RT Journal Article
SR Electronic
T1 TAFKAP: An improved method for probabilistic decoding of cortical activity
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.03.04.433946
DO 10.1101/2021.03.04.433946
A1 R.S. van Bergen
A1 J.F.M. Jehee
YR 2021
UL http://biorxiv.org/content/early/2021/04/16/2021.03.04.433946.abstract
AB Cortical activity can be difficult to interpret. Neural responses to the same stimulus vary between presentations, due to random noise and other sources of variability. This unreliable relationship to external stimuli renders any pattern of activity open to a multitude of plausible interpretations. We have previously shown that this uncertainty in cortical stimulus representations can be characterized using a probabilistic decoding algorithm, which inverts a generative model of stimulus-evoked cortical responses. Here, we improve upon this method in two important ways, which both target the precision with which the generative model can be estimated from limited, noisy training data. We show that these improvements lead to considerably better estimation of the presented stimulus and its associated uncertainty. Estimates of the presented stimulus are recovered with an accuracy that exceeds that of standard decoding methods (SVMs), and in some cases even approaches the behavioral accuracy of human observers. Moreover, the uncertainty in the decoded probability distributions better characterizes the precision of cortical stimulus information from trial to trial.Competing Interest StatementThe authors have declared no competing interest.