Knowledge Representation Meets Simulation to Investigate Memory Problems after Seizures
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).
KeywordsBayesian Network Synaptic Strength Inhibitory Synapse Memory Retention Model Sharing
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- 1.Ansorg, R., Schwabe, L.: Domain-specific modeling as a pragmatic approach to neuronal model descriptions. Brain Informatics, 168–179 (2010)Google Scholar
- 2.Le Novère, N.: Model storage, exchange and integration. BMC Neuroscience 7 (suppl.1), S11 (2006)Google Scholar
- 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
- 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
- 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
- 13.Bayes Net Toolbox for Matlab: http://code.google.com/p/bnt