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 propos...
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AIMS Press
2013-08-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.49 |
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| author | Hideaki Kim Shigeru Shinomoto |
| author_facet | Hideaki Kim Shigeru Shinomoto |
| author_sort | Hideaki Kim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f34ac821d037437e8bd180aa66fb82f2 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2013-08-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-f34ac821d037437e8bd180aa66fb82f22025-01-24T02:26:48ZengAIMS PressMathematical Biosciences and Engineering1551-00182013-08-01111496210.3934/mbe.2014.11.49Estimating nonstationary inputs from a single spike train based on a neuron model with adaptationHideaki Kim0Shigeru Shinomoto1NTT Service Evolution Laboratories, NTT Corporation, Yokosuka-shi, Kanagawa, 239-0847Department of Physics, Graduate School of Science, Kyoto University, Sakyo-ku, Kyoto 606-8502Because 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.https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.49method of momentsspike frequency adaptationinput estimationneuronal modelspike datastate-space methodbayesian analysisfirst-passage time. |
| spellingShingle | Hideaki Kim Shigeru Shinomoto Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation Mathematical Biosciences and Engineering method of moments spike frequency adaptation input estimation neuronal model spike data state-space method bayesian analysis first-passage time. |
| title | Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation |
| title_full | Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation |
| title_fullStr | Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation |
| title_full_unstemmed | Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation |
| title_short | Estimating nonstationary inputs from a single spike train based on a neuron model with adaptation |
| title_sort | estimating nonstationary inputs from a single spike train based on a neuron model with adaptation |
| topic | method of moments spike frequency adaptation input estimation neuronal model spike data state-space method bayesian analysis first-passage time. |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2014.11.49 |
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