Correction: Standardized and reproducible measurement of decision-making in mice

Aguillon, Valeria; Angelaki, Dora E.; Bayer, H; Bonacchi, Niccolò; Carandini, Matteo; Cazettes, Fanny; Chapuis, Gaelle; Churchland, Anne K.; Dan, Yang; DeWitt, Eric; Faulkner, Mayo; Forrest, Hamish; Haetzel, Laura; Häusser, Michael; Hofer, Sonja B.; Hu, Fei; Khanal, Anup; Krasniak, Christopher; Laranjeira, Inês; Mainen, Zachary F.; Meijer, Guido T.; Miska, Nathaniel; Mrsic-Flogel, Thomas D.; Murakami, Masayoshi; Noel, Jean‐Paul; Pan-Vazquez, Alejandro; Rossant, Cyrille; Sanders, Joshua I.; Socha, Karolina; Terry, Robert M.; Urai, Anne E; Vergara, Hernando; Wells, Miles J.; Wilson, C.; Witten, Ilana B.; Wool, Lauren; Zador, Anthony M.

doi:10.7554/elife.84310

Cited by 2 publications

(2 citation statements)

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“…We conducted experiments using both electrophysiological and behavior data obtained from the International Brain Laboratory (IBL) (IBL et al 2021). The electrophysiological recordings were acquired using Neuropixels (NP) probes that were implanted in mice performing a decision-making task.…”

Section: Methodsmentioning

confidence: 99%

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes

Zhang,

He,

Boussard

et al. 2023

Preprint

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Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting, the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting.

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Section: Methodsmentioning

confidence: 99%

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes

Zhang,

He,

Boussard

et al. 2023

Preprint

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show abstract

“…This dataset is publicly available on figshare at this link https://doi.org/10.6084/m9.figshare.11636748. We applied the GLM-HMM model to this data, and you can find more details in this paper [33].…”

Section: Datasetmentioning

confidence: 99%

Identifying the factors governing internal state switches during nonstationary sensory decision-making

Mohammadi,

Ashwood,

Pillow

2024

Preprint

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Recent work has revealed that mice do not rely on a stable strategy during perceptual decision-making, but switch between multiple strategies within a single session [1, 2]. However, this switching behavior has not yet been characterized in non-stationary environments, and the factors that govern switching remain unknown. Here we address these questions using an internal state model with input-driven transitions. Our approach relies on a hidden Markov model (HMM) with two sets of per-state generalized linear models (GLMs): a set of Bernoulli GLMs for modeling the animal state- and stimulus-dependent choice on each trial, and a multinomial GLM for modeling input-dependent transitions between states. We used this model to analyze a dataset from the International Brain Laboratory (IBL), in which mice performed a binary decision-making task with non-stationary stimulus statistics. We found that mouse behavior in this task was accurately described by a four-state model. This model contained two "engaged" states, in which performance was good despite slight left and right biases, and two "disengaged" states, where performance was low and exhibited with larger left and right biases, respectively. Our analyses revealed that mice preferentially used left-bias strategies during left-bias stimulus blocks, and right-bias strategies during right-bias stimulus blocks, meaning that they could achieve reasonably high performance even in disengaged states simply by biasing choice toward the side with greater prior probability. Our model showed that past choices and past stimuli predicted transitions between left- and right-bias states, while past rewards predicted transitions between engaged and disengaged states. In particular, greater past reward predicted transition to disengaged states, suggesting that disengagement may be associated with satiety.

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Correction: Standardized and reproducible measurement of decision-making in mice

Cited by 2 publications

References 0 publications

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes

Identifying the factors governing internal state switches during nonstationary sensory decision-making

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