ArticleJournal of computational neuroscience2009
Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: Computational study.
Article in Journal of computational neuroscience, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- A Glutamatergic Spine Model to Enable Multi-Scale Modeling of Nonlinear Calcium Dynamics.Frontiers in computational neuroscience · 2018Article
- Cannabinoids disrupt memory encoding by functionally isolating hippocampal CA1 from CA3.PLoS computational biology · 2017Article
- Hippocampal closed-loop modeling and implications for seizure stimulation design.Journal of neural engineering · 2015Article
- Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling.Journal of neuroscience methods · 2015Article
- Model-based asessment of an in-vivo predictive relationship from CA1 to CA3 in the rodent hippocampus.Journal of computational neuroscience · 2015Article
- System identification of point-process neural systems using probability based Volterra kernels.Journal of neuroscience methods · 2015Article
- Understanding spike-triggered covariance using Wiener theory for receptive field identification.Journal of vision · 2015Article
- Volterra representation enables modeling of complex synaptic nonlinear dynamics in large-scale simulations.Frontiers in computational neuroscience · 2015Article
- From data patterns to mechanistic models in acute critical illness.Journal of critical care · 2014Review
- Extraction and restoration of hippocampal spatial memories with non-linear dynamical modeling.Frontiers in systems neuroscience · 2014Article
- Identification of sparse neural functional connectivity using penalized likelihood estimation and basis functions.Journal of computational neuroscience · 2013Article
- Insights on synaptic paired-pulse response using parametric and non-parametric models.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2013Article
- A nonlinear model for hippocampal cognitive prosthesis: memory facilitation by hippocampal ensemble stimulation.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2012Article
- A hippocampal cognitive prosthesis: multi-input, multi-output nonlinear modeling and VLSI implementation.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2012Article
- Nonlinear dynamic modeling of neuron action potential threshold during synaptically driven broadband intracellular activity.IEEE transactions on bio-medical engineering · 2012Article
- Nonlinear dynamic modeling of synaptically driven single hippocampal neuron intracellular activity.IEEE transactions on bio-medical engineering · 2011Article
- The Neurobiological Basis of Cognition: Identification by Multi-Input, Multioutput Nonlinear Dynamic Modeling: A method is proposed for measuring and modeling human long-term memory formation by mathematical analysis and computer simulation of nerve-cell dynamics.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2010Article
- A method for decoding the neurophysiological spike-response transform.Journal of neuroscience methods · 2009Article
- Nonlinear modeling of neural population dynamics for hippocampal prostheses.Neural networks : the official journal of the International Neural Network Society · 2009Article
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3 authors.
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Abstract
Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input-output data. Results show that the non-parametric models accurately and efficiently replicate the input-output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP.
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