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).
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Zheng, Y., Schwabe, L. (2011). Knowledge Representation Meets Simulation to Investigate Memory Problems after Seizures. In: Hu, B., Liu, J., Chen, L., Zhong, N. (eds) Brain Informatics. BI 2011. Lecture Notes in Computer Science(), vol 6889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23605-1_11
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DOI: https://doi.org/10.1007/978-3-642-23605-1_11
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