Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation

Hideaki Kim; Shigeru Shinomoto

doi:10.3934/mbe.2014.11.49

Article Contents

2014, Volume 11, Issue 1: 49-62. Doi: 10.3934/mbe.2014.11.49

This issue Previous Article Cross nearest-spike interval based method to measure synchrony dynamics Next Article The effect of interspike interval statistics on the information gain under the rate coding hypothesis

Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation

Hideaki Kim^1, and
Shigeru Shinomoto^2,

1.
NTT Service Evolution Laboratories, NTT Corporation, Yokosuka-shi, Kanagawa, 239-0847
2.
Department of Physics, Graduate School of Science, Kyoto University, Sakyo-ku, Kyoto 606-8502

Received: November 30, 2012

Revised: May 31, 2013

Published: August 2013

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Abstract

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.

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Mathematics Subject Classification: Primary: 60H30, 62P10; Secondary: 65C30.

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