Firing rate estimation using infinite mixture models and its application to neural decoding

Shibue, Ryohei; Komaki, Fumiyasu

doi:10.1152/jn.00818.2016

Cited by 3 publications

(3 citation statements)

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“…The fact that neighboring neurons are not necessarily the nearest in connections, i.e., are activated by different pathways or stimuli (Rey et al, 2015 ) mainly owing to information processing and energy optimization through structural solutions (Pregowska et al, 2019 ) calls the promise of neural decoding. This concept, although not equal with spike sorting, relies heavily on it and undertakes the risk of bias and wrong intensity estimation generated by spike sorting (Shibue and Komaki, 2017 ). Spike sorting also incites statistical analyses, involving correlogram analysis, inter-spike intervals, or spike rates (Veerabhadrappa et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

Bod

Rokai

Meszéna

et al. 2022

Front. Neuroinform.

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The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

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Section: Discussionmentioning

confidence: 99%

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

Bod

Rokai

Meszéna

et al. 2022

Front. Neuroinform.

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“…Nonhomogeneous Poisson process models have a variety of applications. For example, they have recently been used in neural information processing ( [29], [30]). The development of data analyzing methods for such applications based on the theory presented here is also a future challenge.…”

Section: Discussionmentioning

confidence: 99%

Shrinkage Priors for Nonparametric Bayesian Prediction of Nonhomogeneous Poisson Processes

Komaki

2021

IEEE Trans. Inform. Theory

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We consider nonparametric Bayesian estimation and prediction for nonhomogeneous Poisson process models with unknown intensity functions. We propose a class of improper priors for intensity functions. Nonparametric Bayesian inference with kernel mixture based on the class improper priors is shown to be useful, although improper priors have not been widely used for nonparametric Bayes problems. Several theorems corresponding to those for finite-dimensional independent Poisson models hold for nonhomogeneous Poisson process models with infinite-dimensional parameter spaces. Bayesian estimation and prediction based on the improper priors are shown to be admissible under the Kullback-Leibler loss. Numerical methods for Bayesian inference based on the priors are investigated.

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“…This preprocessing generally does not use stimulus information in this spike assignment, and it causes bias and a loss of information for tuning curve estimation [11]. To avoid such information loss, clusterless decoding methods using marked point processes are proposed [12,13,14]. In this approach, spike sequences and the attached waveform information are expressed as marked sequences, and the distribution of the marked sequences is directly estimated.…”

Section: Introductionmentioning

confidence: 99%

Deconvolution of calcium imaging data using marked point processes

Shibue¹,

Komaki

2020

PLoS Comput Biol

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Calcium imaging has been widely used for measuring spiking activities of neurons. When using calcium imaging, we need to extract summarized information from the raw movie beforehand. Recent studies have used matrix deconvolution for this preprocessing. However, such an approach can neither directly estimate the generative mechanism of spike trains nor use stimulus information that has a strong influence on neural activities. Here, we propose a new deconvolution method for calcium imaging using marked point processes. We consider that the observed movie is generated from a probabilistic model with marked point processes as hidden variables, and we calculate the posterior of these variables using a variational inference approach. Our method can simultaneously estimate various kinds of information, such as cell shape, spike occurrence time, and tuning curve. We apply our method to simulated and experimental data to verify its performance.

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Firing rate estimation using infinite mixture models and its application to neural decoding

Cited by 3 publications

References 20 publications

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

Shrinkage Priors for Nonparametric Bayesian Prediction of Nonhomogeneous Poisson Processes

Deconvolution of calcium imaging data using marked point processes

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