Filters

2016 - 20173

More filters

2020 - 20223
Reset filters

Settings

Type: Text × Subject: experimental model ×

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Improving Accuracy and Temporal Resolution of Learning Curve Estimation for within- and across-Session Analysis
    (San Francisco, California, US : PLOS, 2016) Deliano, Matthias; Tabelow, Karsten; König, Reinhard; Polzehl, Jörg
    Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. Thereby, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors in the analysis of single-subject data as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from an avoidance learning experiment with rodents, these methods revealed performance changes occurring at multiple time scales within and across training sessions which were otherwise obscured in the conventional analysis. Our work shows that the proper assessment of the behavioral dynamics of learning at high temporal resolution can shed new light on specific learning processes, and, thus, allows to refine existing learning concepts. It further disambiguates the interpretation of neurophysiological signal changes recorded during training in relation to learning.
  • Thumbnail Image
    Item
    An efficient supervised training algorithm for multilayer spiking neural networks
    (San Francisco, CA : Public Library of Science (PLoS), 2016) Xie, X.; Qu, H.; Liu, G.; Zhang, M.; Kurths, J.
  • Thumbnail Image
    Item
    Experimental investigation on preconditioned rate induced tipping in a thermoacoustic system
    (London : Nature Publishing Group, 2017) Tony, J.; Subarna, S.; Syamkumar, K.S.; Sudha, G.; Akshay, S.; Gopalakrishnan, E.A.; Surovyatkina, E.; Sujith, R.I.
    Many systems found in nature are susceptible to tipping, where they can shift from one stable dynamical state to another. This shift in dynamics can be unfavorable in systems found in various fields ranging from ecology to finance. Hence, it is important to identify the factors that can lead to tipping in a physical system. Tipping can mainly be brought about by a change in parameter or due to the influence of external fluctuations. Further, the rate at which the parameter is varied also determines the final state that the system attains. Here, we show preconditioned rate induced tipping in experiments and in a theoretical model of a thermoacoustic system. We provide a specific initial condition (preconditioning) and vary the parameter at a rate higher than a critical rate to observe tipping. We find that the critical rate is a function of the initial condition. Our study is highly relevant because the parameters that dictate the asymptotic behavior of many physical systems are temporally dynamic.