The geometry of correlated variability leads to highly suboptimal discriminative sensory coding

Le résumé fourni par la source

The brain represents the world through the activity of neural populations that exhibit correlated variability. We assessed optimality of correlated variability for discriminative sensory coding in diverse datasets by developing two novel null models. Across datasets, correlated variability in neural populations leads to highly suboptimal discriminative sensory coding according to both null models. Biological constraints prevent the neural populations from achieving optimality. Together, these results demonstrate that the geometry of correlated variability leads to highly suboptimal discriminative sensory coding.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.