The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
뉴런의 스파이크 타이밍은 불규칙하며 1차원 점 과정으로 간주됩니다. 베이지안 접근 방식은 일반적으로 스파이크 타이밍 시퀀스에서 시간에 따른 발사 속도 함수를 추정하는 데 사용됩니다. 또한 단일 스파이크 시퀀스에서만 발사 속도를 추정하는 데 사용할 수도 있습니다. 그러나 일반적으로 비율 함수는 자유도가 너무 높기 때문에 베이지안 추정을 수행하기 위해 근사 기법을 사용하는 경우가 많습니다. 정확한 주변 분포를 효율적으로 계산하는 전달 행렬 방법을 발사 속도 추정에 적용하고 베이지안 프레임워크에 대해 정확한 결과를 얻을 수 있는 알고리즘을 개발했습니다. 이 추정 방법을 사용하여 이전 하이퍼파라미터 값의 불일치가 한계 분포와 발사 속도 추정에 어떤 영향을 미치는지 조사했습니다.
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -