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A Bayesian method for reducing bias in neural representational similarity analysis

View ORCID ProfileMing Bo Cai, Nicolas W. Schuck, Jonathan Pillow, Yael Niv
doi: https://doi.org/10.1101/073932
Ming Bo Cai
1Princeton Neuroscience Institute Princeton University Princeton, NJ 08544
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  • For correspondence: mcai@princeton.edu
Nicolas W. Schuck
2Princeton Neuroscience Institute Princeton University Princeton, NJ 08544
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  • For correspondence: nschuck@princeton.edu
Jonathan Pillow
3Princeton Neuroscience Institute Princeton University Princeton, NJ 08544
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  • For correspondence: pillow@princeton.edu
Yael Niv
4Princeton Neuroscience Institute Princeton University Princeton, NJ 08544
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  • For correspondence: yael@princeton.edu
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Abstract

1In neuroscience, the similarity matrix of neural activity patterns in response to different sensory stimuli or under different cognitive states reflects the structure of neural representational space. Existing methods derive point estimations of neural activity patterns from noisy neural imaging data, and the similarity is calculated from these point estimations. We show that this approach translates structured noise from estimated patterns into spurious bias structure in the resulting similarity matrix, which is especially severe when signal-to-noise ratio is low and experimental conditions cannot be fully randomized in a cognitive task. We propose an alternative Bayesian framework for computing representational similarity in which we treat the covariance structure of neural activity patterns as a hyper-parameter in a generative model of the neural data, and directly estimate this covariance structure from imaging data while marginalizing over the unknown activity patterns. Converting the estimated covariance structure into a correlation matrix offers an unbiased estimate of neural representational similarity. Our method can also simultaneously estimate a signal-to-noise map that informs where the learned representational structure is supported more strongly, and the learned covariance matrix can be used as a structured prior to constrain Bayesian estimation of neural activity patterns.

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Posted September 07, 2016.
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A Bayesian method for reducing bias in neural representational similarity analysis
Ming Bo Cai, Nicolas W. Schuck, Jonathan Pillow, Yael Niv
bioRxiv 073932; doi: https://doi.org/10.1101/073932
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A Bayesian method for reducing bias in neural representational similarity analysis
Ming Bo Cai, Nicolas W. Schuck, Jonathan Pillow, Yael Niv
bioRxiv 073932; doi: https://doi.org/10.1101/073932

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