Sequential and efficient neural-population coding of complex task information

A crucial component of cortical computation is context: variables that indicate the external state of the world, and the internal state of the animal. However, the need to simultaneously represent many pieces of information in neural activity can also pose computational challenges for neural systems to overcome. We recorded from large neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. How are these variables represented together without crosstalk, and what of their time-dependent relationships to each other? We found that the neural encoding implied that correlated task variables were represented by uncorrelated modes in an information-coding subspace, which can in theory enable optimal decoding directions to be insensitive to neural noise levels. Across posterior cortex, principles of efficient coding thus applied to task-specific information, with neural-population modes as the encoding unit. Remarkably, this encoding function was multiplexed with rapidly changing, sequential neural dynamics, yet reliably followed slow changes in task-variable correlations through time. We can explain this as due to a mathematical property of high-dimensional spaces that the brain might exploit as a temporal scaffold.