Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation.
Because every spike of a neuron is determined by input signals, a train of spikes may contain information about the dynamics of unobserved neurons. A state-space method based on the leaky integrate-and-fire model, describing neuronal transformation from input signals to a spike train has been proposed for tracking input parameters represented by their mean and fluctuation [11]. In the present paper, we propose to make the estimation more realistic by adopting an LIF model augmented with an adaptive moving threshold. Moreover, because the direct state-space method is computationally infeasible for a data set comprising thousands of spikes, we further develop a practical method for transforming instantaneous firing characteristics back to input parameters. The instantaneous firing characteristics, represented by the firing rate and non-Poisson irregularity, can be estimated using a computationally feasible algorithm. We applied our proposed methods to synthetic data to clarify that they perform well.
NTT Service Evolution Laboratories, NTT Corporation, Yokosuka-shi, Kanagawa, 239-0847, Japan. kin.hideaki@lab.ntt.co.jp.
Hideaki
H
Kim
Shigeru
S
Shinomoto
D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D008959:Models, Neurological; D008962:Models, Theoretical; D009474:Neurons / Q000502:physiology*; D016011:Normal Distribution; D016012:Poisson Distribution; D011336:Probability; D015203:Reproducibility of Results