Statistical neuroscience in the single trial limit

Williams, Alex H.; Linderman, Scott W.

doi:10.1016/j.conb.2021.10.008

Cited by 16 publications

(10 citation statements)

References 103 publications

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“…There has been a recent push in behavioral studies to account for behavioral events at the resolution of single trials ( 35 ). This is a worthwhile goal, especially in evaluating the predictive performance of behavioral models.…”

Section: Discussionmentioning

confidence: 99%

Mice exhibit stochastic and efficient action switching during probabilistic decision making

Beron

Neufeld

Linderman

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Significance To obtain rewards in changing and uncertain environments, animals must adapt their behavior. We found that mouse choice and trial-to-trial switching behavior in a dynamic and probabilistic two-choice task could be modeled by equivalent theoretical, algorithmic, and descriptive models. These models capture components of evidence accumulation, choice history bias, and stochasticity in mouse behavior. Furthermore, they reveal that mice adapt their behavior in different environmental contexts by modulating their level of stickiness to their previous choice. Despite deviating from the behavior of a theoretically ideal observer, the empirical models achieve comparable levels of near-maximal reward. These results make predictions to guide interrogation of the neural mechanisms underlying flexible decision-making strategies.

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Section: Discussionmentioning

confidence: 99%

Mice exhibit stochastic and efficient action switching during probabilistic decision making

Beron

Neufeld

Linderman

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…However, additional work is needed to better understand the alignment of perceptual/behavioral uncertainty and decoder posterior uncertainty (Panzeri et al, 2017). Models with more accurate descriptions of single neuron variability (Gao et al, 2015; Ghanbari et al, 2019), with nonstationarity (Shanechi et al, 2016; Wei and Stevenson, 2023), additional stimulus/movement nonlinearities (Schwartz and Simoncelli, 2001), state-dependence (Panzeri et al, 2016), and with more complex latent structure (Glaser et al, 2020a; Williams et al, 2020; Sokoloski et al, 2021; Williams and Linderman, 2021) may all show better coverage while maintaining coherence. Our results here indicate that Bayesian decoders of spiking activity are not necessarily well calibrated by default.…”

Section: Discussionmentioning

confidence: 99%

Calibrating Bayesian decoders of neural spiking activity

Wei,

Tajik Mansouri,

Wang

et al. 2023

Preprint

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Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, that provide probabilistic estimates, are some of the most widely used. Here we show how, in many common settings, the probabilistic predictions made by traditional Bayesian decoders are overconfident. That is, the estimates for the decoded stimulus or movement variables are more certain than they should be. We then show how Bayesian decoding with latent variables, taking account of low-dimensional shared variability in the observations, can improve calibration, although additional correction for overconfidence is still needed. We examine: 1) decoding the direction of grating stimuli from spike recordings in primary visual cortex in monkeys, 2) decoding movement direction from recordings in primary motor cortex in monkeys, 3) decoding natural images from multi-region recordings in mice, and 4) decoding position from hippocampal recordings in rats. For each setting we characterize the overconfidence, and we describe a possible method to correct miscalibration post-hoc. Properly calibrated Bayesian decoders may alter theoretical results on probabilistic population coding and lead to brain machine interfaces that more accurately reflect confidence levels when identifying external variables.Significance StatementBayesian decoding is a statistical technique for making probabilistic predictions about external stimuli or movements based on recordings of neural activity. These predictions may be useful for robust brain machine interfaces or for understanding perceptual or behavioral confidence. However, the probabilities produced by these models do not always match the observed outcomes. Just as a weather forecast predicting a 50% chance of rain may not accurately correspond to an outcome of rain 50% of the time, Bayesian decoders of neural activity can be miscalibrated as well. Here we identify and measure miscalibration of Bayesian decoders for neural spiking activity in a range of experimental settings. We compare multiple statistical models and demonstrate how overconfidence can be corrected.

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“…Single-neuron activities, however, exhibited larger variance, sparsity, and randomicity through trials. 116 The commonly used spectrum methods on field potentials decompose different frequency components and analyze the phase lag between brain areas to infer the information flow through the precedence of brain areas. These methods became meaningless on single-neuron traces because the frequency components lack interpretable physical meanings.…”

Section: Functional Mapping and Network Inferencementioning

confidence: 99%

“…Brain activities acquired through these techniques characterize the area-averaged multi-unit neuron activity containing multi-frequency oscillation components, 115 thus are temporally smoother and more stable across trials. Single-neuron activities, however, exhibited larger variance, sparsity, and randomicity through trials 116 . The commonly used spectrum methods on field potentials decompose different frequency components and analyze the phase lag between brain areas to infer the information flow through the precedence of brain areas.…”

Section: Functional Mapping and Network Inferencementioning

confidence: 99%

Review on data analysis methods for mesoscale neural imaging in vivo

Cai

Dai

2022

Neurophoton.

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. Significance: Mesoscale neural imaging in vivo has gained extreme popularity in neuroscience for its capacity of recording large-scale neurons in action. Optical imaging with single-cell resolution and millimeter-level field of view in vivo has been providing an accumulated database of neuron-behavior correspondence. Meanwhile, optical detection of neuron signals is easily contaminated by noises, background, crosstalk, and motion artifacts, while neural-level signal processing and network-level coordinate are extremely complicated, leading to laborious and challenging signal processing demands. The existing data analysis procedure remains unstandardized, which could be daunting to neophytes or neuroscientists without computational background. Aim: We hope to provide a general data analysis pipeline of mesoscale neural imaging shared between imaging modalities and systems. Approach: We divide the pipeline into two main stages. The first stage focuses on extracting high-fidelity neural responses at single-cell level from raw images, including motion registration, image denoising, neuron segmentation, and signal extraction. The second stage focuses on data mining, including neural functional mapping, clustering, and brain-wide network deduction. Results: Here, we introduce the general pipeline of processing the mesoscale neural images. We explain the principles of these procedures and compare different approaches and their application scopes with detailed discussions about the shortcomings and remaining challenges. Conclusions: There are great challenges and opportunities brought by the large-scale mesoscale data, such as the balance between fidelity and efficiency, increasing computational load, and neural network interpretability. We believe that global circuits on single-neuron level will be more extensively explored in the future.

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Statistical neuroscience in the single trial limit

Cited by 16 publications

References 103 publications

Mice exhibit stochastic and efficient action switching during probabilistic decision making

Mice exhibit stochastic and efficient action switching during probabilistic decision making

Calibrating Bayesian decoders of neural spiking activity

Review on data analysis methods for mesoscale neural imaging in vivo

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