Scalable approximate Bayesian inference for particle tracking data

Sun, Ruoxi; Paninski, Liam

doi:10.1101/276253

Cited by 2 publications

(3 citation statements)

References 12 publications

(26 reference statements)

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“…All ground truth for training comes only from simulation. Realistic synthetic, semi-synthetic or augmented datasets have been key to cracking other challenging problems in neurosicence [26][27][28][29][30][31] and have already shown promising potential for tracking neurons [17].…”

Section: Introductionmentioning

confidence: 99%

“…All ground truth for training comes only from simulation. Realistic synthetic, semi-synthetic or augmented datasets have been key to cracking other challenging problems in neurosicence ( Parthasarathy et al, 2017 ; Yoon et al, 2017 ; Sun et al, 2018 ; Lee et al, 2020 ; Mathis and Mathis, 2020 ; Pereira et al, 2020 ) and have already shown promising potential for tracking neurons ( Wen et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training

Creamer

Randi

et al. 2021

eLife

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We present an automated method to track and identify neurons in C. elegans, called 'fast Deep Neural Correspondence' or fDNC, based on the transformer network architecture. The model is trained once on empirically derived semi-synthetic data and then predicts neural correspondence across held-out real animals. The same pre-trained model both tracks neurons across time and identifies corresponding neurons across individuals. Performance is evaluated against hand-annotated datasets, including NeuroPAL [1]. Using only position information, the method achieves 79.1% accuracy at tracking neurons within an individual and 64.1% accuracy at identifying neurons across individuals. Accuracy at identifying neurons across individuals is even higher (78.2%) when the model is applied to a dataset published by another group [2]. Accuracy reaches 74.7% on our dataset when using color information from NeuroPAL. Unlike previous methods, fDNC does not require straightening or transforming the animal into a canonical coordinate system. The method is fast and predicts correspondence in 10ms making it suitable for future real-time applications.

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Section: Introductionmentioning

confidence: 99%

“…All ground truth for training comes only from simulation. Realistic synthetic, semi-synthetic or augmented datasets have been key to cracking other challenging problems in neurosicence ( Parthasarathy et al, 2017 ; Yoon et al, 2017 ; Sun et al, 2018 ; Lee et al, 2020 ; Mathis and Mathis, 2020 ; Pereira et al, 2020 ) and have already shown promising potential for tracking neurons ( Wen et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training

Creamer

Randi

et al. 2021

eLife

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“…See e.g. (Parthasarathy et al, 2017, Yoon et al, 2017, Weigert et al, 2018, Sun and Paninski, 2018 for previous applications of similar ideas to image denoising and decoding problems.…”

Section: Detectionmentioning

confidence: 99%

YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina

Lee

Mitelut

Shokri

et al. 2020

Preprint

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Spike sorting is a critical first step in extracting neural signals from large-scale multi-electrode array (MEA) data. This manuscript presents several new techniques that make MEA spike sorting more robust and accurate. Our pipeline is based on an efficient multi-stage "triage-then-cluster-then-pursuit" approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or "collided" events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection and denoising method followed by efficient outlier triaging. The denoised spike waveforms are then used to infer the set of spike templates through nonparametric Bayesian clustering. We use a divide-andconquer strategy to parallelize this clustering step. Finally, we recover collided waveforms with matching-pursuit deconvolution techniques, and perform further split-and-merge steps to estimate additional templates from the pool of recovered waveforms. We apply the new pipeline to data recorded in the primate retina, where high firing rates and highly-overlapping axonal units provide a challenging testbed for the deconvolution approach; in addition, the well-defined mosaic structure of receptive fields in this preparation provides a useful quality check on any spike sorting pipeline. We show that our pipeline improves on the state-of-the-art in spike sorting (and outperforms manual sorting) on both real and semi-simulated MEA data with > 500 electrodes; open source code can be found at https://github.com/paninski-lab/yass. * Equal contribution authors ‡ DARPA Neural Engineering System Design program BAA-16-09 1 datastream as efficiently as possible. Finally, scalability must be a key consideration. To feasibly process the oncoming data deluge, we use parallel, scalable algorithms based on efficient data summarizations wherever possible and focus computational power on the "hard cases," using cheap fast methods to handle easy cases.To evaluate the resulting pipeline, we focus here on MEA data collected from the primate retina. This preparation is a useful spike sorting testbed for several important reasons. First, the two-dimensional MEA used here matches the approximately two-dimensional substrate of the retinal ganglion layer. Second, receptive fields of well-characterized retinal ganglion cell (RGC) types (e.g., ON parasols, OFF midgets, etc.) are known to approximately tile the visual field, providing useful side information for scoring different spike sorting pipelines. Third, many RGCs have moderately high firing rates and often have significant axonal projections that overlap with each other spatially on the MEA, making it challenging to demix spikes that overlap spatially and temporally from different RGCs * .We will first outline the methodology that forms the core of our pipeline in Section 2.1, then provide details of each module in the following subsections, and finally demonstrate the improvements in performance on 512-electrode primat...

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Scalable approximate Bayesian inference for particle tracking data

Cited by 2 publications

References 12 publications

Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training

Fast deep neural correspondence for tracking and identifying neurons in C. elegans using semi-synthetic training

YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array recordings in primate retina

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