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Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part II: Application to the Rat Hippocampus

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Abstract

This paper presents a pilot application of the Boolean–Volterra modeling methodology presented in the companion paper (Part I) that is suitable for the analysis of systems with point-process inputs and outputs (e.g., recordings of the activity of neuronal ensembles). This application seeks to discover the causal links between two neuronal ensembles in the hippocampus of a behaving rat. The experimental data come from multi-unit recordings in the CA3 and CA1 regions of the hippocampus in the form of sequences of action potentials—treated mathematically as point-processes and computationally as spike-trains—that are collected in vivo during two behavioral tasks. The modeling objective is to identify and quantify the causal links among the neurons generating the recorded activity, using Boolean–Volterra models estimated directly from the data according to the methodological framework presented in the companion paper. The obtained models demonstrate the feasibility of the proposed approach using short data-records and provide some insights into the functional properties of the system (e.g., regarding the presence of rhythmic characteristics in the neuronal dynamics of these ensembles), making the proposed methodology an attractive tool for the analysis and modeling of multi-unit recordings from neuronal systems in a practical context.

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References

  1. Berger T. W., A. Ahuja, S. H. Courellis, S. A. Deadwyler, G. Erinjippurath, G. A. Gerhardt, G. Gholmeih, J. J. Granacki, R. E. Hampson, M-C. Hsiao, J. LaCoss, V. Z. Marmarelis, P. Nasiatka, V. Srinivasan, D. Song, A. R. Tanguay Jr, J. Wills (2005) Hippocampal-cortical neural prostheses to restore lost cognitive function. IEEE EMBS Special Issue: Toward Biomimetic Microelectronics as Neural Prostheses. 24:30–44

    Google Scholar 

  2. Berger T. W., M. Baudry, R. D. Brinton, J. S. Liaw, V. Z. Marmarelis, A. Y. Park, B. J. Sheu and A. R. Tanguay (2001) Brain-implantable biomimetic electronics as the next era in neural prosthetics. IEE, 89:993-1012

    Article  CAS  Google Scholar 

  3. Berger T. W., J. L. Eriksson, D. A. Ciarolla, R. J. Sclabassi (1988) Nonlinear systems analysis of the hippocampal perforant path-dentate system. II. Effects of random train stimulation. J. Neurophysiol. 60:1077-1094

    Google Scholar 

  4. Berger, T. W., and D. L. Glanzman. Towards Replacement Parts for the Brain: Implantable Biomimetic Electronics as Neural Prostheses, Cambridge, MA: MIT Press, Chapter 12, p. 241, 2005.

  5. Buzsaki G., L. W. Leung and C. H. Vanderwolf (1983) Cellular bases of hippocampal EEG in the behaving rat. Brain Res. 287(2):139–171

    PubMed  CAS  Google Scholar 

  6. Dimoka, A., S. H. Courellis, G. Gholmieh, V. Z. Marmarelis, and T. W. Berger. Modeling the nonlinear properties of the in vitro hippocampal perforant path-dentate system using multi-electrode array technology. IEEE Trans. Biomed. Eng. (in press) 2008.

  7. Foster D. J. and M. A. Wilson (2007) Hippocampal Theta Sequences. Hippocampus. 17:1093-99 doi:10.1002/hipo.20345

    Article  PubMed  Google Scholar 

  8. Gholmieh G., S. H. Courellis, S. Fakheri, E. Cheung, V. Z. Marmarelis, M. Baudry and T. W. Berger (2003) Detection and classification of neurotoxins using a novel short-term plasticity quantification method. Biosens. Bioelectron. 18(12):1467–78 doi:10.1016/S0956-5663(03)00120-9

    Article  PubMed  CAS  Google Scholar 

  9. Gholmieh G., S. H. Courellis, V. Z. Marmarelis, T. W. Berger (2002) An efficient method for studying short-term plasticity with random impulse train stimuli. J Neurosci Methods 21(2):111–127 doi:10.1016/S0165-0270(02)00164-4.

    Article  Google Scholar 

  10. Gholmieh G., S. H. Courellis, V. Z. Marmarelis, T. W. Berger (2005) Detecting CA1 short-term plasticity variations with changes in stimulus intensity and extracellular medium composition. Neurocomputing. 63:465-481, doi:10.1016/j.neucom.2004.07.001

    Article  Google Scholar 

  11. Gholmieh G., S. H. Courellis, V. Z. Marmarelis and T. W. Berger (2007) Nonlinear dynamic model of CA1 short-term plasticity using random impulse train stimulation. Ann. Biomed. Eng. 35(5):847-857. doi:10.1007/s10439-007-9253-6

    Article  PubMed  Google Scholar 

  12. Gruart A., M. D. Munoz, J. M. Delgado-Garcia (2006) Involvement of the CA3-CA1 synapse in the acquisition of associative learning in behaving mice. J of Neurosci. 26(4):1077-87. doi:10.1523/JNEUROSCI.2834-05.2006

    Article  PubMed  CAS  Google Scholar 

  13. Hampson, R. E., J. D. Simeral and S. A. Deadwyler (1999) Distribution of spatial and nonspatial information in dorsal hippocampus. Nature. 402(6762): 610-14. doi:10.1038/45154

    Article  PubMed  CAS  Google Scholar 

  14. Leutgeb S., J. K. Leutgeb, M. B. Moser, E. I. Moser (2005) Place cells, spatial maps and the population code for memory. Curr Opin Neurobiol. 15(6):738–746. doi:10.1016/j.conb.2005.10.002

    Article  PubMed  CAS  Google Scholar 

  15. Levy, W. B (1996) A sequence predicting CA3 is a flexible associator that learns and uses context to solve hippocampal-like tasks. Hippocampus 6(6):579–590. doi:10.1002/(SICI)1098-1063(1996)6:6<579::AID-HIPO3>3.0.CO;2-C

    Article  PubMed  CAS  Google Scholar 

  16. Marmarelis, V. Z. Nonlinear Dynamic Modeling of Physiological Systems. New York: Wiley Interscience, 2004, 359 pp.

    Google Scholar 

  17. Maurer A. P., B. L. MacNaughton (2007) Network and intrinsic cellular mechanisms underlying theta phase precession of hippocampal neurons. Trends in Neurosci. 30(7):325–33, doi:10.1016/j.tins.2007.05.002

    Article  PubMed  CAS  Google Scholar 

  18. Menschik, E. D., S. C. Yen and L. H. Finkel (1999) Model- and scale-independent performance of a hippocampal CA3 network architecture. Neurocomputing. 26-7: 443-453. doi:10.1016/S0925-2312(99)00050-8

    Article  Google Scholar 

  19. O’Keefe J., J. Dostrovsky (1971) The hippocampus as a spatial map Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34(1):171–175. doi:10.1016/0006-8993(71)90358-1

    Article  PubMed  Google Scholar 

  20. Sargsyan A., A. Melkonyan, H. Mkrtchian, C. Paptheodoropoulos, G. Kostopoulos (2004) A computer model of field potential responses for the study of short-term plasticity in hippocampus. J. of Neurosci. Methods 135(1–2):175–191. doi:10.1016/j.jneumeth.2003.12.009

    Article  PubMed  Google Scholar 

  21. Song D., R. H. Chan, V. Z. Marmarelis, R. E. Hampson, S. A. Deadwyler, T. W. Berger (2007) Nonlinear dynamic modeling of spike train transformations for hippocampal-cortical prostheses. IEEE Trans Biomed Eng 54(6):1053-66. doi:10.1109/TBME.2007.891948

    Article  PubMed  Google Scholar 

  22. Touretzky D. S., A. D. Redish (1996) Theory of rodent navigation based on interacting representations of space. Hippocampus. 6(3): 247-270. doi:10.1002/(SICI)1098-1063(1996)6:3<247::AID-HIPO4>3.0.CO;2-K

    Article  PubMed  CAS  Google Scholar 

  23. Treves A., E. T. Rolls (1992) Computational constraints suggest the need for two distinct input systems to the hippocampal CA3 network. Hippocampus. 2(2): 189-199. doi:10.1002/hipo.450020209

    Article  PubMed  CAS  Google Scholar 

  24. Vertes R. P (2005) Hippocampal theta rhythm: a tag for short-term memory. Hippocampus. 15(7):923-35. doi:10.1002/hipo.20118

    Article  PubMed  CAS  Google Scholar 

  25. Zanos T. P., S. H. Courellis, T. W. Berger, R. E. Hampson, S. A. Deadwyler, V. Z. Marmarelis (2008) Nonlinear modeling of causal interrelationships in neuronal ensembles. IEEE Trans Neural Systems & Rehab Eng 16(4): 336-52. doi:10.1109/TNSRE.2008.926716

    Article  PubMed  Google Scholar 

  26. Zanos T. P., S. H. Courellis, R. E. Hampson, S. A. Deadwyler, V. Z. Marmarelis, T. W. Berger (2006) A multi-input modeling approach to quantify hippocampal nonlinear dynamic transformations. IEEE Eng. Medicine Biology Conf. 1:4967-70.

    Article  Google Scholar 

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Acknowledgments

This work was supported by the NIH/NIBIB Grant No. P41-EB001978 to the Biomedical Simulations Resource at USC and by the NSF Grant No. EEC-0310723 to the Engineering Research Center for Biomimetic Micro-Electronic Systems at USC.

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, DRB 367, Los Angeles, CA, 90089-1111, USA

    Theodoros P. Zanos, Theodore W. Berger & Vasilis Z. Marmarelis

  2. Department of Physiology and Pharmacology, Wake Forest University, Winston-Salem, NC, USA

    Robert E. Hampson & Samuel E. Deadwyler

Authors
  1. Theodoros P. Zanos
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  2. Robert E. Hampson
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  3. Samuel E. Deadwyler
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  4. Theodore W. Berger
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  5. Vasilis Z. Marmarelis
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Correspondence to Theodoros P. Zanos.

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Zanos, T.P., Hampson, R.E., Deadwyler, S.E. et al. Boolean Modeling of Neural Systems with Point-Process Inputs and Outputs. Part II: Application to the Rat Hippocampus. Ann Biomed Eng 37, 1668–1682 (2009). https://doi.org/10.1007/s10439-009-9716-z

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  • DOI: https://doi.org/10.1007/s10439-009-9716-z

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