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The spike timings of neurons are irregular and are considered to be a one-dimensional point process. The Bayesian approach is generally used to estimate the time-dependent firing rate function from sequences of spike timings. It can also be used to estimate the firing rate from only a single sequence of spikes. However, the rate function has too many degrees of freedom in general, so approximation techniques are often used to carry out the Bayesian estimation. We applied the transfer matrix method, which efficiently computes the exact marginal distribution, to the estimation of the firing rate and developed an algorithm that enables the exact results to be obtained for the Bayesian framework. Using this estimation method, we investigated how the mismatch of the prior hyperparameter value affects the marginal distribution and the firing rate estimation.

- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.7 pp.1362-1368

- Publication Date
- 2009/07/01

- Publicized

- Online ISSN
- 1745-1361

- DOI
- 10.1587/transinf.E92.D.1362

- Type of Manuscript
- Special Section INVITED PAPER (Special Section on Large Scale Algorithms for Learning and Optimization)

- Category

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Kazuho WATANABE, Hiroyuki TANAKA, Keiji MIURA, Masato OKADA, "Transfer Matrix Method for Instantaneous Spike Rate Estimation" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 7, pp. 1362-1368, July 2009, doi: 10.1587/transinf.E92.D.1362.

Abstract: The spike timings of neurons are irregular and are considered to be a one-dimensional point process. The Bayesian approach is generally used to estimate the time-dependent firing rate function from sequences of spike timings. It can also be used to estimate the firing rate from only a single sequence of spikes. However, the rate function has too many degrees of freedom in general, so approximation techniques are often used to carry out the Bayesian estimation. We applied the transfer matrix method, which efficiently computes the exact marginal distribution, to the estimation of the firing rate and developed an algorithm that enables the exact results to be obtained for the Bayesian framework. Using this estimation method, we investigated how the mismatch of the prior hyperparameter value affects the marginal distribution and the firing rate estimation.

URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1362/_p

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@ARTICLE{e92-d_7_1362,

author={Kazuho WATANABE, Hiroyuki TANAKA, Keiji MIURA, Masato OKADA, },

journal={IEICE TRANSACTIONS on Information},

title={Transfer Matrix Method for Instantaneous Spike Rate Estimation},

year={2009},

volume={E92-D},

number={7},

pages={1362-1368},

abstract={The spike timings of neurons are irregular and are considered to be a one-dimensional point process. The Bayesian approach is generally used to estimate the time-dependent firing rate function from sequences of spike timings. It can also be used to estimate the firing rate from only a single sequence of spikes. However, the rate function has too many degrees of freedom in general, so approximation techniques are often used to carry out the Bayesian estimation. We applied the transfer matrix method, which efficiently computes the exact marginal distribution, to the estimation of the firing rate and developed an algorithm that enables the exact results to be obtained for the Bayesian framework. Using this estimation method, we investigated how the mismatch of the prior hyperparameter value affects the marginal distribution and the firing rate estimation.},

keywords={},

doi={10.1587/transinf.E92.D.1362},

ISSN={1745-1361},

month={July},}

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TY - JOUR

TI - Transfer Matrix Method for Instantaneous Spike Rate Estimation

T2 - IEICE TRANSACTIONS on Information

SP - 1362

EP - 1368

AU - Kazuho WATANABE

AU - Hiroyuki TANAKA

AU - Keiji MIURA

AU - Masato OKADA

PY - 2009

DO - 10.1587/transinf.E92.D.1362

JO - IEICE TRANSACTIONS on Information

SN - 1745-1361

VL - E92-D

IS - 7

JA - IEICE TRANSACTIONS on Information

Y1 - July 2009

AB - The spike timings of neurons are irregular and are considered to be a one-dimensional point process. The Bayesian approach is generally used to estimate the time-dependent firing rate function from sequences of spike timings. It can also be used to estimate the firing rate from only a single sequence of spikes. However, the rate function has too many degrees of freedom in general, so approximation techniques are often used to carry out the Bayesian estimation. We applied the transfer matrix method, which efficiently computes the exact marginal distribution, to the estimation of the firing rate and developed an algorithm that enables the exact results to be obtained for the Bayesian framework. Using this estimation method, we investigated how the mismatch of the prior hyperparameter value affects the marginal distribution and the firing rate estimation.

ER -