Recursive estimation and time-series analysis: an introduction
Recursive estimation and time-series analysis: an introduction
System identification: theory for the user
System identification: theory for the user
Memorization and Association on a Realistic Neural Model
Neural Computation
Environmental time series analysis and forecasting with the Captain toolbox
Environmental Modelling & Software
System identification of Drosophila olfactory sensory neurons
Journal of Computational Neuroscience
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Recent advances have started to uncover the underlying mechanisms of metabotropic glutamate receptor mGluR-dependent long-term depression LTD. However, it is not completely clear how these mechanisms are linked, and it is believed that several crucial mechanisms remain to be revealed. In this study, we investigated whether system identification SI methods can be used to gain insight into the mechanisms of synaptic plasticity. SI methods have been shown to be an objective and powerful approach for describing how sensory neurons encode information about stimuli. However, to our knowledge, it is the first time that SI methods have been applied to electrophysiological brain slice recordings of synaptic plasticity responses. The results indicate that the SI approach is a valuable tool for reverse-engineering of mGluR-LTD responses. We suggest that such SI methods can aid in unraveling the complexities of synaptic function.