RT Journal Article SR Electronic T1 Population adaptation in efficient balanced networks JF bioRxiv FD Cold Spring Harbor Laboratory SP 211748 DO 10.1101/211748 A1 Gabrielle J. Gutierrez A1 Sophie Denève YR 2018 UL http://biorxiv.org/content/early/2018/11/01/211748.abstract AB Adaptation is a key component of efficient coding in sensory neurons. However, it remains unclear how neurons can provide a stable representation of external stimuli given their history-dependent responses. Here we show that a stable representation is maintained if efficiency is optimized by a population of neurons rather than by neurons individually. We show that spike-frequency adaptation and E/I balanced recurrent connectivity emerge as solutions to a global cost-accuracy tradeoff. The network will re-distribute sensory responses from highly excitable neurons to less excitable neurons as the cost of neural activity increases. This does not change the representation at the population level, despite causing dynamic changes in individual neurons. By applying this framework to an orientation coding network, we reconcile neural and behavioral findings. Our approach underscores the common mechanisms behind the diversity of neural adaptation and its role in producing a reliable representation of the stimulus while minimizing metabolic cost.