INTERNATIONAL WORKSHOP

ON

BRAIN-INSPIRED COMPUTING

 

Computational models, algorithms, applications, and implementations

 

 

 

CetraroItaly, July 8-11, 2013

 

 

 

 

Final Programme

 

 

 

Programme Committee

 

Thomas Lippert, Chair

(Germany)

Nicolai Petkov, Chair

(The Netherlands)

Lucio Grandinetti

(Italy)

Jack Dongarra

(U.S.A.)

 

 

 Sponsors

 

 

IBM

 

 

 

JUELICH SUPERCOMPUTING CENTER, Germany

 

 

ParTec Cluster Competence Centre

 

 

Intel

 

 

University of Calabria, Italy

 

 

 

 

Speakers

 

Katrin Amunts

Jülich Research Centre

GERMANY

 

George Azzopardi

TNO

NETHERLANDS

 

Frank Baetke

Global HPC Programs

Academia and Scientific Research

Hewlett Packard

Palo Alto, CA

USA

 

Antonio Bandera Rubio

University of Malaga

SPAIN

 

Ulrik Beierholm

University of Birmingham

School of Psychology

UNITED KINGDOM

 

Gyan Bhanot

Rutgers University

New Brunswick

NJ, U.S.A.

 

Michael Biehl

University of Groningen

NETHERLANDS

 

Youssry Botros

Intel

Aliso Viejo, CA, U.S.A.

 

Alessandro Curioni

Department Head, Mathematical & Computational Sciences

IBM Research Division - Zurich Research Laboratory

SWITZERLAND

 

Marcus Diesmann

Jülich Research Centre

GERMANY

 

Hugo Falter

ParTec Cluster Competence Center GmbH

Münich

GERMANY

 

Lucio Grandinetti

University of Calabria

ITALY

 

Sonja Grün

Jülich Research Centre

GERMANY

 

Bart ter Haar Romeny

Eindhoven University of Technology

Department of Biomedical Engineering

NETHERLANDS

 

Torsten Kuhlen

Center for Computing

GERMANY

 

Jesus Labarta

Barcelona Supercomputing Centre

SPAIN

 

Ales Leonardis

University of Birmingham

UNITED KINGDOM

 

Thomas Lippert

Institute for Advanced Simulation
Jülich Supercomputing Centre

and

University of Wuppertal, Computational Theoretical Physics,

and

John von Neumann Institute for Computing (NIC)

also

Europen PRACE IP Projects and of the DEEP Exascale Project

GERMANY

 

Rebeca Marfil Rubio

University of Malaga

SPAIN

 

Vicente Martin

DLSIIS- Facultad de Informatica, Univ. Politecnica

and

Centro de Supercomputacion y Visualizacion de Madrid

SPAIN

 

Karlheinz Meier

University of Heidelberg

GERMANY

 

Abigail Morrison

Jülich Research Centre

GERMANY

 

Murat Okandan

Microsystems Science and Technology

Sandia National Laboratories

U.S.A.

 

Luis Pastor

Polytechnical University of Madrid

SPAIN

 

Nicolai Petkov

Inst. Mathematics and Computer Science

University of Groningen

NETHERLANDS

 

Nick Ramsey

University of Utrecht

NETHERLANDS

 

Antonio Rodriguez Sanchez

University of Innsbruck

AUSTRIA

 

Thomas Schulthess

Supercomputing Center at Manno

SWITZERLAND

 

Felix Schürmann

Ecole Polytechnique Federale de Lausanne

SWITZERLAND

 

Thomas Sterling

Indiana University

U.S.A.

 

 

 

Workshop Agenda

Monday, July 8th

 

Session

Time

Speaker/Activity

 

9:00 – 9:15

Welcome Address

Session I

 

Brain Structure and Function - A Neuroscience Perspective

 

9:15 – 9:45

Katrin Amunts

Towards a multiscale and multimodal model of the human brain

 

9:45 – 10:15

NICK RAMSEY

Neuronal ensembles in human brain function

 

10:15 – 10:45

SONJA GRÜN

Data-driven evaluation of functional correlations in (massively) parallel spike trains

 

10:45 – 11:15

GYAN Bhanot

Modeling Emergent Behavior in Complex Dynamical Systems

 

11:15 – 11:45

COFFEE BREAK

 

11:45 – 12:15

markus diesmann

Integrating brain structure and dynamics with spiking neuronal network models

 

12:15 – 12:45

Abigail Morrison

Scaling up to brain-scale simulations with NEST

 

12:45 – 13:00

CONCLUDING REMARKS

Session II

 

Computational Models and Brain Inspired Computing I

 

17:00 – 17:30

Bart ter haAr Romeny

Vision for vision

 

17:30 – 18:00

Ales Leonardis

Combining compositional shape hierarchy and multi-class object taxonomy for efficient object categorisation

 

18:00 – 18:30

Antonio RODRIGUEZ Sanchez

2DSIL - A biologically plausible computational model of shape representation

 

18:30 – 19:00

COFFEE BREAK

 

19:00 – 19:30

Nicolai Petkov

Brain-Inspired Pattern Recognition

 

19:30 – 20:00

Antonio Bandera

Towards the development of cognitive robots

 

Tuesday, July 9th

 

Session

Time

Speaker/Activity

Session III

 

HPC and Visualization for Human Brain Simulations

 

9:00 – 9:30

Thomas Sterling

Brain Inspired Computing Structures, Scale, and Function

 

9:30 – 10:00

Jesus Labarta

t.b.a.

 

10:00 – 10:30

FRANK BAETKE

Towards Ultra-efficient Optical Interconnects for Brain Simulation Architectures

 

10:30 – 11:00

Torsten Kuhlen

Visual Analysis of Human Brain Simulations -- An Overview

 

11:00 – 11:30

COFFEE BREAK

 

11:30 – 12:00

Luis Pastor

Analyzing the brain: what makes visualization hard within the HBP

 

12:00 – 12:30

ALESSANDRO CURIONI

t.b.a.

 

12:30 – 13:00

Vicente Martin

Visualization towards the exascale in HBP

 

13:00 – 13:10

CONCLUDING REMARKS

Session IV

 

Computational Models and Brain Inspired Computing II

 

17:00 – 17:30

yOUSSRy BOTROS

Non-Boolean Brain-like unconventional computing

 

17:30 – 18.00

ulrik beierholm

Bayesian models of human multisensory perception

 

18:00 – 18:30

Ramesh Visvanathan

Cognitive Vision Architectures - Fusing Systems Engineering with Brain Science Insights

 

18:30 - 19:00

COFFEE BREAK

 

19:00 – 19:30

REBECCA MARFIL

Merging attention and segmentation: active foveal image representation

 

19:30 -20:00

Murat okandan

Neuro-inspired Computational Engines

 

20:00 – 20:10

CONCLUDING REMARKS

 

Wednesday, July 10th

 

Session

Time

Speaker/Activity

Session V

 

The Human Brain Project – A European Science Flagship

 

9:00 – 9:30

Karlheinz Meier

Computers like brains rather than brains on computers

 

9:30 – 10:00

FELIX SCHURMANN

Challenges and Opportunities for HPC in the Human Brain Project

 

10:00 – 10:30

Thomas Lippert

t.b.a.

 

10:30 – 11:00

Thomas Schulthess

Opportunities for new developments in supercomputing in the HBP

 

11:00 – 11:30

COFFEE BREAK

 

11:30 – 12:00

George Azzopardi

GOOSE: Search on internet of connected sensors

 

12:00 – 12:30

Concluding Remarks

Session VI

 

 

 

 

PANEL DISCUSSION

 

17:00 – 20:00

 

Brain Science and Computing - points of encounter and chances for synergy

Participants: K. Amunts , Y. Botros, T. Lippert, K. Meier, N. Petkov,

T. Schulthess, T. Sterling

 

 

Thursday, July 11th

 

Session

Time

Speaker/Activity

Session VII

 

TUTORIAL

 

 

Michael Biehl

Prototype-based learning and adaptive distances for classification

 

9:30– 10:30

part i

 

10:30 – 11:00

COFFEE BREAK

 

11:00 – 12:00

part ii

 

(t.b.a.: to be announced)

 

 

ABSTRACTS

Towards a multiscale and multimodal model of the human brain

 

Katrin Amunts

Jülich Research Centre, Germany

 

Reference brains are indispensable tools in human brain mapping enabling integration of multimodal data into an anatomically defined standard space. Cytoarchitectonic maps as combined part of the JuBrain atlas, for example, inform about cortical areas and nuclei, which are involved in a particular brain function as obtained in a functional imaging study. Available reference brains, however, are restricted to the macroscopical scale, and do not provide information on the functionally important microscopical dimension. They do not allow, e.g., to analyze findings on the level of cortical layers and sublayers.

We push the limits of current technology by creating the first ultra-high resolution 3D- model of the human brain at nearly cellular resolution of 20 microns, based on 7,404 histological sections. The total volume of this original histological data set was 1 TByte. Major challenges of this human brain model comprise - among others - the highly folded cerebral cortex, considerable inter-subject variability, and last but not least, the pure size of the brain with its nearly 100 billion nerve cells and the same number of glial cells. “BigBrain” is a freely available tool with unprecedented neuroanatomical insight. It allows extracting microscopic data for modeling and simulation. BigBrain enables testing of hypotheses on optimal path lengths between interconnected cortical regions, or spatial organization of genetic patterning, redefining the traditional neuroanatomy maps such as those of Brodmann and von Economo.

Back to Session I

Neuronal ensembles in human brain function

 

Nick Ramsey

University of Utrecht, Netherlands

 

With advances in imaging technologies for human research, the attainable level of detail is starting to reveal the cortical organization of brain functions. Thanks to the new 7 Tesla MRI scanners we can now image humans at sub-mm resolution in vivo, and new insights into the topographical representation of sensory and motor functions are starting to emerge. Patches of several 100,000 neurons (approximately 1 mm of cortex), also called functional units or neuronal ensembles, seem to perform specific functions together, suggesting that they form the building blocks of sensory and motor function.

However, the 7 Tesla MRI scanner measures bloodflow, a correlate of metabolism, and as such does not reveal any of the neuronal processes underlying cortical function. Hence we are now facing questions regarding the nature of the apparent coherent activity within neuronal ensembles, and how information is represented and transferred in terms of electrical and metabolic features. Addressing such questions is starting to become possible with intracranial electrical recordings in neurosurgery patients.

I will present some of the latest human functional imaging results obtained with MRI and intracranial electrical recordings, as well as potential clinical applications of recorded and decoded brain signals. I will argue that we are on the verge of being able to address the challenge of bridging what we know about single neuron dynamics  from animal research and modeling, to what we know about dynamics of functional units from neuroimaging and intracranial electrical recordings in humans. I will then discuss what is required and what is possible in terms of measurements in humans to address the challenge.

Back to Session I

Data-driven evaluation of functional correlations in (massively) parallel spike trains

 

Sonja Grün

(1) Institute of Neuroscience and Medicine (INM-6) and Institute for

Advanced Simulations (IAS-6) Forschungszentrum Jülich and JARA, Germany

(2) Theoretical Systems Neurobiology, RWTH Aachen Univ., Germany

 

Cell assemblies, defined as groups of neurons exhibiting precise spike coordination, were proposed as the units of network processing in the cortex (Hebb, 1949). Fortunately, in recent years considerable progress has been made in multi-electrode recordings, which enable the simultaneous recording of massively parallel spike trains of hundreds of neurons (e.g. Riehle et al, 2013).  However, statistical methods developed for the identification of significant spike patterns in a small number of parallel spike trains (e.g. Gr\"un et al, 1999; 2002a,b; 2003; 2009) do not scale to massively parallel spike data. Therefore, we develop statistical approaches coping with the massive parallelism (Berger et al, 2007, 2010; Louis et al, 2010; Staude et al, 2010a,b; Picado-Muino et al, 2013; Torre et al, subm). However, their application to experimental data is still a challenge due to considerable computational demands originating from a) large data sets and many recording sessions, the requirement to b) perform the analyses in a time resolved manner to follow the dynamics of neuronal processing and to c) evaluate the significance of identified patterns based on surrogate data and Monte Carlo approaches to account for the statistical properties of neuronal data and avoid false positives. In this talk I will outline the reasonings behind the above mentioned requirements and present first steps for deadling with the computational demands and the increased complexity of the analysis workflows.

Back to Session I

Modeling Emergent Behavior in Complex Dynamical Systems

 

Gyan Bhanot

Rutgers University

New Brunswick, NJ, U.S.A.

 

One of the outstanding problems in biology is to understand how complexity and robustness arise as emergent phenomena, driven by selection pressure in a world of interacting components. In this context, I will first discuss a scenario for how and why complex systems such the brain may have emerged in a world of RNA Replicators. Next, I will describe how emergent stable behavior can be understood by modeling the dynamics of the cell cycle and predator-prey systems. Finally, I will describe a genetic learning algorithm that may explain the topological properties of neural systems that learn to perform specific tasks.

Back to Session I

Integrating brain structure and dynamics with spiking neuronal

network models

 

Markus Diesmann

Jülich Research Centre, Germany

 

(1) Institute of Neuroscience and Medicine (INM-6) and Institute for

Advanced Simulations (IAS-6) Forschungszentrum J\"ulich and JARA,

Germany

(2) Medical Faculty, RWTH Aachen University, Germany

 

 

The cortical microcircuit, the network comprising a square millimetre of brain tissue, has been the subject of intense experimental and theoretical research. We recently achieved full-scale simulations of this circuit at cellular and synaptic resolution [1]: the model comprises about 100,000 neurons and one billion local synapses connecting them.

The purpose of the model is to investigate the effect of network structure on the observed activity. To this end we incorporate cell-type specific connectivity but use identical single neurons dynamics for all cell types. The emerging network activity exhibits a number of the fundamental properties found in nature: asynchronous irregular activity, layer specific spike rates, higher spike rates of inhibitory neurons as compared to excitatory neurons, and a characteristic response to transient input.  The importance of network structure is exemplified by the target specificity of feedback connections which led us to the hypothesis of a handshake mechanism between the cortical layers.

 

Despite these successes, the explanatory power of such local models is limited as half of the synapses of each excitatory nerve cell have non-local origins and at the level of areas the brain constitutes a recurrent network of networks. The model may be criticized as severly underconstrained. The second part of the talk therefore argues for the need of brain-scale models to arrive at self-consistent descriptions of the multi-scale architecture of the circuit. Such models will enable us to relate the microscopic activity to mesoscopic measures [2] and functional imaging data and to interpret those with respect to brain structure.

 

www.nest-initiative.org

www.csn.fz-juelich.de

 

[1] Potjans TC, Diesmann M (2012) Cerebral Cortex (online first)

    doi:10.1093/cercor/bhs358

[2] Linden H, Tetzlaff T, Potjans TC, Pettersen KH, Grün S, Diesmann M,

    Einevoll GT (2011) Neuron 72(5):859-872

Back to Session I

Scaling up to brain-scale simulations with NEST

 

Abigail Morrison

Jülich Research Centre, Germany

 

(1) Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulations (IAS-6) Forschungszentrum Jülich and JARA, Germany

(2) Institue for Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Germany

 

As the need for neuroscientific models integrating multiple brain regions at cellular resolution grows, the availability of corresponding high-performance simulation tools becomes ever more critical.  Scaling up the performance of the software to exploit modern supercomputers, while important, is only part of the story. In this talk, I will demonstrate the initiatives of the NEST project to scale up not only its development practices, but also its interaction with other neuroinformatics projects and the wider neuroscience community.

 

[1] Zaytsev YV, Morrison A (2013) Front Neuroinform 6:31

[2] Helias M, Kunkel S, Masumoto G, Igarashi J, Eppler JM, Ishii S, Fukai T, Morrison A, Diesmann M (2012) Front Neuroinform 6:26

[3] Kunkel S, Potjans TC, Eppler JM, Plesser HE, Morrison A, Diesmann M (2012) Front Neuroinform 5:35

 

www.nest-initiative.org

www.fz-juelich.de/bfsd

Back to Session I

Vision for vision

 

Bart Ter Haar Romeny

Eindhoven University of Technology

Department of Biomedical Engineering, Netherlands

 

We aim to substantially improve computer vision algorithms for image analysis in medical

imaging with an innovative Lie group model, inspired by optical findings in early vision functional circuits, and mathematical elegance. Current computer vision techniques often require significant application-specific tuning and are often not generic, while human vision is. Inspired by the multi-scale sampling in the visual front-end we first discuss a multi-scale differential geometry engine model for early vision, with a solid mathematical foundation, based on first principles and proper regularization properties of the operators. The pinwheel structure in the cortical columns have inspired us (Duits et al.) to develop a novel multi-orientation framework, which combines (partial and ordinary) differential equations on non-compact Lie groups(induced by stochastic processes and sub-Riemannian geometric control) with wavelet transforms. This leads to ’invertible orientation scores’, a higher-dimensional transform enabling powerful contextual analysis.

The multi-orientation analysis can be regarded a specific instance in a general higher dimensional Lie group theoretical model for early vision. We aim to develop invertible scores for multi-scale-and-orientation, multi-velocity, and multi-spatial frequency analysis, at each spatial position.A key challenge is to appropriately exploit these scores, their survey ofmultiple features per position, their underlying group structure, and their invertibility.

In the invertible scores adaptive processes can be exploited, such as geometry-driven diffusion with left-invariant evolutions for contextual enhancement, and improvements in curveextractions withleft-invariant sub-Riemannian optimal control. For these challenges a junior ERC grant has recently been awarded to R. Duits in our group.

In medical image analysis, this approach is exploited with excellent results on robust invariant features and shape extraction in computer-aided diagnosis (e.g. in virtual colonoscopy), on contextual enhancement of complex (i.e. highly curved, crossing) brain tractography from diffusion-weighted MRI(for safenavigation in medio-temporal epilepsy surgery), and on the tracking and analysis of complex retinal micro-vessels (in a large screening program in China for early diabetes detection).

These last applications prevent blindness, so ‘Vision for Vision’.

 

Short bio:

Prof. Bart M. ter Haar Romeny is professor at Eindhoven University of Technology in the Netherlands, heading the Biomedical Image Analysis (BMIA) group in the TU/e Department of Biomedical Engineering. MSc in Applied Physics from Delft University of Technology 1978 and PhD from Utrecht University in 1983. He was Head of Physics at the Utrecht University Hospital Radiology Department and associate professor at the Image Sciences Institute (ISI) of Utrecht University (1989-2001).

He is co-appointed professor at Maastricht University and Northeastern University (vice-dean research). His research interests focus on biologically inspired image analysis algorithms, multi-valued 3D visualization, especially brain connectivity and computer-aided diagnosis (in particular for diabetes), and image guided surgery, directed towards neurosurgery.

He is President of the Dutch Society for Pattern Recognition and Image Processing, and has been President of the Dutch Society for Biophysics & Biomedical Engineering (1998 – 2002) and the Dutch Society of Clinical Physics (NVKF, 1990-1992).

He initiated the ‘Scale-Space’ conference series in 1997 (now SSVM). He is reviewer for many journals and conferences, and organized several Summer Schools. He is an awarded teacher, and a frequent keynote lecturer. Prof. Romeny is Senior Member of IEEE, Board member of IAPR, Registered Clinical Physicist of NVKF, partner in the Chinese Brainnetome consortium, and Board member of the Dutch Foundation for Fundamental Research on Matter (FOM).

Back to Session II

Combining compositional shape hierarchy and multi-class object taxonomy for efficient object categorisation

 

Ales Leonardis

University of Birmingham, United Kingdom

 

Visual categorisation has been an area of intensive research in the vision community for several decades.

Ultimately, the goal is to efficiently detect and recognize an increasing number of object classes. The problem entangles three highly interconnected issues: the internal object representation, which should compactly capture the visual variability of objects and generalize well over each class; a means for learning the representation from a set of input images with as little supervision as possible; and an effective inference algorithm that robustly matches the object representation against the image and scales favorably with the number of objects. In this talk I will present our novel approach which combines a learned compositional hierarchy, representing (2D) shapes of multiple object classes, and a coarse-to-fine matching scheme that exploits a taxonomy of objects to perform efficient object detection.

Our framework for learning a hierarchical compositional shape vocabulary for representing multiple object classes takes simple contour fragments and learns their frequent spatial configurations. These are recursively combined into increasingly more complex and class-specific shape compositions, each exerting a high degree of shape variability. At the top-level of the vocabulary, the compositions represent the whole shapes of the objects. The vocabulary is learned layer after layer, by gradually increasing the size of the window of analysis and reducing the spatial resolution at which the shape configurations are learned. The lower layers are learned jointly on images of all classes, whereas the higher layers of the vocabulary are learned incrementally, by presenting the algorithm with one object class after another.

However, in order for recognition systems to scale to a larger number of object categories, and achieve running times logarithmic in the number of classes, building visual class taxonomies becomes necessary.

We propose an approach for speeding up recognition times of multi-class part-based object representations. The main idea is to construct a taxonomy of constellation models cascaded from coarse-to-fine resolution and use it in recognition with an efficient search strategy. The structure and the depth of the taxonomy is built automatically in a way that minimizes the number of expected computations during recognition by optimizing the cost-to-power ratio. The combination of the learned taxonomy with the compositional hierarchy of object shape achieves efficiency both with respect to the representation of the structure of objects and in terms of the number of modeled object classes. The experimental results show that the learned multi-class object representation achieves a detection performance comparable to the current state-of-the-art flat approaches with both faster inference and shorter training times.

Back to Session II

2DSIL - A biologically plausible computational model of shape representation

 

Antonio Sanchez

University of Innsbruck, Austria

 

That shape is important for perception has been known for almost a thousand years (thanks to Alhazen in 1083) and has been a subject of study ever since by scientists and phylosophers (such as Descartes, Helmholtz or the Gestalt psychologists). Shapes are important object descriptors. If there was any remote doubt regarding the importance of shape, recent experiments have shown that intermediate areas of primate visual cortex such as V2, V4 and TEO are involved in analyzing shape features such as corners and curvatures. The primate brain appears to perform a wide variety of complex tasks by means of simple operations. These operations are applied across several layers of neurons, representing increasingly complex, abstract intermediate processing stages. Recently, new models have attempted to emulate the human visual system. However, the role of intermediate representations in the visual cortex and their importance have not been adequately studied in computational modeling.

 

In this talk I propose a model of shape-selective neurons whose shape-selectivity is achieved through intermediate layers of visual representation not previously fully explored. I hypothesize that hypercomplex - also known as endstopped - neurons play a critical role to achieve shape selectivity and show how shape-selective neurons may be modeled by integrating endstopping and curvature computations. This model - a representational and computational system for the detection of 2-dimensional object silhouettes that we term 2DSIL - provides a highly accurate fit with neural data and replicates responses from neurons in area V4 with an average of 83% accuracy. I  successfully test a biologically plausible hypothesis on how to connect early representations based on Gabor or Difference of Gaussian filters and later representations closer to object categories without the need of a learning phase as in most recent models.

References:

Pasupathy, A. and Connor, C. (2002). Population coding of shape in area V4. Nature Neuroscience, 5(12):1332–1338.

Yamane, Y., Carlson, E., Bowman, K., Wang, Z., and Connor, C. (2008). A neural code for three-dimensional object shape in macaque inferotemporal cortex. Nature Neuroscience, 11(11):1352–1360.

Serre, T., Wolf, L., Bileschi, S., and Riesenhuber, M. (2007). Robust object recognition with cortex-like mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(3):411–426.

Rodríguez-Sánchez, AJ and Tsotsos, JK (2012), The roles of endstopped and curvature tuned computations in a hierarchical representation of 2D shape. PLoS ONE 7 (8), pp. 1–13.

Back to Session II

Brain-Inspired Pattern Recognition

 

Nicolai Petkov

Inst. Mathematics and Computer Science

University of Groningen, Netherlands

 

Insights into the function of the brain can provide clues for designing effective computer algorithms for pattern recognition. This thesis is illustrated by the design of feature detectors that is inspired by the properties of shape-selective neurons in area V4 of visual cortex.

Such a filter is trainable as it is configured by the automatic analysis of a feature specified by a user. Subsequently, it can detect features that are similar to the training feature. By means of training multiple such filters for different features of an object, we design effective feature vector representation of that object that is analogous to population coding in the brain. This approach shares two important aspects with pattern recognition by the brain: the ability to learn and deal with variability. It is illustrated by the detection of vascular bifurcations in medical images, traffic sign recognition in complex scenes, optical character recognition, word spotting, object recognition in a domestic environment and the automatic motif and repetition detection in music.

Back to Session II

Towards the development of cognitive robots

 

Antonio Bandera

University of Malaga, Spain

 

Cognitive neuroscience robotics integrates studies on robotic, cognitive science and brain science. The aim is to translate those models that explain the functionality of the human brain to the domain of social robots. In order to engage people in social interactions, social robots should be able to emanate responses at human interaction rates, and exhibit a pro-active behaviour. This pro-active behaviour implies that the internal architecture of these robots should not only be able to perceive and act. It should also be able to perform off-line reasoning. Cognition is the ability that allows us to internally deal with the information about the external world and, hence, this ability is subject to the existence of an internal representation of this information. Classical cognitive systems posit an inner realm richly populated with internal tokens that stand for external objects and states of affair. These internal representations, however, are not valid to generate predictions or reasoning. Recent work suggests that cognitive architectures cannot work on a passive, bottom-up fashion, simply waiting to be activated by external stimuli. Instead, these architectures must continuously use memory to interpret sensory information and predict the immediate future.

These predictions about the outer world can be used to actively drive the resources to relevant data in top-down modes of behaviour, allowing an efficient and accurate interpretation of the environment. This talk will describe two use cases where we are introducing the use of socially interactive robots. These robots build the deliberative planner over a mental model, a set of parameterised structures and procedures that constitute a virtual representation of the reality. This model should not be available only through interaction with the outer world, but it should be used as virtual reality. This allows us to test the situation postulated by Holland (2004): at the heart of the mechanism is not just the body in the environment, it is a model of the body in a model of the environment. This situation allows emanating predictions from the model, which can be correlated with real perceived information to drive attention, increase efficiency and filter noisy perception, while the contents of the mental model are also updated through experience.

Back to Session II

Brain Inspired Computing Structures, Scale, and Function

 

Thomas Sterling

Indiana University, U.S.A.

 

Without exaggeration, the brain may be the most complex system in the universe, and over the last half century or more has inspired thinkers about its structure, shear scale (89 billion neurons with 10s of thousands of synaptic junctions), and its functionality yielding the emergent behavioral properties of thought and consciousness. Neural nets and algorithms as well as artificial intelligence and cognitive science are among only a few of the many ways that researchers are motivated and guided by understanding of the brain. This presentation examines two issues inspired by brain form and function with practical implications for future real-world systems and their operations. The first objective discussed is to provide a lower bound on the resource requirements to achieve a key property associated with the brain: intelligence. The CRIS (Cognitive Real-time Interactive System) project at Indiana University (IU) is dedicated to the development of an abstract architecture that embodies many of the principal properties associated with intelligence and quantifying the time and physical components required to achieve this level of operation in real time. The second objective presented is to describe a simple cellular automata structure capable of implementation of irregular dynamic graph structures, the principal abstraction of intelligence. The CCA (Continuum Computer Architecture) project, also at IU is dedicated to the creation of the finest grain hardware components that in ensemble realize the emergent behavior of global general purpose parallel computing in ways reminiscent to the way neural structures accomplish autonomic intelligent operation but through virtualization of the interconnectivity via packet switching (rather than physical topologies). While both projects are in their inchoate phase, their respective goals are realizable and being undertaken. This talk will describe both and tie them back to intrinsic characteristics of the brain.

Back to Session III

 

 

Jesus Labarta

Barcelona Supercomputing Centre, Spain

 

 

Back to Session III

Visual Analysis of Human Brain Simulations -- An Overview

 

Torsten Kuhlen

Center for Computing, Germany

 

While in the past, visualization in Neuroscience focused mostly on medical imaging techniques and microscopic data, the development of tools for a visual analysis of simulated neuronal networks at scale is still in its infancy. My talk will give a brief overview of related work in this field and will then introduce some preliminary efforts resulting from a fruitful collaboration between RWTH Aachen University and the Institute of Neuroscience and Medicine at the Research Center Jülich. Finally, I will present an outlook on future work, particularly addressing the visualization requirements and challenges in the Human Brain Project. To this end, I will briefly discuss the potential of advanced methodology such as immersive Virtual Reality and multi-view techniques.

Back to Session III

Analyzing the brain: what makes visualization hard within the HBP

 

Luis Pastor

Polytechnical University of Madrid, Spain

 

The human brain is the most complex system Humanity has ever studied. The complexity of each of the elements which compose it, as well as that of the interconnections network that links them together is a barrier that has hampered advances in research, despite the huge effort performed by a large number of scientists during more than a century. The purpose of this talk is to dig a little bit into the problem, analyzing the specific features that make the visualization task difficult, and reviewing the main lines in which research is strongly needed, from different points of view.

Back to Session III

 

 

Alessandro Curioni

Department Head, Mathematical & Computational Sciences

IBM Research Division - Zurich Research Laboratory, Switzerland

 

 

Back to Session III

Visualization towards the exascale in HBP

 

Vicente Martin

DLSIIS- Facultad de Informatica, Univ. Politecnica and

Centro de Supercomputacion y Visualizacion de Madrid, Spain

 

A key goal of the HBP is to develop a capability for the analysis and visualisation of exascale data sets and for the steering of simulations. With this capability, supercomputers will act as an interactive scientific instrument, providing researchers with visual feedback and allowing them to control simulations while they are executing on a exascale computers.

 

A exascale computer will produce exabytes of data. It is not expected that we will be able to store such an enormous amount of data for later exploration. In fact, it is expected that the cost of memory to floating point operation will grow, a trend that will make necessary the in situ analysis of simulation data. In situ means essentially that the analysis is done on the data as it is generated, since making a copy to work on it in a completely asynchronous way is extremely costly. This would allow to steer the simulation in close to real time, turning a supercomputer in an interactive intrument able to probe the structure and behaviour of the human brain. However, to meet this challenge, current visualization paradigms have to be revisited and the supporting hardware and software designed accordingly. The talk will explore the challenges that have to be met to make this goal a reality.

 

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Non-Boolean Brain-like unconventional computing

 

Youssry Botros

Intel, Aliso Viejo, CA, U.S.A.

 

Synchronized oscillations in disparate regions of the brain have been observed in the visual cortex after a stimulus and it is hypothesized that this synchronization may be correlated with the association of memory fragments. This hypothesis is currently pursued by seeking to organize hierarchies of associative memories.  An approach to realizing this associative memory hierarchy is to utilize interacting nonlinear systems whose spontaneous synchronization corresponds to a minimum in the energy space which is achieved by minimizing the ‘distance’ between a memorized template and the test vectors; a point of maximum entropy in information space.  A 15.5 G pixel array has been tested and simulation results appear to offer signficant performance advantages (greater than 3 orders of magnitude) in speed, power, and area, relative to the state of the art micro-processor implementation.  An entropy-based associative memory implemented as coupled oscillators has been conceptualized and several types of nano-oscillators have been studied.  An important task ahead is to implement system prototypes and to identify classes of suitable applications for this unconventional method of computing.

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Bayesian models of human multisensory perception

 

Ulrik Beierholm

University of Birmingham, School of Psychology, United Kingdom

 

A human observer is constantly bombarded by a wealth of information from many sensory sources, visual, auditory, haptics etc.. Given this complexity it quickly becomes neccessary, and even advantageous, to parse this information and fuse together information from common sources. However infering the strucure underlying the sources is a complex problem akin to performing causal inference.

I will present Bayesian statistical models for performing such inference and will debate the experimental evidence for differentiating between them, with a focus on human behaviour.

Overall, evidence indicate that for perceptual tasks the human brain performs close to an optimal observer.

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Cognitive Vision Architectures - Fusing Systems Engineering with Brain Science Insights

 

Ramesh Visvanathan

Frankfurt Institute for Advanced Studies

Frankfurt, Germany

 

The Frankfurt Vision initiative focuses on the development of an integrated architecture for vision whose design is inspired by bringing together insights from neuroscience, cognitive science/psychology and computer vision systems.  From a systems perspective, we can view the human brain as an evolved system, with a flexible learning architecture designed by nature to solve a range of specific tasks in a class of environments that enhances the survival of humans.

Model-driven systems engineering is a discipline that formalizes application domain specification, i.e. task performance requirements and contextual models, and translates these specifications into system designs.  Systems engineering in the context of computer vision has its origins from the early 90’s and has been refined over the years through practice.  At a coarse-level the architectures inspired from systems engineering have parallels to models of brain function detailed in the brain and cognitive sciences.

Our architectural design involves massively parallel modules performing feed-forward decomposition of input visual signal into constituent modalities (e.g. color, motion, texture, shadow, reflection, contours, etc.) that allow for indexing into a rich memory structure. Generated hypotheses are then refined via a dynamic, recurrent process to converge to an interpretation. While both engineering and brain science views of the architecture are in agreement at this higher level, practical considerations present a multitude of options on module selection, learning and inference approaches, and memory representation schemes.  We present an overview of our ongoing efforts in the construction of the cognitive vision framework and discuss open research challenges.

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Merging attention and segmentation: active foveal image representation

 

Rebecca Marfil

University of Malaga, Spain

 

It is well established that the units of attention on human vision are not merely spatial but closely related to perceptual objects.

This implies a strong relationship between segmentation and attention processes. This interaction is bi-directional: if the segmentation process constraints attention, the way an image is segmented may depend on the specific question asked to an observer, i.e. what she 'attend' in this sense. When the focus of attention is deployed from one visual unit to another, the rest of the scene is perceived but at a lower resolution that the focused object. The result is a multi-resolution visual perception in which the fovea, a dimple on the central retina, provides the highest resolution vision. While much work has recently been focused on computational models for object-based attention, the design and development of multi-resolution structures that can segment the input image according to the focused perceptual unit is largely unexplored. This paper proposes a novel structure for multi-resolution image segmentation that extends the encoding provided by the Bounded Irregular Pyramid.

Bottom-up attention is enclosed in the same structure, allowing to set the fovea over the most salient image region. Preliminary results obtained from the segmentation of natural images show that the performance of the approach is good in terms of speed and accuracy.

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Neuro-inspired Computational Engines

 

Murat Okandan

Microsystems Science and Technology

Sandia National Laboratories, U.S.A.

 

For neural processing functionality, a new hardware approach, a new substrate is necessary. When deep neural approaches are implemented on conventional von Neumann/Turing architecture with precise, exact calculations only and large energy requirements for data movement, the overhead penalty becomes too large.  We are developing a new substrate utilizing novel optoelectronic and electronic devices, with the necessary functionality implemented at the lowest device level possible to perform pattern recognition, abstraction, prediction and model adjustment (learning), enabled by massive interconnectivity and reconfigurability (plasticity) driven by local rules.

Our proposed Grand Challenge Neuro-inspired Computational Engines (NiCE) project is aimed at demonstrating quantified performance improvement in specific applications using the approach and systems outlined above. The goal for the project is to show feasibility and benefits of using neuro-inspired approaches, while developing the scientific and technological foundation for this new hardware platform and information processing approach.

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Computers like brains rather than brains on computers

 

Karlheinz Meier

University of Heidelberg, Germany

 

The lecture will discuss the state-of-the-art of brain-inspired or neuromorphic computing systems. The importance of configurability in view of our limited knowledge of brain circuit architectures will be emphasized and technological solutions will be presented. The plans for neuromorphic computing in the Human Brain Project will be introduced with special emphasis on the integration with the HPC infrastructure in the project and the operational regimes of the two proposed large-scale neuromorphic systems.

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Challenges and Opportunities for HPC in the Human Brain Project

 

Felix Schurmann

Ecole Polytechnique Federale de Lausanne, Switzerland

 

A mouse brain is assumed have around 70 million and a human brain up to around 90 billion nerve cells. Each cell is a universe in itself, containing possibly billions of proteins. In terms of scales, the physics and chemistry involved for the functioning of the brain extends over 9 orders of magnitude of spatial scales and up to 18 orders of magnitude of time scales considering the lifespan of a human. Computationally speaking, modeling such a system represents a formidable weak scaling matching today's trend of parallelism in computing nicely yet requiring Moore's law to continue to work at least for the next decade and a shift towards interactive supercomputing. At the same time, it represents a strong scaling challenge, which poses algorithmic challenges and opens up the question of custom vs commodity in order to address the rate limiting steps in hardware. Lastly, the brain is an information processing device itself and thus might open up novel paths of computing altogether. The lecture will give a detailed overview of the systematic and architectural challenges of detailed brain simulations for High Performance Computing.

 

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Thomas Lippert

Institute for Advanced Simulation

Jülich Supercomputing Centre and

University of Wuppertal, Computational Theoretical Physics, and

John von Neumann Institute for Computing (NIC), also

Europen PRACE IP Projects and of the DEEP Exascale Project, Germany

 

 

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Opportunities for new developments in supercomputing in the HBP

 

Thomas Schulthess

Supercomputing Center at Manno, Switzerland

 

Supercomputers consume megawatts while the human brain runs at 30 watts. This comparison is often used to motivate human brain studies as inspirations for future computer systems development. But how do we move beyond the buzz? In this lecture I will try to get us back on the ground of reality, and analyze the real challenges for continued development of high-end computing. Hopefully this will lead to a discussion on how neuroscience might inspire future computing systems design.

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GOOSE: Search on internet of connected sensors

 

George Azzopardi

TNO, Netherlands

 

TNO is a non-profit research organization founded 80 years ago. The areas of expertise within TNO include technical sciences, behavioural and societal sciences, as well as earth, environmental and life sciences with applications in seven themes: a) healthy living, b) industrial innovation, c) defence, safety and security, d) energy, e) transport and mobility, f) built environment, and g) information society. Pattern recognition and machine learning  are active fields of research within TNO. One major project is called GOOSE (GOOgle for SEnsors) which has the ambition to provide the capability to search semantically for any relevant information within “all” (including imaging) sensor streams, in near real time,  in the entire internet of sensors. The concept is similar to the capability provided by currently available search engines which enable the retrieval of information on “all” pages on the internet.

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Brain Science and Computing - points of encounter and chances for synergy

 

N. Petkov, T. Schulthess, K. Meier, T. Sterling, K. Amunts

 

 

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Prototype-based learning and adaptive distances for classification

 

Michael Biehl

University of Groningen, Netherlands

 

This tutorial talk gives an introduction to prototype based systems in the context of supervised learning. The so called Learning Vector Quantization (LVQ) serves as a particularly intuitive framework, in which to discuss the basic ideas of distance or similarity based classification. Problems from the bio-medical domain will be presented in order to illustrate the concept.

An important issue is that of chosing an appropriate distance measure for the task at hand. The elegant framework of relevance learning aims at solving this problem means of adaptive distance measures. These are determined together with the prototypes in  the same training process. Example problems illustrate how relevance learning provides novel insight into the problem and how it can be  used in the context of feature selection.

Back to Session VII