TY - CHAP
T1 - Identification of Neural Plasticity From Spikes
AU - Song, Dong
AU - Robinson, Brian S.
AU - Chan, Rosa H.M.
AU - Berger, Theodore W.
PY - 2019
Y1 - 2019
N2 - This chapter describes a computational modeling approach for identifying short-term and long-term synaptic plasticity (LTSP) from spikes recorded in vivo. In this approach, synaptic strength is represented as input–output dynamics between neurons; short-term synaptic plasticity (STSP) is defined as input–output nonlinear dynamics; LTSP is formulated as the nonstationarity of such nonlinear dynamics; synaptic learning rule is essentially the function governing the characteristics of the LTSP based on the input–output spiking patterns. As a special case, spike timing–dependent plasticity is equivalent to a second-order learning rule describing the pairwise interactions between single-input spikes and single-output spikes. Using experimental and simulated input–output data, it has been shown that STSP, LTSP, and learning rules can be accurately identified with a set of nonstationary, nonlinear dynamical models.
AB - This chapter describes a computational modeling approach for identifying short-term and long-term synaptic plasticity (LTSP) from spikes recorded in vivo. In this approach, synaptic strength is represented as input–output dynamics between neurons; short-term synaptic plasticity (STSP) is defined as input–output nonlinear dynamics; LTSP is formulated as the nonstationarity of such nonlinear dynamics; synaptic learning rule is essentially the function governing the characteristics of the LTSP based on the input–output spiking patterns. As a special case, spike timing–dependent plasticity is equivalent to a second-order learning rule describing the pairwise interactions between single-input spikes and single-output spikes. Using experimental and simulated input–output data, it has been shown that STSP, LTSP, and learning rules can be accurately identified with a set of nonstationary, nonlinear dynamical models.
KW - Brain
KW - Depression
KW - Facilitation
KW - Hippocampus
KW - Learning rule
KW - Nonlinear dynamical model
KW - Nonlinear dynamics
KW - Nonstationarity
KW - Potentiation
KW - Regularized estimation
KW - Sparsity
KW - Spatiotemporal pattern
KW - Spike
KW - Spike timing–dependent plasticity
KW - Volterra kernel
UR - http://www.scopus.com/inward/record.url?scp=85052500419&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85052500419&origin=recordpage
U2 - 10.1016/B978-0-12-812028-6.00007-0
DO - 10.1016/B978-0-12-812028-6.00007-0
M3 - 12_Chapter in an edited book (Author)
SN - 9780128120286
T3 - Handbook of Behavioral Neuroscience
SP - 135
EP - 151
BT - Handbook of in Vivo Neural Plasticity Techniques
A2 - Manahan-Vaughan, Denise
PB - Elsevier B.V.
ER -