RT Journal Article
SR Electronic
T1 Revealing nonlinear neural decoding by analyzing choices
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 332353
DO 10.1101/332353
A1 Qianli Yang
A1 Xaq Pitkow
YR 2018
UL http://biorxiv.org/content/early/2018/05/28/332353.abstract
AB Sensory data about most natural task-relevant variables are confounded by task-irrelevant sensory variations, called nuisance variables. To be useful, the sensory signals that encode the relevant variables must be untangled from the nuisance variables through nonlinear transformations, before the brain can use or decode them to drive behaviors. The information to be untangled is represented in the cortex by the activity of large populations of neurons, constituting a nonlinear population code. Here we provide a new way of thinking about non-linear population codes and nuisance variables, leading to a theory of nonlinear feedforward decoding of neural population activity. This theory obeys fundamental mathematical limitations on information content that are inherited from the sensory periphery, producing redundant codes when there are many more corti-cal neurons than primary sensory neurons. The theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices if the brain uses its nonlinear population codes optimally: more informative patterns should be more correlated with choices.