title: Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling.
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.
authors.affiliation: Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: dsong@usc.edu.
authors.forename: Dong
authors.initials: D
authors.lastname: Song
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. Electronic address: rosachan@cityu.edu.hk.
authors.forename: Rosa H M
authors.initials: RH
authors.lastname: Chan
authors.affiliation: Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: brian.samuel.robinson@gmail.com.
authors.forename: Brian S
authors.initials: BS
authors.lastname: Robinson
authors.affiliation: Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: marmarelis@hotmail.com.
authors.forename: Vasilis Z
authors.initials: VZ
authors.lastname: Marmarelis
authors.affiliation: Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157, USA. Electronic address: ioanopris.phd@gmail.com.
authors.forename: Ioan
authors.initials: I
authors.lastname: Opris
authors.affiliation: Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157, USA. Electronic address: rhampson@wfubmc.edu.
authors.forename: Robert E
authors.initials: RE
authors.lastname: Hampson
authors.affiliation: Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157, USA. Electronic address: sdeadwyl@wfubmc.edu.
authors.forename: Sam A
authors.initials: SA
authors.lastname: Deadwyler
authors.affiliation: Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: berger@bmsr.usc.edu.
authors.forename: Theodore W
authors.initials: TW
authors.lastname: Berger
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D006624:Hippocampus / Q000166:cytology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics
keywords: Learning rule; Spatio-temporal pattern; Spike; Spike-timing-dependent plasticity