Name
Title | ||
|---|---|---|
Laguerre-Volterra identification of spike-timing-dependent plasticity from spiking activity: a simulation study. |
Abstract | ||
|---|---|---|
This paper presents a Laguerre-Volterra methodology for identifying a plasticity learning rule from spiking neural data with four components: 1) By analyzing input-output spiking data, the effective contribution of an input on the output firing probability can be quantified with weighted Volterra kernels. 2) The weight of these Volterra kernels can be tracked over time using the stochastic state point processing filtering algorithm (SSPPF) 3) Plasticity system Volterra kernels can be estimated by treating the tracked change in weight over time as the plasticity system output and the spike timing data as the input. 4) Laguerre expansion of all Volterra kernels allows for minimization of open parameters during estimation steps. A single input spiking neuron with Spike-timing-dependent plasticity (STDP) and prolonged STDP induction is simulated. Using the spiking data from this simulation, the amplitude of the STDP learning rule and the time course of the induction is accurately estimated. This framework can be applied to identify plasticity for more complicated plasticity paradigms and is applicable to in vivo data. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6610814 | EMBC |
Keywords | Field | DocType |
stochastic processes,spike timing data,laguerre-volterra identification,neurophysiology,laguerre expansion,spike-timing-dependent plasticity,spiking neural data,stochastic state point processing filtering algorithm,in vivo data,stdp learning rule,plasticity learning rule,bioelectric phenomena,input-output spiking data,spiking activity,filtering theory,single input spiking neuron,ssppf,output firing probability,probability,weighted volterra kernels,feedforward neural networks,shape,kernel,spike timing dependent plasticity,estimation | Computer vision,Neurophysiology,Biological system,Laguerre polynomials,Computer science,Stochastic process,Filter (signal processing),Learning rule,Minification,Artificial intelligence,Spike-timing-dependent plasticity,Plasticity | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 3 |
PageRank | References | Authors |
0.59 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian S Robinson | 1 | 5 | 2.32 |
| Dong Song | 2 | 202 | 34.25 |
| theodore w berger | 3 | 380 | 87.26 |