A Bayesian method for reducing bias in neural representational similarity analysis
Mingbo Cai · Nicolas W Schuck · Jonathan W Pillow · Yael Niv

Mon Dec 5th 06:00 -- 09:30 PM @ Area 5+6+7+8 #36 #None

In 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 a much less biased 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. Our code is freely available in Brainiak (, a python toolkit for brain imaging analysis.

Author Information

Mingbo Cai (Princeton University)
Nicolas W Schuck (Princeton Neuroscience Institute)
Jonathan W Pillow (Princeton University)
Yael Niv (Princeton University)

Yael Niv received her MA in psychobiology from Tel Aviv University and her PhD from the Hebrew University in Jerusalem, having conducted a major part of her thesis research at the Gatsby Computational Neuroscience Unit in UCL. After a short postdoc at Princeton she became faculty at the Psychology Department and the Princeton Neuroscience Institute. Her lab's research focuses on the neural and computational processes underlying reinforcement learning and decision-making in humans and animals, with a particular focus on representation learning. She recently co-founded the Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry, and is currently taking the research in her lab in the direction of computational psychiatry.

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