ArticleJournal of neuroscience methods2015
Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling.
Article in Journal of neuroscience methods, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Computational Model of Working Memory Based on Spike-Timing-Dependent Plasticity.Frontiers in computational neuroscience · 2021Article
- A 512-Channel Multi-Layer Polymer-Based Neural Probe Array.Journal of microelectromechanical systems : a joint IEEE and ASME publication on microstructures, microactuators, microsensors, and microsystems · 2020Article
- Multi-Input, Multi-Output Neuronal Mode Network Approach to Modeling the Encoding Dynamics and Functional Connectivity of Neural Systems.Neural computation · 2019Article
- Acute in vivo testing of a conformal polymer microelectrode array for multi-region hippocampal recordings.Journal of neural engineering · 2018Article
- Tracking recurrence of correlation structure in neuronal recordings.Journal of neuroscience methods · 2017Article
- High-fat diet impairs spatial memory and hippocampal intrinsic excitability and sex-dependently alters circulating insulin and hippocampal insulin sensitivity.Biology of sex differences · 2016Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This paper presents a systems identification approach for studying the long-term synaptic plasticity using natural spiking activities. This approach consists of three modeling steps. First, a multi-input, single-output (MISO), nonlinear dynamical spiking neuron model is formulated to estimate and represent the synaptic strength in means of functional connectivity between input and output neurons. Second, this MISO model is extended to a nonstationary form to track the time-varying properties of the synaptic strength. Finally, a Volterra modeling method is used to extract the synaptic learning rule, e.g., spike-timing-dependent plasticity, for the explanation of the input-output nonstationarity as the consequence of the past input-output spiking patterns. This framework is developed to study the underlying mechanisms of learning and memory formation in behaving animals, and may serve as the computational basis for building the next-generation adaptive cortical prostheses.
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Registered trials
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