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Research Report 1996

Research Reports: A. Aertsen, D. Heck, S. Rotter

Dynamic Organization of Brain Activity - Mechanisms and Function


Team: Dipl.Phys. Markus Diesmann, Carsten Ebbinghaus, Dr. Uli Egert, Dipl. Phys. Marc-Oliver Gewaltig, Martin Nawrot

Prof. Ad Aertsen joined the Institute of Bology III in May 1996, coming fom the Weizmann Institute of Science (Israel). Dr. Stefan Rotter joined at the same time, coming from the Max-Planck-Institute for Developmental Biology in Tübingen. Dr. Detlef Heck came from the Washington University Medical School in St. Louis (USA) in October 1996. Together with our team, we are currently building the new Laboratory of Neurobiology and Biophysics.

1. Research Goals

Modern approaches to understand the mechanisms of higher brain function are increasingly concerned with neuronal dynamics. The task of organizing perception and behavior in a meaningful interaction with the external world prompts the brain to recruit its resources in a properly orchestrated manner. Contributions from many elements, ranging from individual nerve cells to entire brain areas, need to be coordinated in space and time.

Our principal research goal is to understand how this organization is dynamically brought about, and how coordinated activity of neurons is used by the brain. To this end, we study the spatio-temporal organization of brain activity at many different sites at a time. The rules that govern this organization and the underlying mechanisms are brought to light by complementary approaches of neurobiological experimentation, advanced data analysis, and mathematical modeling.

The long-term goals of our research are:

  • to uncover organizing principles and functional mechanisms of higher brain function

  • to capture these principles and mechanisms in formal theories and mathematical models

In combination with these scientific goals, we also pursue more applied goals:

  • to apply techniques and insights from brain research in the field of bio-medicine

  • to develop new methods for the representation and processing of information

To achieve these goals requires a multi-disciplinary approach, with contributions from biology, physics, mathematics, computer science, and also psychology and medical sciences. The research projects in our laboratory touch all these disciplines.

2. Outline of Research

Subject of our research is the structural and functional organization of the mammalian brain, with the aim to capture the rules governing this organization into quantitative models. The modeling is based on the characteristics of the brain's anatomy and physiology, the functional properties of which are explored by means of computer simulations and analytic calculations. The models should summarize our current knowledge, suggest predictions to be tested in new experiments, and enable a conceptual interpretation of the results. Thus, the research program aims at an integration and mutual inspiration of various system oriented approaches in experimental and theoretical neuroscience.

Our interest is focussed on the cerebral and the cerebellar cortex, and on their interactions with subcortical structures. Specific research projects are organized according to the following topics:

  1. Elucidation of the physiological mechanisms underlying the spatio-temporal organization of neuronal activity. Key issues are: neural coding, synchronization, cell assemblies, functional maps, dynamics and plasticity; with particular attention being paid to the mutual relations among these different levels of description.

  2. Characterization of the anatomical and functional architecture of these brain areas, with particular emphasis on their role in the dynamic organization of activity and synchronization.

  3. Assessment and formal analysis of biophysical processes and possible principles of information processing in the brain. This includes the development of quantitative models of higher brain function, and the design of new experimental paradigms.

  4. Implementation of the insights gained into new methods of neurobiological experimentation and the development of new tools for data analysis and information processing techniques, and their application in bio-medical sciences, computer science, and cognitive science.

3. Research Strategy

The composition of the research group and the strategies pursued follows from the above research concept and from further reasoning:

1. Brain Research and Theory

The brain is an enormously complex system. The total number of possible states in a network comprising 1011 neurons is exceedingly large. In fact, only a vanishing fraction of them will actually occur during a life-time. This complexity has important strategic implications, both for experimental and theoretical approaches to brain function. In such a system, we cannot expect that the underlying principles will simply pop out from merely observing the neuronal activity during an experiment. A formal theory is needed to work out testable predictions regarding the functioning of the system. These predictions, in turn, lead to the design of new experiments which can critically test the theory. At the same time, a theory of such a complex and only partially observable system must incorporate the relevant biological constraints. Otherwise, it runs the danger of degenerating into a sterile formal game.

Therefore, we consider a close interaction of experiment and theory as the optimal research strategy to make substantial progress in brain research.

2. Brain Theory, Neurobiology, and Computational Neuroscience

In view of our research goals, we expect the best results from a well-balanced combination of different approaches and methods. Ideas on possible principles of brain function may well be formulated on the basis of common sense. At the level of neuronal activity, however, this approach is often misleading, or even incorrect. A serious discourse on brain function only developed a few decades ago, after researchers attempted to abstractly define and actually mimick "intelligent behavior" in terms of models, and simulated these on electronic computers. This started a new process of scientific inquiry in areas like artificial intelligence, cognitive science, and computational neuroscience which, in turn, served as a rich source of inspiration for brain theoriticians. At the same time, insights and findings from brain research provided useful ideas for the understanding of complex artificial systems, as studied in dynamical systems theory. Thus, in our experience the conceptual connections between modern brain science and physics, mathematics, and computer science are the most exciting and fruitful ones.

Therefore, we expect that a strategy which decisively and competently combines methods and concepts from the above mentioned areas will prove successful.

4. Methods

A comprehensive knowledge base and a broad repertory of methods are the prerequisite for a successful realization of the above described multi-disciplinary research concept. This implies the necessity to establish and employ a rich palette of adequate modern research methods. From our main research themes and the scientific approaches adopted in their pursuit, we arrive at the following methods:

  1. Neurophysiology : electro-physiological (single and multiple micro-electrode recordings, extra- and intracellular) and opto-physiological (real-time optical imaging) recordings from neural activity in vitro and in vivo, combined with advanced computer-aided data analysis.

  2. Neuroanatomy : modern methods of descriptive and functional neuroanatomy; particularly micro-injection of suitable dyes in vitro and in vivo, computer-aided analysis of structural data from light- and electron-microscopy, and quantitative model-oriented neural network analysis.

  3. Neurotheory : the development and application of a formal theory of higher brain function takes a central position in our research program. It guides the design of experiments, the choice of data analysis tools, and the interpretation of results. Depending on their motivation, the models employed are either physical-mathematical or computational.

  4. Neurosimulation : in view of the complexity of the systems under study, theoretical analysis can only partially yield closed solutions. Therefore, large scale computer simulations of the dynamical processes in networks typically comprising several 103 neurons play an important role. These simulations vary in their degree of biological realism, both at the level of single nerve cells and at the level of network architecture. They range from detailed, biologically realistic models, via approaches inspired by statistical physics, to abstract models of formal systems. This new level of description ("in virtu" neurobiology) provides a link between hitherto separate areas of experimental and theoretical neuroscience.

5. Current Projects

  • analysis of spike correlations in multi-channel recordings from cortical areas in the awake behaving monkey (Rotter, Aertsen)

  • analysis of dynamical states in cortical population activity (data from real-time optical imaging and local field potential recordings) (Aertsen)

  • mechanisms of spike synchronization in cortical and cerebellar slices (Heck)

  • current balance and neuronal assemblies (Rotter)

  • neuronal network models of cortical synfire activity (Diesmann, Gewaltig)

  • analytic description of cortical synfire activity (Gewaltig, Diesmann)

  • current and voltage analysis of the neural firing threshold (Ebbinghaus)

  • firing rate estimation from single trial spike trains (Nawrot, Rotter)

  • spatio-temporal firing patterns in the cerebellar cortex (Heck)

  • SYNOD: development of software environment for simulation of biological neurons and networks (Diesmann, Gewaltig)

  • network analysis of microelectrode-array (MEA) recordings from slice preparations, and their application in pharmaceutical biotechnology (Egert, Aertsen)

Collaborations

  • Dept. Neurobiology, The Weizmann Institute of Science, Rehovot, Israel (Arieli, Grinvald)

  • Dept. Physiology, Hadassah Medical School and The Center for Neural Computation, Hebrew University Jerusalem, Israel (Abeles, Bergman, Grün, Vaadia)

  • Dept. Neuroscience, University of Pennsylvania, Philadelphia, USA (Gerstein, Keating)

  • Washington University Medical School, St. Louis, USA (Thatch)

  • Center for Cognitive Neuroscience, CNRS, Marseille, France (Riehle)

  • Dept. Anatomy and Neurobiology, University of Tennessee, Memphis, USA (Plenz)

  • AG Hirnforschung, Universität Freiburg (Krüger)

  • Institut für Neuroinformatik, Ruhr-Universität, Bochum (Von Seelen, Von der Malsburg)

  • Institut für Neuroinformatik, Universität Ulm (Palm)

  • Max-Planck-Institut für biologische Kybernetik, Tübingen (Antkowiak, Schüz, Braitenberg)

References

Papers

Aertsen A, Arndt M (1993) Response synchronization in the visual cortex. Current Opinion in Neurobiology 3: 586-594

Aertsen A, Erb M, Palm G (1994) Dynamics of functional coupling in the cerebral cortex: an attempt at a model-based interpretation. Physica D 75: 103-128

Aertsen A, Diesmann M, Gewaltig M-O (1996) Propagation of synchronous spiking activity in feedforward neural networks. J Physiol (Paris) 90: 243-247

Antkowiak B, Heck D (1997) Effects of the volatile anaesthetic enflurane on spontaneous discharge rate and GABAA-mediated inhibition of Purkinje cells in rat cerebellar slices. J Neurophysiol (in press)

Arieli A, Sterkin A, Grinvald A, Aertsen A (1996) Dynamics of ongoing activity: Explanation of the large variability in evoked cortical responses. Science 273:1868-1871

Bekkering H, Heck D, Sultan F (1996) What has to be learned in motor learning? Behav Brain Sci

Braitenberg V, Heck D, Sultan F (1997) The detection and generation of sequences as a key to cerebellar function. Experiments and theory. Behav Brain Sci (in press)

Braitenberg V, Heck D, Sultan F (1997) Waiting for the ultimate theory of the cerebellum. Behav Brain Sci (in press)

Heck D (1993) Rat cerebellar cortex in vitro responds specifically to moving stimuli. Neurosci Lett 157:95-98

Heck D (1995) Investigating dynamic aspects of brain function in slice preparations: Spatiotemporal stimulus patterns generated with an easy to build multi-electrode array. J Neurosci Meth 58:81-87

Heck D (1995) Sequential input to guinea pig cerebellar cortex in vitro strongly affects Purkinje cells via parallel fibers. Naturwissenschaften 82:201-203

Hellwig B, Schüz A, Aertsen A (1994) Synapses on axon collaterals of pyramidal cells are spaced at random intervals: A Golgi study in the mouse cerebral cortex. Biol Cybern 71: 1-12

Martignon L, von Hasseln H, Grün S, Aertsen A, Palm G (1995) Detecting higher-order interactions among the spiking events in a group of neurons. Biol Cybern 73: 69-81

Nisch W, Böck J, Egert U, Hämmerle H, Mohr A (1994) A thin film microelectrode array for monitoring extracellular neuronal activity in vitro. Biosens Bioelectron 9: 737-741

Plenz D, Aertsen A (1993) Current source density profiles of optical recording maps: A new approach to the analysis of spatio-temporal neural activity patterns. Europ J Neurosci 5: 437-448

Plenz D, Aertsen A (1996) Neural dynamics in cortex-striatum co-cultures I. Anatomy and electrophysiology of neuronal cell types. Neurosci 70: 861-891

Plenz D, Aertsen A (1996) Neural dynamics in cortex-striatum co-cultures. II. Spatiotemporal characteristics of neuronal activity. Neurosci 70: 893 - 924

Schlosshauer B, Stier H, Egert U (1993) Graded distribution of the neural 2A10 antigen in the developing chicken retina. Brain Res Dev Brain Res 76: 13-22

Shaw GL, Krüger J, Silverman DJ, Aertsen AMHJ, Aiple F, Liu H-C (1993) Rhythmic and patterned neuronal firing in visual cortex. Neurol Res 15: 46-50

Sultan F, Heck D, Bekkering H (1996) How to link the specificity of cerebellar anatomy to motor learning? Behav Brain Sci

Vaadia E, Haalman I, Abeles M, Bergman H, Prut Y, Slovin H, Aertsen A (1995) Dynamics of neuronal interactions in monkey cortex in relation to behavioral events. Nature 373: 515-518

Vaadia E, Aertsen A, Nelken I (1995) `Dynamics of neuronal interactions' cannot be explained by `neuronal transients'. Proc R Soc Lond B 261: 407-410

Books

Aertsen A (ed) (1993) Brain Theory: Spatio-Temporal Aspects of Brain Function. Amsterdam, New York: Elsevier Science Publ

Aertsen A, Braitenberg V (eds) (1996) Brain Theory: Biological Basis and Computational Principles. Amsterdam, New York: Elsevier Science Publ

Egert U (1995) Entwicklung und Erprobung eines Multielektroden-Ableitsystems auf der Basis eines photolithographisch hergestellten Mikroelektroden-Arrays, PhD Thesis, Universität Tübingen.

Heck D (1995) Die Bedeutung raum-zeitlicher Dynamik für die Aktivität des Kleinhirnkortex und die Interpretation seiner Anatomie. Hamburg: Verlag Dr. Kovac

Rotter S (1994) Wechselwirkende stochastische Punktprozesse als Modell für neuronale Aktivität im Neocortex der Säugetiere. Reihe Physik, Vol. 21, Frankfurt: Harri Deutsch.

Book Chapters

Abeles M, Prut Y, Bergman H, Vaadia E, Aertsen A (1993) Integration, synchronicity and periodicity. In: Brain Theory: Spatio-Temporal Aspects of Brain Function, pp 149-181. Aertsen A (ed). Amsterdam, New York: Elsevier Science Publ

Aertsen A, Diesmann M, Grün S, Arndt M, Gewaltig M-O (1995) Coupling dynamics and coincident spiking in cortical neural networks. In: Supercomputers in Brain Research: From Tomography to Neural Networks, pp 213-223. Herrmann H, Pöppel E, Wolf DW (eds). Singapore, World Scientific Publ

Aertsen A, Erb M, Palm G, Schüz A (1995) Coherent assembly dynamics in the cortex: Multi-neuron recordings, network simulations and anatomical considerations. In: Oscillatory Event-Related Brain Dynamics, pp 59-83. Pantev C, Elbert T, Lütkenhöner B (eds), New York: Plenum Press

Aertsen A, Erb M, Haalman I, Vaadia E (1996) Coherent dynamics in the frontal cortex of the behaving monkey - Experimental observations and model interpretation. In: Advances in Processing and Pattern Analysis of Biological Signals, pp 205-224. Gath I, Inbar GF (eds). New York, London, Plenum Press

Heck D (1993) Specific responses of the cerebellar cortex to moving stimuly. In: Brain Theory: Spatio-Temporal Aspects of Brain Function, pp 127-130. Aertsen A (ed). Amsterdam: Elsevier

Heck D (1996) The functional significance of cerebellar anatomy. Theory and Experiments. In: Neurobiology: Ionic Channels, Neurons, and the Brain. V Torre, F Conti (eds). Plenum Press

Heck D, Rotter S, Aertsen A (1993) Spike generation in cortical neurons: Probabilistic threshold function shows intrinsic and long-lasting dynamics. In: Brain Theory: Spatio-Temporal Aspects of Brain Function, pp 241-249. Aertsen A (ed). Amsterdam, New York: Elsevier Science Publ

Plenz D, Aertsen A (1994) The basal ganglia: minimal coherence detection on cortical activity distributions. In: The Basal Ganglia IV. New Ideas and Data on Structure and Function, pp 579-588. Percheron G, McKenzie JS, Féger J (eds), New York: Plenum Press

Riehle A, Seal J, Requin J, Grün S, Aertsen A (1995) Multi-electrode rcording of neuronal activity in the motor cortex: Evidence for changes in the functional coupling between neurons. In: Supercomputers in Brain Research: From Tomography to Neural Networks, pp 281-288. Herrmann H, Pöppel E, Wolf DW (eds). Singapore, World Scientific Publ

Rotter S (1996) Biophysical aspects of cortical networks. In: V Torre and F Conti (eds.) Neurobiology: Ionic Channels, Neurons, and the Brain, pp 355-369. New York: Plenum

Rotter S, Aertsen A, Vaadia E (1993) Neuronal interaction in the cortex - Quantitative characterization by cross-interval statistics. In: Brain Theory: Spatio-Temporal Aspects of Brain Function, pp 231-239. Aertsen A (ed). Amsterdam, New York: Elsevier Science Publ

Stern E, Aertsen A, Vaadia E, Hochstein S (1993) Stimulus encoding by multidimensional receptive fields in single cells and cell populations in V1 of awake monkey. In: Advances in Neural Information Processing Systems 5, pp 377-384. Hanson SJ, Cowan JD, Giles CL (eds). San Mateo (CA): Morgan Kaufmann Publ

Conference Proceedings

Aertsen A (1993) Dynamic coupling in cortical neural networks. In: Gielen C, Kappen B (eds) ICANN'93, pp 3-10. Proc Intern Conf Artificial Neural Networks, Amsterdam, The Netherlands. Heidelberg: Springer

Aertsen A, Vaadia E, Abeles M, Ahissar E, Bergman H, Karmon B, Lavner Y, Margalit E, Nelken I, Rotter S (1993) Dynamics of coherence in cortical neural activity: experimental observations and functional interpretations. In: Benhar O, Bosio C, Del Giudice P, Grandolfo M (eds) Neural Networks: from Biology to High Energy Physics. Int J of Neural Systems 3 (Suppl 1992), pp 105-114

Aertsen A (1994) Dynamical aspects of functional coupling in cortical neural networks. In: Proc Euromedecine 94; 1res Assises Européennes de Neurosciences: Le cerveau et le temps - Le temps du Fonctionnement, pp 538-540. S.N. Editel, Paris (France)

Aertsen A (1996) Dynamic organization of cortical activity. In: IWB `96, pp 23-26. Proc Internat Workshop on Brainware, Tokyo (Japan)

Arndt M, Erlhagen W, Aertsen A (1995) Propagation of synfire activity in cortical networks: a dynamical systems approach. In: Kappen B, Gielen C (eds) Neural Networks: Artificial Intelligence and Industrial Applications, pp 41-44. Proc Third Ann SNN-Symp on Neural Networks, Nijmegen, The Netherlands. Heidelberg: Springer

Diesmann M, Gewaltig M-O, Aertsen A (1996) Characterization of synfire activity by propagating `pulse packets'. In: Computational Neuroscience - Trends in Research 1995, pp 59-64. Bower J (ed). San Diego: Academic Press

Egert U, Nisch W, Hämmerle H (1996) Simultaneous recording of the electroretinogram and spike activity with a microelectrode array. In: Bridging Disciplines for Biomedicine. Proc 18th Ann Conf IEEE Engineering

Gewaltig M-O, Diesmann M, Aertsen A (1995) Propagation of synfire activity in cortical networks: a statistical approach. In: Kappen B, Gielen C (eds) Neural Networks: Artificial Intelligence and Industrial Applications, pp 37-40. Proc Third Ann SNN-Symp on Neural Networks, Nijmegen, The Netherlands. Heidelberg: Springer

Heck D (1996) Spatio-temporal patterns in mossy fiber activity and their possible role for the function of the cerebellum in motor control. In: Computational Neuroscience. Trends in Research 1995, pp 355-360. J. Bower (ed). SanDiego: Academic Press

Janders M, Egert U, Stelzle M, Nisch W (1996) Novel thin film titantium nitride micro-electrodes with excellent charge transfer capability for cell stimulation and sensing applications. In: Bridging Disciplines for Biomedicine. Proc 18th Ann Conf IEEE Engineering

Riehle A, Grün S, Aertsen A, Requin J (1996) Signatures of dynamic cell assemblies in monkey motor cortex. In: Artificial Neural Networks - ICANN96, pp 673-678. Von der Malsburg C, Von Seelen W, Vorbrüggen JC, Sendhoff B (eds). Berlin: Springer

Rotter S, Aertsen A (1995) A point process approach to cortical networks. In: Kappen B, Gielen C (eds) Neural Networks: Artificial Intelligence and Industrial Applications, pp 59-62. Proc Third Ann SNN-Symp on Neural Networks, Nijmegen, The Netherlands. Heidelberg: Springer

Rotter S, Aertsen A (1995) A point process approach to cortical networks. In: B Kappen and S Gielen (eds.) Neural Networks: Artificial Intelligence and Industrial Applications, pp 59-62. Berlin: Springer

Rotter S, Heck D, Aertsen A (1996) Spatio-temporal patterns of activity in cortical networks. In: Computational Neuroscience - Trends in Research 1995, pp 261-266. Bower J (ed). San Diego: Academic Press

Miscellaneous

Aertsen A (1995) An active brin is organized in time. In: Brain Research at the Weizmann Institute, p 2. The Weizmann Institute of Science, Rehovot (Israel)

Aertsen A et al (1996) Dynamic organization of cortical cell assemblies - Mechanisms and function. In: Life Sciences 96, p 202-203. The Weizmann Institute of Science, Rehovot (Israel)

Diesmann M, Gewaltig M-O, Aertsen A (1995) SYNOD: An Environment for Neural Systems Simulations - Language Interface and Tutorial. Technical Report GC-AA/95-3. The Grodetsky Center for Research of Higher Brain Functions, The Weizmann Institute of Science, Rehovot (Israel)

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