Trends in European Computational Neuroscience


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Trends in European Computational Neuroscience

  1. 1. Trends in European Computational Neuroscience ROLF KÖTTER C. & O. Vogt Brain Research Institute and Institute of Morphological Endocrinology and Histochemistry, Heinrich Heine University, PO Box 101007, D-40001 Düsseldorf, Germany Running head: European computational neuroscience Key words: Structure-function relationships, neuroinformatics, computational neuroscience, neural information processing, theoretical neurobiology, European initiatives, Forum of European Neuroscience Address for correspondence: Dr. Rolf Kötter C. und O. Vogt-Institut für Hirnforschung Heinrich-Heine-Universität Universitätsstr. 1 D-40225 Düsseldorf, Germany Tel.: +49-211-8112095 Fax: +49-211-8112336
  2. 2. Synopsis Understanding the complex structure and function of the central nervous system requires the integration of many scientific disciplines and descriptive levels, and the development and application of adequate research tools. The fast growing field of computational neuroscience plays an important role within this integrative effort. In Europe, an active community is emerging that investigates structure- function relationships in the nervous system at subcellular, cellular, network, and behavioural levels. An increasing number of courses and conferences are offering opportunities for development and exchange of ideas between scientists interested in computational neuroscience. Thus, it is timely to present a spectrum of European activities in this field and to foster the communication between scientists across disciplines towards joint efforts.
  3. 3. Towards the end of the twentieth century the field of neuroscience has seen an unprecedented expansion. Vast amounts of data and many noticeable insights have been produced but we are still long ways from understanding how the brain works. What becomes clearer is that this understanding is not attained in a single step: like many other complex questions it has many different facets that need to be addressed in detail. In analogy to the 23 mathematical problems presented by David Hilbert in 1900 one can conceive a list of important and difficult, but accessible problems that neuroscientists should strive to solve ( Although one may argue about the relative importance of these various problems, decisive advancements on any item on this list would constitute a major step in the right direction. A common feature of these problems is that they address relationships between morphological, physiological and behavioural aspects of information processing in nervous systems ("structure-function relationships") as, for example, the synaptic mechanisms of information storage or the role of anterior cingulate gyrus in conscious decisions. This broad scope is a challenge since it requires interdisciplinary approaches across established neuroscientific disciplines, such as neuroanatomy, neurophysiology, neuropsychology, and clinical neuroscience. Besides horizontal links between disciplines most approaches require vertical cross-linking of different levels of description including molecular, cellular, network or organismic levels. Since each level has its own set of theories and methods it constitutes another challenge for neuroscientists to integrate these.
  4. 4. An increasing number of research publications in highly ranked neuroscientific journals show aspects of such horizontal and vertical cross-linking and underline the significance of integrated approaches to address important neuroscientific questions. It is important to keep these questions in mind and not to lose oneself when diving into the myriad details that need to be worked out before decisive answers are produced. How does integration across disciplines and across descriptive levels in neuroscience take place? First of all, neuroscientists themselves make this happen: anatomists have started to apply molecular approaches, electrophysiologists embrace cell staining and network reconstruction, neuropsychologists use pharmacological manipulations in functional imaging, and clinicians venture into a broad range of basic neuroscientific disciplines. Thus, the reality of research work carried out in the traditional disciplines is changing towards a broader spectrum of methods and co-operation. This tendency is reflected in an increasing proportion of funding and education available for co-operative projects in neuroscience. In addition, an increasing impact is made by scientists who have specialised into the investigation of aspects common to several neuroscientific disciplines and levels of description, such as principles of information processing and properties of non-linear systems. Their backgrounds range even further, extending to engineering, computer science or theoretical physics, and their levels of approach may be even more abstract, employing information theory or phase plane analysis to
  5. 5. neuronal and brain functions. Eventually, the complexity of the brain seems to be reflected in the complexity of expertise and methods employed to unravel its mechanisms. These encouraging developments bear their problems, too. While some departments or disciplines may fear to lose their identities or impact, others may re-duplicate techniques that are available at higher standards elsewhere. Different educational backgrounds may not only hamper interdisciplinary communication, but also lead to biased opinions about what constitutes an important neuroscientific problem or a valuable contribution towards its solution. In particular, it seems counterproductive to argue about the superior significance or authenticity of empirical versus theoretical approaches when both can provide important insights or even complement each other. Therefore, we need to develop a strong integrative neuroscientific culture by fostering exchange of ideas, co-operation between research groups, interdisciplinary education of students, and administrative integration of departments and disciplines. Computational neuroscience is a representative of such an integrative neuroscientific culture since it is located at the crossroads of all neuroscientific disciplines across all levels of description combining empirical and theoretical approaches. As the contributing disciplines and their subjects of study develop, it is constantly changing itself requiring adjustment of subjects, concepts and techniques. Thus, computational neuroscience is more adequately understood as
  6. 6. a mental attitude than a discipline. It may be instantiated administratively by research groups, departments, collaborative research centres or priority research programmes. Computational neuroscience necessarily has a broad scope and is open to interpretations. Attempts to define it have failed in the past: it is neither limited to understanding the computational aspects of neural processes, nor the application of computers to the study of the nervous system, nor the combination of the two, although both approaches are commonly mentioned and widely used /8/. For the same reason it is useless to draw sharp distinctions between computational neuroscience and similar concepts such as neuroinformatics, theoretical biology, analysis of neural systems or neural information processing. Such attempts serve mostly to emphasise a priority of certain contributing disciplines, to focus on arbitrary pet topics or to appeal to some supporting agencies. The focus of this Special Issue on computational neuroscience in Europe is such an artificial and arbitrary limitation. This limitation does in no way intend to claim a European priority or to depreciate contributions from other parts of the world. On the contrary, it responds to a conspicuous lagging behind of computational neuroscience in Europe compared to the United States where annual computational neuroscience conferences ( have seen an enormous growth since their modest start in 1992. In response to the growing attendance of European research workers it will be a big step forward for this meeting to take place for the
  7. 7. first time in Europe in Bruges in the year 2000. Similarly, the recent organisation of EU advanced courses in computational neuroscience (http://bbf-; /10/) follows a scheme that had been successful at the Cold Spring Harbor and Woods Hole Marine Biological Laboratories for many years. What is missing in Europe, however, is a visible community that actively advances the field through methodological workshops, integrated teaching and, most importantly, through intense communication and exchanges with other (in particular: experimental) neuroscientists. In connection with the Forum of European Neurosciences in Berlin 1998, which successfully displayed a fresh spirit in European neuroscience /9/, two important computational events took place. A satellite "Workshop on Neuroinformatics" was organised ( that facilitated communication between computational neuroscientists and formation of plans for European initiatives within the 5th framework programme 1998-2002 under point 9.2 of the work programme "brain theories, computational neuroscience and neuroinformatics". Such initiatives will hopefully allow for a continuation of the EU advanced courses and for the formation of a network of research groups that are committed to the advancement of methods, teaching and research in computational neuroscience.
  8. 8. The other event was an extended Special Workshop entitled "Spectrum of Computational Neuroscience in Europe" (http://www.hirn.uni- as part of the main Forum of European Neuroscience conference programme. The aims of this Special Workshop were to present to a general neuroscience audience the efforts made by an active and growing European community that investigates structure-function relationships of the nervous system using computational neuroscience approaches, and to foster the exchange of ideas between neuroscientists in different disciplines. The immediate outcome of the meeting and subsequent discussions was the intention to form a thematic network that would enhance the visibility of computational neuroscience in Europe and distribute information via the world wide web to all interested individuals and organisations. Further suggestions included the formation of sections on computational neuroscience within the national neuroscience associations and their European federation (FENS), and the organisation of regular meetings in association with the Forum of European neuroscience. This initiative was disseminated through computational mailing lists (e.g. Comp-Neuro: and led to the creation of the first comprehensive and informative web page on computational neuroscience in Europe ( In addition, the contributors to the Special Workshop were invited to prepare peer-reviewed full articles towards a Special Issue of the journal Reviews in the Neurosciences that would communicate current developments in European
  9. 9. computational neuroscience. As a result the present issue covers a wide range of work carried out in several European countries ranging from subcellular to global system levels and applying to a wide range of species. Péter Adorján, Christian Piepenbrock and Klaus Obermayer in Berlin contribute a model of “Contrast adaptation and infomax in visual cortical neurons” to explain how the visual system can adapt to different levels of stimulus contrast /1/. The proposed mechanisms constitute a bridge between abstract principles and basic physiological findings on the function of synapses and on the phenomenon of pattern adaptation. This work provides an important advancement after 20 years of research on pattern adaptation in the visual system. “Single cell and population activities in cortical-like systems” are the topic of Fülöp Bazsó, Ádám Kepecs, Máté Lengyel, Szabolcs Payrits, Krisztina Szalisznyó, László Zalányi and Péter Érdi in Budapest /2/. These authors raise the question of the appropriate levels of description for gaining some understanding of certain parts of the brain. They investigate aspects of neural information processing in the hippocampus and olfactory bulb using three differently detailed modelling approaches. From Plymouth comes the work of Roman Borisyuk, Michael Denham, Susan Denham and Frank Hoppensteadt on “Computational models of predictive and memory-related functions of the hippocampus” /3/. The first model presents a temporally asymmetric learning rule for the synaptic weights of the dentate gyrus - CA3 projection that fits empirical data of CA3 pyramidal cell activity during
  10. 10. prediction of sensory events. The second model investigates memorisation of sequences of sensory events as an effect of entorhinal and septal oscillatory inputs on a chain of interacting neural oscillators in CA3. Erik De Schutter at Antwerp has been “Using realistic models to study synaptic integration in cerebellar Purkinje cells” /4/. Illustrated by examples from this work he proposes a basic similarity of experimental and computational studies ("experiments in computo") which makes it possible to turn around the common approach of using computer models for hypothesis testing, and to build realistic neuronal models for generating testable hypotheses with considerable predictive power. Rolf Kötter and Dirk Schirok at Düsseldorf review advances “Towards an integration of biochemical and biophysical models of neuronal information processing: a case study in the nigro-striatal system” /5/. They note a surprising lack of subcellular models of information processing and demonstrate their technical feasibility and scientific promises. Extensions to combined biochemical and electrophysiological studies are discussed from the viewpoint of experimental and theoretical work on the role of dopamine- and calcium- mediated signals in the striatum. “Interaction of cortex and hippocampus in a model of amnesia and semantic dementia” is the topic of Jaap Murre from Amsterdam /6/. In an attempt to relate psychological functions to underlying brain mechanisms he describes a highly abstract network model inspired by certain features of these brain regions. Lesioning different parts of the network causes typical alterations of its dynamic
  11. 11. behaviour that resemble symptoms observed in some types of memory disorders. Inclusion of further details may help to validate the assumptions of the model and to extend its predictive value. A contribution from Trieste by Giulietta Pinato, Pietro Parodi, Alessandro Bisso, Domenico Macrì, Akio Kawana, Yasuhiko Jimbo and Vincent Torre investigates “Properties of the evoked spatio-temporal electrical activity in neuronal assemblies” /7/. This study raises the important question how reproducible information processing can be achieved in networks whose elements transmit information with low reliability. Based on analyses of stimulation-evoked responses in the leech ganglion and in neuronal cultures the authors conclude that averaging or distributing activity across many neurones can be used to decode the transmitted information faithfully in the presence of noise. “What can robots tell us about brains? A synthetic approach towards the study of learning and problem solving” is proposed by Thomas Voegtlin and Paul Verschure at Zürich /11/. The problem of indeterminacy that modellers have to face when attempting to validate integrative models has to be tackled by imposing additional constraints from multiple levels of description. The authors give examples from their work, which show that the implementation of robots provides rich behaviours that are capable of constraining such models from an additional macroscopic level. Besides demonstrating the wide range and common features of computational neuroscience the contributions to this issue identify areas that require further
  12. 12. development. These include the theoretical and practical advancement of computational neuroscience particularly in the directions of modelling subcellular biochemical and molecular processes, high-level phenomena as revealed by field potential recordings and functional imaging, or ontogenetic and phylogenetic developments. A culture and standards of computational modelling need to be developed that include justification of the chosen modelling approach, control of results by application of sophisticated statistical techniques and validation of generalisations by exploration of parameter space. Interactions between scientists who apply theoretical and experimental approaches need to be intensified to improve the quality and impact of research work. Multi-modal databases of experimental data and models need to be co-ordinated to facilitate cross-fertilisation and to enable integrative work towards a better understanding of how the brain works. Acknowledgment I wish to thank the organisers, advisors, and sponsors of the Forum of European Neuroscience 1998 and the representatives of Reviews in the Neurosciences for their substantial support.
  13. 13. References 1. Adorján P, Piepenbrock C, Obermayer K. Contrast adaptation and infomax in visual cortical neurons. Rev Neurosci 1999; this issue. 2. Bazsó F, Kepecs A, Lengyel M, Payrits S, Szalisznyó K, Zalányi L, Érdi P. Single cell and population activities in cortical-like systems. Rev Neurosci 1999; this issue. 3. Borisyuk R, Denham M, Denham S, Hoppensteadt F. Computational models of predictive and memory-related functions of the hippocampus. Rev Neurosci 1999; this issue. 4. De Schutter E. Using realistic models to study synaptic integration in cerebellar Purkinje cells. Rev Neurosci 1999; this issue. 5. Kötter R, Schirok D. Towards an integration of biochemical and biophysical models of neuronal information processing: a case study in the nigro-striatal system. Rev Neurosci 1999; this issue. 6. Murre JMJ. Interaction of cortex and hippocampus in a model of amnesia and semantic dementia. Rev Neurosci 1999; this issue. 7. Pinato G, Parodi P, Bisso A, Macrì D, Kawana A, Jimbo Y, Torre V. Properties of the evoked spatio-temporal electrical activity in neuronal assemblies. Rev Neurosci 1999; this issue. 8. Sejnowski TJ, Poggio TA. Series Foreword. In: Koch C, Segev I, eds. Methods in Neuronal Modeling. Cambridge: MIT, 1998; ix. 9. Singer W. Towards a European forum for the neurosciences. Trends Neurosci 1997; 20: 116-118.
  14. 14. 10.Ullman S, Roth A, Thomson A, Linne ML. Crete, channels, cells, circuits and computers. Trends Neurosci 1997; 20: 53-54. 11.Voegtlin T, Verschure PFMJ. What can robots tell us about brains? A synthetic approach towards the study of learning and problem solving. Rev Neurosci 1999; this issue.