Different computations over the same inputs produce selective behavior in algorithmic brain networks

Abstract

A key challenge in systems neuroscience remains to understand where, when and how mass brain signals that reflect network activity dynamically represent, transmit and transform sensory information for task behavior. Here, we used the classic XOR, OR and AND that imply a different computation on the same inputs for correct task behavior. We disentangled MEG source activity into three distinct information processes that linearly represents each input before nonlinearly integrating them for task behavior.

Within-participant statistics for cognitive science

“…In neuroimaging, there is renewed interest in the psychophysical approach of longer experiments with fewer subjects [ 8 , 9 ], often combining data over many experimental sessions. Hardware advances such as OPM-MEG and fNIRS allow more participant mobility and more comfortable acquisition of longer sessions.…”