TY - JOUR TI - Stable task information from an unstable neural population AU - Rule, Michael E AU - Loback, Adrianna R AU - Raman, Dhruva V AU - Driscoll, Laura N AU - Harvey, Christopher D AU - O'Leary, Timothy A2 - Palmer, Stephanie A2 - Calabrese, Ronald L VL - 9 PY - 2020 DA - 2020/07/14 SP - e51121 C1 - eLife 2020;9:e51121 DO - 10.7554/eLife.51121 UR - https://doi.org/10.7554/eLife.51121 AB - Over days and weeks, neural activity representing an animal’s position and movement in sensorimotor cortex has been found to continually reconfigure or ‘drift’ during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories, which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. Analyzing long-term calcium imaging recordings from posterior parietal cortex in mice (Mus musculus), we show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioral variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days. KW - spatial navigation KW - learning and memory KW - neural coding KW - computational neuroscience KW - plasticity KW - systems modeling JF - eLife SN - 2050-084X PB - eLife Sciences Publications, Ltd ER -