Decoding neural responses with minimal information loss

Cortical tissue has a circuit motif termed the cortical column, which is thought to represent its basic computational unit but whose function remains unclear. Here we propose, and show quantitative evidence, that the cortical column performs computations necessary to decode incoming neural activity with minimal information loss. The cortical decoder achieves higher accuracy compared to simpler decoders found in invertebrate and subcortical circuits by incorporating specific recurrent network dynamics. This recurrent dynamics also makes it possible to choose between alternative stimulus categories. The structure of cortical decoder predicts quadratic dependence of cortex size relative to subcortical parts of the brain. We quantitatively verify this relationship using anatomical data across mammalian species. The results offer a new perspective on the evolution and computational function of cortical columns.