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)


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).


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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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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