Human Brain Project & Dutch Neuroscience: Shaping Collaborations

14 February 2020 | Amsterdam, Netherlands




Amsterdam by plane

Amsterdam Schiphol ist the primary international airport in the Netherlands and located about 20 km south-west of Amsterdam. The city can be reached in just half an hour by train, taxi or hotel shuttle. About 260 destinations worldwide are reachable from Schiphol.

Get to Amsterdam from the airport:

By bus:
The Connexxion Amsterdam Airport Express (bus number 397) departs every 15 minutes from bus stop B9 every 15 minutes and takes you directly to the city centre in about 30 minutes and costs €10.

By taxi:
You find the taxi rank at the airport’s exit. A ride to the city centre costs €40 - €60.

Don’t take rides from drivers within the airport as they are no approved taxi drivers.
à Info on authorised taxis

By hotel shuttle:
The Connexxion Schiphol Hotel Shuttle brings you to a large number of hotels in the airport area and Amsterdam city.

By train:
Schiphol train station connects you to Amsterdam Central Station (city centre), Amsterdam Zuid (World Trade Center) and Amsterdam RAI (conference centre). Train tickets are available in the airport’s main hall from yellow ticket machines. Here the Amsterdam Travel Ticket is recommended. It includes the return ride as well as free public transport in the city.

Regional airports:

Smaller airlines fly to regional airports like Rotterdam or Eindhoven, from these, Amsterdam is well reachable via public transport or taxi as well.


Amsterdam by train:

If arriving in Amsterdam by train, you most probably will arrive at the Central Station. If travelling from somewhere else in the Netherlands, also one oft he other 8 train station nearer your accommodation can be better suited.


Public transport in Amsterdam:

The public transport network in Amsterdam is well established. Metros, buses, trams and ferries get you around. To save money here you can travel with the I amsterdam City Card.


Pakhuis de Zwijger
Piet Heinkade 179
1019 HC Amsterdam, Niederlande



You can get to Pakhuis De Zwijger by Bus, Train, Tram or Metro. Lines and routes that have stops nearby are the following:

  • Bus 48, Station: Jan Schaeferbrug
  • Tram 26, Station: Kattenburgerstraat
  • Metro 51 – Amsterdam Centraal

Pakhuis De Zwijger is in 20 minutes walking distance to Amsterdams main station Amsterdam Centraal.

Please note that the information provided on this site has been obtained from several different sources and therefore the organisers cannot accept any responsibility for errors therein.

The Human Brain Project (HBP) is a large EU-funded project of the Future and Emerging Technologies (FET) Flagship program, employing scientists and engineers from more than 100 European universities, hospitals and research institutions. The project’s goal is to interconnect computer science, medicine and neuroscience to accelerate the understanding of the human brain and its diseases, and to harness that knowledge to the benefit of the society.

To achieve this, the project is building EBRAINS, the world’s first integrated ICT infrastructure for brain research and development, offering growing capabilities for neuroinformatics, brain simulation, medical informatics, neuromorphic computing and neurorobotics, underpinned by high-performance analytics and computing.  The development of EBRAINS is supported by involvement of neuroscientists active in HBP.

Briefly, this one-day event is organized as follows. First, HBP experts and leadership will present EBRAINS, and demonstrate the capacities of the underlying HBP platforms. They will present a range of opportunities for engagement of researchers in the project’s activities.  Second, these presentations will be coupled to talks by Dutch researchers who may be interested in using HBP facilities in the near future. The goals of the event are thus:

  • To make potential users aware of the HBP infrastructure, plus opportunities for grants and services to foster collaborations;
  • To stimulate interaction with HBP developers and researchers;
  • To promote growth of the Dutch research community using HBP facilities.

The event will be open for the wider research community and will take place at Pakhuis De Zwijger in Amsterdam, on 14 February 2020, and is organized by the University of Amsterdam in collaboration with the Medical University Innsbruck.








Katrin Amunts did a postdoctoral fellowship at the C. & O. Vogt Institute of Brain Research at Duesseldorf University, Germany. In 1999, she set up a new research unit for Brain Mapping at the Research Center Juelich, Germany. In 2004, she became professor for Structural-Functional Brain Mapping, and in 2008 a full professor at the Department of Psychiatry, Psychotherapy and Psychosomatics at the RWTH Aachen University as well as director of the Institute of Neuroscience and Medicine (INM-1) at the Research Center Juelich. Since 2013, she is a full professor for Brain Research, director of the C. and O. Vogt Institute of Brain Research, Heinrich-Heine University Duesseldorf and director of the Institute of Neuroscience and Medicine (INM-1), Research Center Juelich.

Katrin Amunts is a member of the editorial board of Brain Structure and Function. She is member of the German Ethics Council since 2012, and has been elected as vice chair in 2016. Katrin Amunts is the programme speaker of the programme Decoding the Human Brain of the Helmholtz Association, Germany. She is leading Subproject 2 Human Brain Organization of the European Flagship Project The Human Brain Project (HBP). In 2016, she has been elected as Scientific Research Director and Chair of the Science and Infrastructure Board (SIB) of the HBP. Since 2017 Katrin Amunts is co-speaker of the graduate school Max-Planck School of Cognition and since 2018 she is a member of the International Advisory Council Healthy Brains for Healthy Lives, Canada.

In order to better understand the organizational principles of the human brain, she and her team aim to develop a multi-level and multi-scale brain atlas, and use methods of high-performance computing to generate ultra-high resolution human brain models.


The Human Brain Project: New perspectives for brain sciences

Jan Bjaalie, M.D., Ph.D., is professor at the Institute of Basic Medical Sciences, University of Oslo, and Infrastructure Operations Director and leader of the Neuroinformatics Platform of the EU Human Brain Project. He was founding Executive Director of the International Neuroinformatics Coordinating Facility (INCF) and is currently head of the INCF Norwegian Node and member of the INCF Council for Training, Science, and Infrastructure. His research group has studied wiring patterns in the brain and developed data systems for organizing and managing heterogeneous neuroscience research data by use of a new generation of digital brain atlases. The group develops software and workflows for analysis of data integrated in the atlases (“Google maps of the brain”). Jan Bjaalie is Chief editor of Frontiers in Neuroinformatics and Section editor of Brain Structure and Function.


The EBRAINS infrastructure: integrated services adressing current and future challenges in brain research

Jan Bjaalie will show the perspectives on future use of EBRAINS and collaborations; talk about new funding opportunities and how this works for countries that are members of a distributed RI and overall, how and when you can make use EBRAINS services.

Gerard Borst studied biology and medicine at the University of Amsterdam, and received his Ph.D. from the Vrije Universiteit. Between 1993 and 1999 he worked on presynaptic release mechanisms at the Max Planck Institute for medical research in Heidelberg, Germany. After a 2-year stay at the University of Amsterdam, he moved in 2002 to the Erasmus MC Rotterdam. His lab works on the cellular mechanisms of hearing, with main topics development, sound localization and tinnitus.


How the brain computes where sound is coming from.

Sound localization at low frequencies utilizes the difference in arrival time between the ears. Neurons in the medial superior olive (MSO) are specialized in detecting this timing information by acting as coincidence detectors: their response is maximal when the excitatory inputs from the cochlear nuclei arrive simultaneously at the MSO. These excitatory inputs are segregated to different dendrites. I will present recordings from MSO neurons of the gerbil  plus modeling in which we studied how dendritic signals are integrated at the soma to compute sound location.

Dr. Tao Chen is a postdoc researcher in the NanoElectronics Group and Center for Brain-Inspired Nano Systems (BRAINS) at the University of Twente. Tao obtained his Ph.D. in Electronics Engineering from the University of Edinburgh in 2014. He worked in the Chinese Academy of Science as a postdoc (2015-2016) before joining the NanoElectronics group. Previously, Tao had research interests in integrated micro/nano systems based on two-dimensional materials. At the BRAINS center, Tao is exploring the physical properties of nano-materials to realize unconventional information processing hardware.


Evolving functionality in disordered nanosystems

We have recently exploited the nonlinear hopping conduction through an electrically tuneable network of boron dopants in silicon for parallel information processing. The dopant network demonstrates good separation properties in solving the canonical linearly inseparable exclusive OR (XOR) problem. The network can also perform four-input binary classification, i.e. filtering four-pixel features. By pre-processing basic features with the filters as in the brain, the overall accuracy of a linear classifier in classifying the MNIST (Modified National Institute of Standards and Technology) database of handwritten digits reaches up to 96.0%, surpassing state-of-the-art results realised in other materials-based computational systems. These results show that a nano-material system can be programmed for different functionalities, and establish a paradigm of silicon electronics for small-footprint and energy-efficient brain-like computation.

Steve Furber CBE FRS FREng is ICL Professor of Computer Engineering in the Department of Computer Science at the University of Manchester, UK. After completing a BA in mathematics and a PhD in aerodynamics at the University of Cambridge, UK, he spent the 1980s at Acorn Computers, where he was a principal designer of the BBC Microcomputer and the ARM 32-bit RISC microprocessor. Over 130 billion variants of the ARM processor have since been manufactured, powering much of the world's mobile and embedded computing. He moved to the ICL Chair at Manchester in 1990 where he leads research into asynchronous and low-power systems and, more recently, neural systems engineering, where the SpiNNaker project has delivered a computer incorporating a million ARM processors optimised for brain modelling applications.


The Neuromorphic Computing Platform

The Neuromorphic Computing Platform within the Human Brain Project incorporates two neuromorphic computing systems that represent the opposite ends of the scale of the trade-off between biological realism and ease of use. At one end, SpiNNaker is the world’s largest neuromorphic system, incorporating over a million ARM processors, offering the flexibility of software neural and synapse models with and biological real-time performance. At the other end of the scale, the BrainScaleS system offers physical emulation, wherein analogue electronic circuits closely emulate the behaviour of biological neurons and synapses, operating at wafer scale and at 10,000x the speed of the biology.

Inge Huitinga studied Medical Biology and obtained her PhD degree in 1992 on research of multiple sclerosis cum laude at the VUmc. In 1999 was appointed as  researcher at the KNAW. In 2006 she became director of the Netherlands Brain Bank (NBB). She professionalized the NBB and drafted a European Ethical Code of Conduct for Brain Banking. In 2012 she set up NHB-Psy, a national brain donation program for psychiatric diseases. April 2019 she was appointed professor in Neuroimmunology at the UvA.


The Netherlands Brain Bank

The Netherlands Brain Bank (NBB) provides well characterized human brain tissue from donors with neurological and psychiatric diseases and non-diseased controls open access to researchers worldwide. Annually, the NBB performs 130-160 autopsies with impressive short post mortem delays (mean 6.5 h). Since its start in1985, the NBB provided tissue from over 4400 autopsies. Currently, the NBB has over 5000 registered donors. In addition, the NBB brings clinical, pathological and genetic data together in a Neurogenetics D-base for analysis and to determine polygenic risk scores for brain pathologies and symptoms of brain diseases.

New concepts towards neuroscience integration using the HBP platform

Pattern formation in physics, biology and chemistry is based on dynamic principles of self-organization. Pattern formation phenomena in brain research are no exception and form the basis of our current understanding of cognitive brain functions. Perception and motor behavior emerge together with neuroelectrical and chemical activity, but so do epilepsy and neurodegenerative diseases. This pattern formation in the brain results from the interaction of billions of neurons over several time and space scales, but is typically measured in humans only on very large scales such as in magnetic resonance imaging or electroencephalography (EEG). In order to bridge the gap to clinical applications, it is therefore essential to model the traverse of scales using computer simulations and advanced mathematics, supported by individual state-of-the-art brain imaging. This combination allows to create autonomous brain models of individual patients and to test concrete clinical questions, possibly even to develop new therapies. Especially in epilepsy, these modern approaches are applied and enable the development of novel surgical interventions.  

Wouter Klijn completed an MSc in Artificial Intelligence from the University of Groningen, with a thesis on cortical micro-columns. He currently is software architect in the Simlab Neuroscience part of the Jülich Supercomputer Centre, Forschungzentrum Jülich, with a focus on in AI, real-time big data streaming systems and complex HPC processing pipelines. He is responsible for science and use case management in the HBP, and ICEI/FENIX. He is also creating the science and software infrastructure architecture for the HBP.


HPAC and FENIX: HPC platform for storage and computing in the HBP

With FENIX five European supercomputing centres, namely BSC (Spain), CEA (France), CINECA (Italy), CSCS (Switzerland) and JSC (Germany), have aligned to deliver as set of services based primarily on requirements of neuroscience use-cases. The distinguishing characteristic of this e-infrastructure is that data repositories and scalable supercomputing systems are in close proximity and well-integrated. The High Performance Analytics and Computing Platform develops and provides supercomputing, storage, visualization and simulation technology, aimed at neuroscience research of all kinds, to be deployed on HPC and the FENIX infrastructure. Requesting FENIX resources is done with small proposal, which is internally reviewed. Requesting HPC resources entails more effort but dedicated support teams are available.

Maarten H.P. Kole did his postdoctoral research at the Australian National University in Canberra (Australia). In 2011 he became group leader at the Netherlands Institute for Neurosciences (NIN–KNAW, Amsterdam) and in 2014 was appointed as endowed professor at the University of Utrecht (Cell Biology, Biophysics, and Neurobiology, Department of Biology). His research group investigates the specific biophysical properties of neuronal axons and myelinating glia at the single cell and subcellular resolution in normal and diseased brain. He received the A.W. Campbell Award (2010, Australian Neuroscience Society), an ERC starting grant (2011) and Vici grant from the Netherlands Organisation for Scientific Research (2018).


From grey to white; resolving action potentials at nanoscale resolution

One of the main challenges in understanding electrical communication within the neuronal circuits of the brain is their multi-cellular organization. Neurons are not operating in an empty space but maintain a close connection with glia cells that regulate excitability at diverse spatial and temporal scales. One notable example is the ensheathment of axons by myelin membranes. In this talk, I will discuss our recent approaches integrating optical and electrical voltage recordings with computer-generated simulations taking advantage of the NEURON supercomputer platform. The new insights into traveling potentials along myelinated axons represent an important step towards a computer representation of information flow in the brain’s grey and white matter.

Pieter Kubben, MD, PhD, is a neurosurgeon at Maastricht University Medical Center, The Netherlands, with specific expertise in computational neurosurgery. He developed NeuroMind, the #1 app for neurosurgery worldwide, and has research lines on adaptive deep brain stimulation and brain computer interfacing. For the past five years he has served as the medical programme manager eHealth for Maastricht UMC+ and last year he published the open-access


Perspectives on adaptive deep brain stimulation for Parkinson’s disease

Deep Brain Stimulation (DBS) is nowadays a widely used treatment for Parkinson’s disease. However, continuous stimulation independent of daily fluctuations in symptom severity is suboptimal. Using novel paradigms, adaptive of closed-loop DBS is becoming available. Pro’s and con’s will be discussed and put into perspective with other recent developments such as novel waveforms or directional current steering.

Michele Migliore is D.Phil. in Physics, Senior Scientist at the Institute of Biophysics of the Italian National Research Council, Visiting Professor of Cybernetics (Department of Mathematics and Informatics, University of Palermo), Visiting Professor of Computational Neuroscience (University of Rome "La Sapienza"), and Visiting Scientist at the Department of Neuroscience of the Yale University (USA). His lab is involved in modelling realistic neurons and networks, with the main long-term goal is to understand the emergence of higher brain functions and dysfunctions.


HBP Brain modeling and simulation workflows in the EBRAINS Platform: integrated tools to create and investigate models of the brain

The HBP Brain modeling and simulation workflows in the EBRAINS Platform will be illustrated. They will cover simulation engines for models at the molecular/subcellular, cellular, network and whole brain levels, and instruments (workflows, protocols and interfaces) for the integration of multiple simulators into multiscale/co-simulations, and will also include frameworks for analysis, validation, and visualization for users with different background and expertize. 

Yannick Morel studied Ocean Engineering at Florida Atlantic University (MS, 2002) and Mechanical Engineering at Virginia Tech (PhD, 2009). His background is in systems and control theory, with applications to unmanned vehicles and robotics. His research interests include information representation in dynamical systems and issues related to model uncertainty. His postdoctoral research work (in EPFL’s BioRob, 2010-2014) addressed motion control of swimming systems. He has worked on a number of application domains, including marine (CEA Tech, Thales) and defense technology (ISL).


Embodiment: Connecting neural models to function

Functional neural networks are outward-looking in nature; finality of the information processing conducted being interested in either acting upon one’s physical environment, or perceiving and understanding it. Expression of such functions is predicated upon this relationship, from network to physical reality, whose vehicle usually consists of a physical agent – the body. EBRAINS provides the tools to approach this notion of embodiment, connecting functional neural models to digital depictions of reality, or actual physical systems. They make it possible to approach model structure and function simultaneously, and offer novel perspectives in automation and AI technology, transcending limitations of deep learning approaches.

Prof. Dr. Cyriel Pennartz, head of the research group Cognitive and Systems Neuroscience at the Swammerdam Institute for Life Sciences (UvA), is coordinating subproject 3 (SP3) of the Human Brain Project: Systems and Cognitive Neuroscience. In this subproject, he and 15 other research groups within the EU are working to uncover the neural mechanisms underlying cognitive processes, such as sleep, memory and consciousness.


Welcome and Overview

As host of the event Prof. Dr. Cyriel Pennartz will give a short introduction and welcome the speakers and guests.

Tony Prescott is Professor of Cognitive Robotics at the University of Sheffield, UK, and the Director of Sheffield Robotics, a cross-disciplinary research institute with over 150 researchers. He is also the co-creator of the award-winning animal-like robots Scratchbot, Shrewbot and MiRo.  His background mixes psychology, philosophy and brain theory with robotics and artificial intelligence, and his research aims at answering questions about the human condition by creating synthetic entities with animal or human-like capacities such as perception, memory, emotion and sense of self. 


Brain-based robots: Understanding how the brain, body and environment interact to generate intelligent behavior

This talk will explore how the approach of “brain-based robotics”, building robots that controlled by computer models of the brain, is illuminating our understanding of human brains, minds and behaviour.  I will present examples from HBP research on animal and human-like robots including WhiskEye, a whiskered robot platform; MiRo, an animal-like robot for research, education and therapy; and iCub, a humanoid robot that we are using to investigate models of human memory and sense of self.

Nick Ramsey has a degree in Psychology and a PhD in Neuropsychopharmacology from the University of Utrecht. He became a specialist in cognitive neuroimaging in the US (NIH), and applies modern techniques, including fMRI and intracranial EEG, to questions on working memory, language and sensorimotor function. His primary goal is to acquire and translate neuroscientific insights to patients with neurological disorders, with a focus on implantable Brain-Computer Interfaces. He is professor in cognitive neuroscience at the Brain Center UMC Utrecht since 2007. He led the foundation of the international Brain-Computer Interface Society ( and was its president until 2019.


Neural ensemble activity and Brain-Computer Interface implants in Locked-In Syndrome

An increasing number of labs has provided severely paralyzed patients with Brain-Computer Interface implants for restoration of communication or movement. Most cases serve basic research questions, but some are now able to use their BCI for communication at home. The latter reveal new challenges and requirements through chronic use and data collection. I will discuss experiences with two chronically implanted users with Locked-in Syndrome and outline future directions.

Mavi Sanchez-Vives, MD PhD, is ICREA Research Professor and leader of Systems Neuroscience at the Institute of Biomedical Research August Pi Sunyer in Barcelona, Spain. She is also co-Director of the EVENT Lab (Experimental Virtual Environments in Neurosciences and Technology) at the University of Barcelona and one of the Founders of Virtual Bodyworks Inc. Her interests are cellular and network mechanisms for the generation of spontaneous brain emergent activity, brain interfacing and body representation.


From the experimental bench to the platforms: providing/using data and models

Individual inspiration and initiative often drives neuroscience forward. However, nowadays this does not need to be the only avenue forward. The novel storage and data analysis capabilities and computational modelling allow for the coordinated collaboration of numerous groups to advance towards the solution to large open questions about brain function. But how do experimentalists contribute and benefit under this novel frame? Even when the answer to this question is still challenging, we will discuss current and future possibilities that are emerging in HBP and how neuroscientists can participate.

Dr. Scholte is associate professor of cognitive neuroscience at the department of brain and cognition of the University of Amsterdam. He has a strong background in NeuroImaging, working on research topics ranging from low level perception to morbid curiosity. His main interest is in testing computational models of mid-level vision, and cognition in general, using brain data. 


Understanding scene segmentation using deep learning

Many theories of visual perception consider grouping and scene to be among the first computational operations in visual perception. By combining the analysis of neural and behavioral data, with the use of computational models, it is possible to probe which psychological phenomena emerge implicitly within the standard model of the visual system, and which phenomena are not accounted for. Here we show that scene segmentation can be resolved implicitly within the feedforward pass of visual processing, and that more complex scene segmentation can be resolved using recurrent processing, without any explicit operations.

Niels Taatgen is professor of cognitive modeling in the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence at the University of Groningen. He obtained a MSc in computer science and psychology at the University of Groningen, as well as a PhD on the interdisciplinary intersection between the two disciplines. After his PhD, he worked for six years at Carnegie Mellon University, working on models of skill acquisition, time perception and multitasking. In 2011, he was awarded an ERC starting grant for research on multitasking. Presently, he is chair of the Bernoulli Institute, and member of the program board of the Groningen Center for Cognitive Systems and Materials.


Towards a cognitive computer on the basis of neuromorphic hardware

Neuromorphic hardware promises to be the basis for a new kind of low-energy cognitive computer. To achieve this goal, it is not only necessary to develop new hardware, but also a new computer architecture with different levels of abstraction that reduce to one another. In my talk, I will sketch what such a multi-layer architecture could look out, and what the desirable properties are.

Betty Tijms is an associate professor at the Alzheimer center Amsterdam at the Amsterdam UMC, the Netherlands. She obtained a PhD in Neuroinformatics at the University in Edinburgh, Scotland. She is fascinated by the brain’s capability to learn and adjust itself in health and disease. To study such inter-individual differences in such processes, she invented methodology to measure grey matter networks using MRI in single patients. Her group sat the Alzheimer center focusses on studying the relationship of changes in brain networks, biomarker levels in cerebrospinal fluid and cognitive decline in Alzheimer's disease.


Heterogeneity in Alzheimer’s disease.

Alzheimer’s disease is the most common cause of dementia. Drug trials so far have been mostly disappointing. Individual patients show differences in terms terms of clinical symptoms, age of onset, disease progression, pathological markers and genetics, and so it is possible that treatments may need to target specific subgroups. In this talk I will give an overview of disease heterogeneity in Alzheimer’s disease based on with structural MRI and cerebrospinal fluid proteomics, and I will argue that combining different modalities will be key for finding new leads for treatment.

Sacha van Albada is Junior Professor in Computational Neuroanatomy at the Institute of Zoology at the University of Cologne, and leader of the Theoretical Neuroanatomy group at the Institute of Neuroscience and Medicine (INM-6) at Research Center Jülich, Germany. She obtained her MSc in theoretical physics from Utrecht University in the Netherlands and her PhD in computational neuroscience from the School of Physics at the University of Sydney in Australia, followed by postdoctoral work in Sydney and Jülich. In the HBP, she leads a task on large-scale modeling and simulation of primate cortex and is deputy leader of a cross-cutting project on visuomotor integration. In the next HBP phase, she will be deputy leader of the work package on cognitive architectures and lead a task on visuomotor architectures.  


Point neuron network modeling in the HBP

NEST is a tool developed in the HBP for point neuron network simulations on systems ranging from laptops to supercomputers. In this talk, I will provide a brief overview of the functionality of NEST, and illustrate its use and runtime scaling on a multi-area spiking network model of macaque cortex. Furthermore, I will briefly present NEST Desktop, an online tool for interactive point neuron network simulations.

Neurosurgeon Pepijn van den Munckhof specialised in deep brain stimulation (DBS) of the basal ganglia. Since 2009, he acted as a co-investigator on clinical trials studying subthalamic nucleus versus globus pallidus DBS in Parkinson's disease, nucleus accumbens DBS in obsessive-compulsive disorder, and internal capsule DBS in depression. Since 2012, he initiated nationwide collaboration in The Netherlands in order to study the effects of DBS of the central thalamus in disorders of consciousness/arousal.


Deep brain stimulation in disorders of consciousness/arousal

Unresponsive wakefulness syndrome, minimally conscious state and akinetic mutism are disorders of consciousness/arousal that are among the most dramatic conditions in medicine. There is currently no treatment for improvement or restoration of the level of consciousness/arousal. Central thalamic deep been stimulation (DBS) in both animals and human cases has been shown to improve attention and awareness. In the presention, the preliminary results of DBS targeted at the centromedian/ parafascicularis nucleus in a minimally conscious patient will be shown.

Frank van der Velde is a professor of Technical Cognition at the department of Cognitive Psychology & Ergonomics (CPE) of the University of Twente (UT), and is involved in the Digital Society Institute (DSI) and the Center for Brain-Inspired Nano Systems (BRAINS) at the UT. He has a background in Cognitive Psychology (PhD, MSc) and Physics (MSc). His research topics are focused on cognitive processing and neuro-computation of visual attention, working memory, language, concepts, and reasoning. He specifically aims to implement high-level cognitive and productive (combinatorial) processes in neural mechanisms based on neural ‘blackboard’ architectures.



Sentence structures as connection paths in the brain: A Neural Blackboard Architecture (NBA) of language (and cognition)

To understand the human brain, we need to understand how it implements the unique interaction between (gradually learned) forms of classification and ‘on line’ processing of arbitrary (novel) combinatorial structures, as found in language (e.g., sentences) and cognition (e.g., visual scenes). The NBA (current version) generates arbitrary (novel) sentence structures as neural connection paths that (temporarily) bind ’in situ’ neural word representations (e.g., ‘hub and spoke’ neural assemblies) in line with the relations they have in the sentence. The NBA is comprehensive at the sentence level (e.g., Cambridge Grammar, Huddleston and Pullum, 2002), including agreement, case, long-range dependencies and 'gaps'. The NBA uses a dependency parser (e.g., Google’s Parsey McParseface) for control of binding. The NBA models performance effects such as ambiguity resolutions, garden paths, parsing breakdowns, acceptable embeddings and (e.g., intracranial) brain activity. The NBA can be simulated with tools available on EBRAINS (e.g., MIIND).

Fleur Zeldenrust studied physics and neurobiology at the University of Amsterdam, and did a PhD in computational neuroscience at the same university, studying neural coding in bursting neurons and microcircuits. After a postdoc at the École Normale Supérieure in Paris, investigating models of predictive coding and information transfer, she returned to the Netherlands, where she set up a computational neuroscience track in the BSc Psychobiology in Amsterdam. In 2016, she started her own group with a Veni grant at the Donders Institute, Radboud University Nijmegen, studying the relation between physical properties and coding in neural systems.


Understanding information transfer in the brain: from single cell to network

The brain is a unique system, in that its dynamics have a clear function: making its owner respond to the world around it. In order to perform this function, the brain continuously processes information. How do the dynamics of neurons and networks result in information processing? The physical structure of the brain (its ‘hardware’) shapes this information processing and vice versa: the computations needed for information processing (the ‘software’) are adapted to the physical structure of the hardware. Here, I will discuss this relationship between information processing and neural properties on different levels, from single neurons to networks, and from different perspectives, from single cell electrophysiology to network modelling. In particular, I will focus on how neuromodulators such as dopamine influence the information processing and dynamics of single inhibitory and excitatory neurons, and how we can incorporate these effects in network models to ultimately understand how such networks produce behaviour.



Authors: Reinder Dorman1, M.Okun2, J.A.M. Lorteije1, C.M.A. Pennartz1

1Systems and Cognitive Neuroscience group, University of Amsterdam, 2Cortical Dynamics Lab, University of Leicester

Despite the enormous amount of neurons in the cortex, the dimensionality of firing patterns is confined to a lower dimensional space. Most neurons are constrained by the local circuit, and therefor can be said to act as an obedient member of a huge orchestra. However, some neurons deviate from this, acting more as a soloist. We assess this level of population coupling with the spike triggered population rate: a convolution of a single unit’s firing rate with the rest of the population. Here, we assess the level of population coupling between different areas; visual cortex, auditory cortex, perirhinal cortex and hippocampus in the awake rat. We see different dynamics within area versus between areas, and a small subset of units showing strong coupling between areas. On the population levels, no significance difference indicates any preferred coupling between different areas. Furthermore, there is no strong trend between internal coupling and external coupling. Lastly, small subsets of neurons show a wide range of coupling dynamics, likely indicative of behavioural state. 

Authors: J.P.N. Fiorilli1, T. Ruikes1, G. Huis in 't Veld1, M.J. Pearson2, P. Marchesi1, S.Dora1, C.M.A. Pennartz1

1Cognitive and Systems Neuroscience Group, SILS Center for Neuroscience, University of Amsterdam 2Bristol Robotics Laboratory, University of West of England, Bristol

Knowledge of our world is stored in our brains as rich, multimodal representations: When imagining an apple, one can think if its greenness, but also of its taste or its texture. A major outstanding question in the cognitive neurosciences is how different unimodal (sensory) object features get integrated into coherent, multimodal object representations. This is not trivial because the neural pathways that process this diverse sensory information are largely anatomically distinct. While the visual and tactile cortical systems may form their own, sensory-specific representations of objects as they are perceived and retrieved from memory, we hypothesize that medial temporal lobe structures such as perirhinal cortex, and the hippocampus, form more abstract object representations that can be accessed via multiple sensory modalities. To test this hypothesis, we are performing a series of parallel experiments. To gain empirical-based insights in cross-modal recognition mechanisms, we recorded activity of multiple single neurons in different areas across the cortico-hippocampal hierarchy (Barrel cortex, Visual cortex, Perirhinal cortex and Hippocampus) of rats performing a multisensory object recognition task. In parallel, we developed a deep predictive coding network which can be used to reproduce cross-modal recall. Lastly, we developed a robot model (the WhiskEye, with artificial Whiskers and Eyes) which is able to sample its environment using touch and vision, and to navigate in it. One of our future aims is to use the electrophysiological data to restrain the deep predictive coding network to make it more biologically plausible, and to test the model by using real-world physical visual and tactile features sampled by the WhiskEye robot. In the near-future, this multi-disciplinary approach could validate new neuroscientific hypothesis by testing them in models and inspire artificial intelligence to solve hard tasks by copying principles from biology.

Authors: Lisa Kirchberger1*, Sreedeep Mukherjee1*, Ulf H. Schnabel1*, Enny H. van Beest1*, Areg Barsegyan1, Christiaan N. Levelt2,3, J. Alexander Heimel4, Jeannette A. M. Lorteije5, Chris van der Togt1, Matthew W. Self1 & Pieter R. Roelfsema1,6,7

1Department of Vision & Cognition, Netherlands Institute for Neuroscience, Amsterdam  2Molecular Visual Plasticity Group, Netherlands Institute for Neuroscience, Amsterdam  3Department of Molecular and Cellular Neuroscience, CNCR, VU University, Amsterdam 4Cortical Structure & Function Group, Netherlands Institute for Neuroscience, Amsterdam  5Cognitive and Systems Neuroscience Group, University of Amsterdam 6Department of Integrative Neurophysiology, CNCR, VU University, Amsterdam 7Department of Psychiatry, Academic Medical Center, Amsterdam
* These authors contributed equally

The segregation of figures from the background is an important step in generating a visual percept. In primary visual cortex (V1), figures evoke stronger neural firing than backgrounds but it is unknown how this figure-ground modulation (FGM) arises and whether it is necessary for perception. Here we show, using optogenetic silencing in mice, that FGM in V1 is necessary for figure-ground segregation. We find that the source of this enhanced activity is feedback from cortical higher visual areas. Neurons in higher areas also exhibit FGM and optogenetic silencing of higher areas strongly reduced FGM in V1. In V1, figures enhanced the activity of vasoactive intestinal peptide-expressing interneurons and suppressed somatostatin-positive interneurons, causing an increased activation of the cortical column. Our results provide new insight in how lower and higher areas of the visual cortex interact with each other to shape visual perception. 

Author: Alexander Kronera,b, Mario Sendena,b, Kurt Driessensc, Rainer Goebela,b,d

a Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands b Maastricht Brain Imaging Centre, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands c Department of Data Science and Knowledge Engineering, Faculty of Science and Engineering, Maastricht University, Maastricht, The Netherlands d Department of Neuroimaging and Neuromodeling, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences (KNAW), Amsterdam, The Netherlands

Predicting salient regions in natural images requires the detection of objects that are present in a scene. To develop robust representations for this challenging task, high-level visual features at multiple spatial scales must be extracted and augmented with contextual information. However, existing models aimed at explaining human fixation maps do not incorporate such a mechanism explicitly. Here we propose an approach based on a convolutional neural network pre-trained on a large-scale image classification task. The architecture forms an encoder-decoder structure and includes a module with multiple convolutional layers at different dilation rates to capture multi-scale features in parallel. Moreover, we combine the resulting representations with global scene information for accurately predicting visual saliency. The network is based on a lightweight image classification backbone and hence presents a suitable choice for applications with limited computational resources to estimate human fixations across complex natural scenes.

Authors: Nestor Timonidis, Rembrandt Bakker and Paul Tiesinga

Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-type specificity would be a major step forward. We analysed the ability of gene expression patterns to predict cell-type and laminar specific projection patterns and analyzed the biological context of the most predictive groups of genes. To achieve our goal, we used publicly available volumetric gene expression and connectivity data and pre-processed it for prediction by averaging across brain regions, imputing missing values and rescaling. Afterwards, we predicted the strength of axonal projections and their binary form using expression patterns of individual genes and co-expression patterns of spatial gene modules. For predicting projection strength, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r2 score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using the dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r2 score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction pipeline that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.


Cyriel Pennartz | University of Amsterdam 



Hanna Bodde | University of Amsterdam 
Angelica da Silva Lantyer | University of Amsterdam 
Tina Kokan | Medical University Innsbruck



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