RT Journal Article
SR Electronic
T1 Calibrating Bayesian Decoders of Neural Spiking Activity
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP e2158232024
DO 10.1523/JNEUROSCI.2158-23.2024
VO 44
IS 18
A1 Wei (魏赣超), Ganchao
A1 Tajik Mansouri (زینب تاجیک منصوری), Zeinab
A1 Wang (王晓婧), Xiaojing
A1 Stevenson, Ian H.
YR 2024
UL http://www.jneurosci.org/content/44/18/e2158232024.abstract
AB Accurately decoding external variables from observations of neural activity is a major challenge in systems neuroscience. Bayesian decoders, which 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. Using data from males, we examine (1) decoding the direction of grating stimuli from spike recordings in the primary visual cortex in monkeys, (2) decoding movement direction from recordings in the primary motor cortex in monkeys, (3) decoding natural images from multiregion 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.