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Knowledge Representation Meets Simulation to Investigate Memory Problems after Seizures

  • Youwei Zheng
  • Lars Schwabe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6889)

Abstract

Despite much efforts in data and model sharing, the full potential of community-based and computer-aided research has not been unleashed in neuroscience. Here we argue that data and model sharing shall be complemented with machine-readable annotations of scientific publications similar to the semantic web, because this would allow for automated knowledge discovery as recently demonstrated using so-called “robot scientists”. We consider a particular example, namely the potentially disruptive role of synaptic plasticity for memories during paroxysmal brain activity. A systematic simulation study is performed where we compare the combinations of different rules of spike-timing-dependent plasticity (STDP) and different kinds of paroxysmal activity in terms of how they affect memory retention. We translate the simulation results into a Bayesian network and show how new empirical evidence can be used in order to infer currently unknown model properties (the STDP mechanisms and the nature of paroxysmal brain activity).

Keywords

Bayesian Network Synaptic Strength Inhibitory Synapse Memory Retention Model Sharing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ansorg, R., Schwabe, L.: Domain-specific modeling as a pragmatic approach to neuronal model descriptions. Brain Informatics, 168–179 (2010)Google Scholar
  2. 2.
    Le Novère, N.: Model storage, exchange and integration. BMC Neuroscience 7 (suppl.1), S11 (2006)Google Scholar
  3. 3.
    Langley, P., Simon, H.A., Bradshaw, G.L., Zytkow, J.M.: Scientific Discovery: Computational Explorations of the Creative Processes. The MIT Press, Cambridge (1987)Google Scholar
  4. 4.
    Sparkes, A., Aubrey, W., Byrne, E., Clare, A., Khan, M.N., Liakata, M., Markham, M., Rowland, J., Soldatova, L.N., Whelan, K.E., Young, M., King, R.D.: Towards Robot Scientists for autonomous scientific discovery. Automated Experimentation 2, 1 (2010)CrossRefGoogle Scholar
  5. 5.
    Billings, G., van Rossum, M.C.W.: Memory Retention and Spike-Timing-Dependent Plasticity. J. Neurophysiol. 101(6), 2775 (2009)CrossRefGoogle Scholar
  6. 6.
    Truccolo, W., J., Donoghue, J.A., Hochberg, L.R., Eskandar, E.N., Madsen, J.R., Anderson, W.S., Brown, E.N., Halgren, E., Cash, S.S.: Single-neuron dynamics in human focal epilepsy. Nat. Neurosci., 1–9 (2011)Google Scholar
  7. 7.
    Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919–926 (2000)CrossRefGoogle Scholar
  8. 8.
    Markram, H., Leubke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275(5297), 213 (1997)CrossRefGoogle Scholar
  9. 9.
    Kepecs, A., van Rossum, M.C.W., Song, S., Tegner, J.: Spike-timing-dependent plasticity: common themes and divergent vistas. Biol. Cybern. 87(5-6), 446–458 (2002)CrossRefzbMATHGoogle Scholar
  10. 10.
    Bi, G.Q., Poo, M.M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464–10472 (1998)Google Scholar
  11. 11.
    Rodgers, J.L., Nicewander, W.A.: Thirteen ways to look at the correlation coefficient. American Statistician 42(1), 59–66 (1988)CrossRefGoogle Scholar
  12. 12.
    Bower, M.R., Buckmaster, P.S.: Changes in granule cell firing rates precede locally recorded spontaneous seizures by minutes in an animal model of temporal lobe epilepsy. J. Neurophysiol. 99(5), 2431–2442 (2008)CrossRefGoogle Scholar
  13. 13.
    Bayes Net Toolbox for Matlab: http://code.google.com/p/bnt

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Youwei Zheng
    • 1
  • Lars Schwabe
    • 1
  1. 1.Dept. of Computer Science and Electrical Engineering, Adaptive and Regenerative Software SystemsUniversität RostockRostockGermany

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