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Neuroinformatics

, Volume 1, Issue 1, pp 3–42 | Cite as

From biophysics to behavior

Catacomb2 and the design of biologically-plausible models for spatial navigation
  • Robert C. Cannon
  • Michael E. Hasselmo
  • Randal A. Koene
Original Article

Abstract

A variety of approaches are available for using computational models to help understand neural processes over many levels of description, from sub-cellular processes to behavior. Alongside purely deductive bottom-up or top-down modeling, a systems design strategy has the advantage of providing a clear goal for the behavior of a complex model. The order in which biological details are added is dictated by functional requirements in terms of the tasks that the model should perform. Ideas from engineering can be mixed with those from biology to build systems in which some constituents are modeled in detail using biologically-realistic components, while others are implemented directly in software. This allows the areas of most interest to be studied within the context of a behaving system in which each component is constrained both by the biology it is intended to represent as well as the task it is required to perform within the system. The Catacomb2 modeling package has been developed to allow rapid and flexible design and study of complex multi-level systems ranging in scale from ion channels to whole animal behavior. The methodology, internal architecture, and capabilities of the system are described.

Its use is illustrated by a modeling case study in which hypotheses about how parahippocampal and hippocampal structures may be involved in spatial navigation tasks are implemented in a model of a virtual rat navigating through a virtual environment in search of a food reward. The model incorporates theta oscillations to separate encoding from retrieval and yields testable predictions about the phase relations of spiking activity to theta oscillations in different parts of the hippocampal formation at various stages of the behavioral task.

Index Entries

Computational neuroscience simulation software modeling spatial navigation hippocampus theta rhythm 
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References

  1. Alonso, A. and Klink, R. (1993) Differential electroresponsiveness of stellate and pyramidal-like cells of medial entorhinal cortex layer II. J Neurophysiol 70:128–143.PubMedGoogle Scholar
  2. Barnes, C. A., McNaughton, B. L., Mizumori, S., Leonard, B. W., and Lin, L. H. (1990) Comparison of spatial and temporal characteristics of neuronal activity in sequential stages of hippocampal processing. Prog Brain Res 83:287–300.PubMedCrossRefGoogle Scholar
  3. Beeman, D., Bower, J. M., De Schutter, E., Efthimiadis, E. N., Goddard, N., and Leigh, J. (1997) The GENESIS simulator-based neuronal database. In: Neuroinformatics: An Overview of the Human Brain Project. Chapter 4. (Koslow, S. H. and Huerta, M. F., eds.) Lawrence Erlbaum Associates, Mahwah, NJ.Google Scholar
  4. Bi, G. Q. and Poo, M. M. (1998) Synaptic modifiction in cultured hippocampal neurons: dependence on spike timing, synaptic strength and postsynaptic cell type. J Neurosci 18(24):10,464–10,472.Google Scholar
  5. Borg-Graham, L. (1999) Interpretations of data and mechanisms for hippocampal pyramidal cell models. In: Cerebral Cortex Vol. 13—Cortical Models, (Jones, E., Ulinski, P., and Peters, A., eds.) Plenum Publishing Corporation. pp. 19–138.Google Scholar
  6. Borg-Graham, L. (2001) The surf-hippo neuron simulation system, v3.0. (http://www.cnrsgif.fr/iaf/iaf9/surf-hippo.html).Google Scholar
  7. Bower, J. M. and Beeman, D. (1994) The Book of GENESIS. Teleos Publishing, Los Angeles, CAGoogle Scholar
  8. Burgess, N. and O’Keefe, J. (1996) Neuronal computing underlying the firing of place cells and their role in navigation. Hippocampus 6(6):749–762.PubMedCrossRefGoogle Scholar
  9. Cannon, R. C. (2001a) CD-ROM. Computational Neuroscience-Realistic Modelling for Experimentalists. (De Schutter, E., ed.) CRC Press, Boca-Raton, FL.Google Scholar
  10. Cannon, R. C. (2001b) Eggleton ’71 revisited. In: ASP Conf. Ser. 229: Evolution of Binary and Multiple Star Systems, pp. 15+.Google Scholar
  11. Cannon, R. C., Howell, F. W., Goddard, N., and De Schutter, E. (2002) Non-curated distributed databases for experimental data and models in neuroscience. Network, in press.Google Scholar
  12. Cannon, R. C., Turner, D. A., Papyali, G., and Wheal, H. V. (1998) An on-line archive of reconstructed hippocampal neurons. Journal of Neuroscience Methods 84(1–2):49–54.PubMedCrossRefGoogle Scholar
  13. Cornelis, H. and De Schutter, E. (2003) NeuroSpaces: New approaches in neuronal modeling software. Neurocomputing, in press.Google Scholar
  14. Eggleton, P. P. (1971) The evolution of low mass stars. Monthly Notices of the Royal Astronomical Society 151:351.Google Scholar
  15. Faulkner, D. J. (1968) The evolution of helium shell-burning stars. Monthly Notices of the Royal Astronomical Society 140:223.Google Scholar
  16. Forss, J., Beeman, D., Bower, J. M., and Eichler-West, R. (1999) The Modeler’s Workspace: a distributed digital library for neuroscience. Future Generation Computer Systems 16:111–121.CrossRefGoogle Scholar
  17. Fox, P. A., Hall, A. D., and Schryer, N. L. (1978) The PORT mathematical subroutine library. ACM Trans Math Software 4:104–126.CrossRefGoogle Scholar
  18. Frank, L. M., Brown, E. N., and Wilson, M. (2000) Trajectory encoding in the hippocampus and entorhinal cortex. Neuron 27(1):168–178.CrossRefGoogle Scholar
  19. Fransen, A., Alonso, A., and Hasselmo, A. E. (2002) Simulation of the role of the muscarinic-activated calcium-sensitive non-specific cataion current I(NCM) in entorhinal neuronal activity during delayed matching tasks. J Neurosci 22(3):1081–1097.PubMedGoogle Scholar
  20. Funge, J. D. (1999) AI for Computer Games and Animation: A Cognitive Modeling Approach. J D Peters.Google Scholar
  21. Gamma, E., Horn, R., Johnson, R., and Vlissides, J. (1995) Design Patterns. Elements of Reusable Object-Oriented Software. Addison-Wesley.Google Scholar
  22. Goddard, N., Hood, G., Howell, F., Hines, M., and De Schutter, E. (2001a) NEOSIM: Portable largescale plug and play modelling. Neurocomputing 38:1657–1661.CrossRefGoogle Scholar
  23. Goddard, N. H., Hucka, M., Howell, F., Cornelis, H., Shankar, K., and Beeman, D. (2001b) Towards NeuroML: model description methods for collaborative modelling in neuroscience. Philos Trans R Soc Lond B Biol Sci 29(352):1209–1228.Google Scholar
  24. Hagan, J. J., Verheijck, E. E., Spigt, M. H., and Ruigt, G. S. (1992) Behavioral and electrophysiological studies of entorhinal cortex lesions in the rat. Physiol Behav 51(2):155–266.CrossRefGoogle Scholar
  25. Hasselmo, M. E., Bodelon, C., and Wyble, B. P. (2002a) A proposed function for hippocampal theta rhythmn: Separate phases of encoding and retrieval enhance reversal of prior learning. Neural Computation 14(4):792–812.CrossRefGoogle Scholar
  26. Hasselmo, M. E., Fransen, E., Dickson, C. T., and Alonso, A. A. (2000) Computational modeling of entorhinal cortex. Annals NY Acad Sci 911:418–446.CrossRefGoogle Scholar
  27. Hasselmo, M. E., Wyble, B. P., and Cannon, R. C. (2002b) From spike frequency to free recall: How neural circuits perform encoding and retrieval. The Cognitive Neuroscience of Memory: Encoding and Retrieval (Wilding, E., Parler, A., and Busey, T. J., eds) Psychology Press.Google Scholar
  28. Hines, M. (1984) Efficient computation of branched nerve equations. Int J Bio Med Comput 15:69–76.CrossRefGoogle Scholar
  29. Hines, M. L. and Carnevale, N. T. (2001) NEURON: a tool for neuroscientists. The Neuroscientist 7:123–135.PubMedCrossRefGoogle Scholar
  30. Holscher, C., Anwyl, R., and Rowan, M. J. (1997) Stimulation on the positive phase of hippocampal theta rhythm induces long-term potentiation that can be depotentiated by stimulation on the negative phase in area CA1 in vivo. J Neurosci 17(16):6470–6477.PubMedGoogle Scholar
  31. Hucka, M., Finney, A., Sauro, H., and Bolouri, H. (2001) Systems Biology Markup Language (SBML) Level 1: Structures and Facilities for Basic Model Definitions. (http://www.cds.caltech.edu/erato/sbml/).Google Scholar
  32. Kali, S. and Dayan, P. (2000) The involvement of recurrent connections in area CA3 in establishing the properties of place fields: a model. J Neurosci 20 (19):7463–7477.PubMedGoogle Scholar
  33. Klink, R. and Alonso, A. (1997) Muscarinic modulation of oscillatory and repetitive firing properties of entorhinal cortex layer II neurons. J Neurphysiol 77:813–1828.Google Scholar
  34. Kotter, R., Nielse, P., Dyhrfjeld-Johnsen, J., Sommer, F. T., and Northoff, G. (2002) Multilevel neuron and network modeling in computational neuroanatomy. In: Computational Neuroanatomy: Principles and Methods. (Ascoli, G., ed.) Humana Press, Totowa, NJ.Google Scholar
  35. Lattanzio, J. C. (1986) The asymptotic giant branch evolution of 1.0–3.0 solar mass stars as a function of mass and composition. Astrophysical Journal 311:708–730.CrossRefGoogle Scholar
  36. Lattanzio, J. C., Frost, C. A., Cannon, R. C., and Wood, P. R. (1997) Hot bottom burning nucleosynthesis in 6 M stellar models. Nuclear Physics A 621 (1–2):C435-C438.CrossRefGoogle Scholar
  37. Markram, H., Lubke, J., Frotscher, M., and Sakmann, B. (1997) Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275(5297):213–215.PubMedCrossRefGoogle Scholar
  38. McNaughton, B. L., Barnes, C. A., and O’Keefe, J. (1983) The contributions of position, direction and velocity to single unit activity in the hippocampus to freely-moving rats. Exp Brain Res 52(1):41–49.PubMedCrossRefGoogle Scholar
  39. Muller, R. U., Kubie, J. L., and Ranck J. B. Jr., (1987) Spatial firing patterns of hippocampal complex-spike cells in a fixed environment. J Neurosci 7(7):1935–1950.PubMedGoogle Scholar
  40. O’Keefe, J. and Dostrovsky, J. (1997) The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res 34(1):171–175.CrossRefGoogle Scholar
  41. Orr, G., Rao, G., Houston, F. P., McNaughton, B. L., and Barnes, C. A. (2001) Hippocampal synaptic plasticity is modulated by theta rhythm in fascia dentata of adult and aged freely behaving rats. Hippocampus 11(6):647–654.PubMedCrossRefGoogle Scholar
  42. Pavlides, C., Greenstein, Y. J., Grudman, M., and Winson, J. (1998) Long-term potentiation in the dentate gyrus is induced preferentially on the positive phase of theta-rhythmn. Brain Res 439(2):383–387.CrossRefGoogle Scholar
  43. Press, W. H., Teukolsky, S. K., Flannery, B. P., and Vetterling, T. (1993) Numerical Recipes. In C: The Art of Scientific Computing. Cambridge University Press, Cambridge, UK.Google Scholar
  44. Quirk, G. J., Muller, R. U., Kubie, J. L., and Ranck, J. B. (1992) The positional firing properties of medial entorhinal neurons: description and comparison with hippocampal place cells. J Neurosci 12(5):1945–1963.PubMedGoogle Scholar
  45. Raymond, E. S. and Young, B. (2001) The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary. O’Reilley and Associates.Google Scholar
  46. Redish, A. D. and Touretzky, D. S. (1998) The role of hippocampus in solving the Morris water maze. Neural Comp 10:73–111.CrossRefGoogle Scholar
  47. Schroedinger. (1956) What is Life? And Other Scientific Essays. Doubleday, Garden City, NY.Google Scholar
  48. Sharp, P. E. (1991) Computer simulation of hippocampal place cells. Psychobiology 19:103–115.Google Scholar
  49. Sharp, P. E., Blair, H. T., and Brown, M. (1996) Neural network modeling of the hippocampal formation spatial signals and their possible role in navigation a modular approach. Hippocampus 6(6):720–734.PubMedCrossRefGoogle Scholar
  50. Stephan, K. E., Kamper, L., Bozkurt, A., Burns, G. A., Young, M. P., and Kotter, R. (2000) Advanced database methodology for the collation of connectivity data on the macaque brain (CoCoMac). Philos Trans R Soc Lond B Biol Sci 355(1393):37–54.PubMedCrossRefGoogle Scholar
  51. Suzuki, W. A., Miller, E. K., and Desimone, R. (1997) Object and place memory in the macaque entorhinal cortex. J Neurophysiol 78:1062–1081.PubMedGoogle Scholar
  52. Vanier, M. C. and Bower, J. M. (1999) A comparative survey of automated parameter-search methods for compartmental neural models. Comp Neurosci 7(2):149–171.CrossRefGoogle Scholar
  53. Wenger, M., Ochsenbein, F., Egret, D., Dubois, P., Bonnarel, F., Borde, S., Genova, F., Jasniewicz, G., Laloüe, S., Lesteven, S., and Monier, R. (2000) The SIMBAD astronomical database: the CDS reference database for astronomical objects. Astronomy and Astrophysics Supplement 143:9–22.CrossRefGoogle Scholar
  54. Wyble, B. P., Linster, C., and Hasselmo, M. E. (2000) Size of CA1 evoked synaptic potentials is related to theta rhythm phase in rat hippocampus. J Neurophysiol 83:2138–2144.PubMedGoogle Scholar
  55. Young, B. J., Otto, T., Fox, G., and Eiechenbaum, H. (1997) Memory representation within the parahippocampal region. J Neurosci 17:5183–5195.PubMedGoogle Scholar

Copyright information

© Humana Press Inc 2003

Authors and Affiliations

  • Robert C. Cannon
    • 1
    • 2
  • Michael E. Hasselmo
    • 3
  • Randal A. Koene
    • 3
  1. 1.Theoretical Neurobiology, Born-Bunge FoundationUniversity of AntwerpAntwerpBelgium
  2. 2.Institute for Adaptive and Neural Computation, Division of InformaticsUniversity of EdinburghEdinburghUK
  3. 3.Department of Psychology and Program in NeuroscienceBoston UniversityBoston

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