential mechanism for preserving information through bottlenecks in sensory pathways. This mechanism is related to that preserves

image quality using compressed-sensing type data acquisition. We propose that the principle of compressed sensing may provide guidance for studying information transfer in more realistic neuronal network models as well as experiments studying sensory pathways.


David Cai is professor of mathematics and neural science in the Courant Institute of Mathematical Sciences and the Center for Neural Science at New York University. His research interests include applied mathematics, physics, and neuroscience.  He received his B.S. from Peking University, China, and Ph.D. from Northwestern University, U.S. He has conducted research in Los Alamos National Laboratory, University of North Carolina at Chapel Hill, the Institute for Advanced Study in Princeton, Shanghai Jiao Tong University, and New York University. He was awarded a Sloan Research Fellowship in 2001 in U.S. He was awarded Chang Jiang Scholarship in 2009 by the Chinese Ministry of Education. In 2010, he founded the Institute of Natural Sciences at Shanghai Jiao Tong University.


Delay Compensation in Neural Fields:Intrinsic Behavior Determines Tracking Performance

 Michael Wong ,Hong Kong University of Science & Technology .

Neural fields process continuous dynamic information, such as position and direction, by generating its own internal state to track the changes in external inputs. To achieve real-time tracking, it is critical to compensate the transmission and processing delays in the system. Here we show that dynamical synapses with short-term depression can enhance the mobility of the network states such that an effectively constant anticipatory time or zero-lag between the tracking state and the stimulus is achieved. The anticipatory time covers the range of 101 ms and decreases mildly with stimulus speed, in agreement with head-direction experiments in rodents.

The condition for anticipatory tracking demonstrates the strong correlation

between the intrinsic behavior and the tracking performance, reminiscent of the relation between the fluctuations and response functions in a wide range of many-body systems. For neural fields in general, we show that the displacement of the localized state relative to the stimulus during tracking is proportional to the intrinsic relaxation rate of the positional distortion of the localized state. Thus the parameter regions for delayed, perfect, and anticipative tracking correspond to static, ready-to-move, and spontaneously moving network states respectively. Furthermore, when the stimulus moves with the natural speed of the network state, the delay becomes effectively independent of the stimulus amplitude.

This work was done in collaboration with C. C. Alan Fung (HKUST) and Si Wu (Beijing Normal University), and was supported by the Research Grants Council of Hong Kong (grant numbers 604008, 605010 and N_HKUST606/12) and the National Foundation of Natural Science of China (No.91132702, No.31221003).


Prof. Michael Wong received PhD in Physics from University of California, Los Angeles in 1986. He did postdoctoral research in Imperial College London and Oxford University before moving to the Hong Kong University of Science and Technology, where he is now Professor of Physics. His research interest includes the study of complex and disordered systems; computational neuroscience; optimization; multi-agent systems; spin glasses; machine learning; and optimal control in telecommunications networks. He also serves as an editor for Journal of Statistical Mechanics: Theory and Experiment.

Brainnetome: A New Avenue to Understand the Brain and its Disorders

 Tianzi Jiang ,Institute of Automation, Chinese Academy of Science .

The Brainnetome (Brain-net-ome) is a new "-ome" in which the brain network is its basic research unit. It includes at least the following essential components: network topological structure (connectome), performance, dynamics, manifestation of functions and malfunctions of brain on different scales, genetic basis of brain networks, and simulating and modeling brain networks on supercomputing facilities. Here we will review progress on some aspects of the Brannetome, including Brainnetome atlas, Brainnetome-wise Association Studies (BWAS) of neurological and psychiatric diseases, such as schizophrenia and Alzheimer’s disease, and how the Brainnetome meets genome, and so on. It envisions that the Brainnetome will become an emerging co-frontier of brain imaging,

information technology, neurology and psychiatry. Some long-standing issues in neuropsychiatry may be solved by combining the Brainnetome with genome.


Tianzi Jiang is Professor of Brain Imaging and Cognitive Disorders, Institute Automation, Chinese Academy of Sciences, and Professor of Queensland Brain Institute, University of Queensland. He is the Chinese Director of the Sino-French Laboratory in Computer Science, Automation and Applied Mathematics (LIAMA), one National Center for International Research, since 2006. His research interests include neuroimaging, Brainnetome, imaging genetics, and their clinical applications in brain disorders and development. He is the author or co-author of over 170 reviewed journal papers in these fields and the co-editor of six issues of the Lecture Notes in Computer Sciences. He is Associate Editor of IEEE Transactions on Medical Imaging, IEEE Transactions on Autonomous Mental Development, Neuroscience Bulletin and an Academic Editor of PLoS One.


Intelligence Science Is The Road To Human-Level Artificial Intelligence

Zhongzhi Shi,Institute of Computing Technology, Chinese Academy of Sciences (ICT) .

Intelligence Science is an interdisciplinary subject which dedicates to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain, research on the principle and model of natural intelligence in molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. Research scientists coming from above three disciplines work together to explore new concept, new theory, new methodology. It will be successful and create a brilliant future in 21 century.

The long-term scientific goal of Artificial Intelligence is human-level artificial intelligence. Intelligence Science is the road to reach the long-term goal. The presentation will outline the framework of intelligence science and point out the big issues. Mind modeling is the core issue in intelligence science and a mind model CAM will be presented.


Zhongzhi Shi is a Professor at the Institute of Computing Technology, the Chinese Academy of Sciences, leading the Research Group of Intelligent Science. His research interests include intelligence science, machine learning, cognitive science and so on. Professor Shi has published 15 monographs and more than 400 research papers in journals and conferences. In 2006 he published the monograph “Intelligence Science”. The World Scientific Publishers publish the series on Intelligence Science in 2008. The International Journal of Intelligence Science is published by Scientific Research Publishing. He is the Editor-in-Chief. He is a fellow of CCF and CAAI, senior member of IEEE, ACM and AAAI member, a Chair for the WG 12.2 of IFIP.


This work was Supported by the National Program on Key Basic Research Project (973 Program) (No. 2013CB329502),National Natural Science Foundation of China (No. 61035003, 60933004).

Key Laboratory of Intelligent Information Processing
Institute of Computing Technology, Chinese Academy of Sciences