Neuroscience Department, Icahn School of Medicine at Mount Sinai, New York, United States.
Center for Systems Neuroscience, Boston University, Boston, United States.
Elife. 2020 Dec 29;9:e63550. doi: 10.7554/eLife.63550.
While memories are often thought of as flashbacks to a previous experience, they do not simply conserve veridical representations of the past but must continually integrate new information to ensure survival in dynamic environments. Therefore, 'drift' in neural firing patterns, typically construed as disruptive 'instability' or an undesirable consequence of noise, may actually be useful for updating memories. In our view, continual modifications in memory representations reconcile classical theories of stable memory traces with neural drift. Here we review how memory representations are updated through dynamic recruitment of neuronal ensembles on the basis of excitability and functional connectivity at the time of learning. Overall, we emphasize the importance of considering memories not as static entities, but instead as flexible network states that reactivate and evolve across time and experience.
虽然记忆通常被认为是对以前经历的倒叙,但它们不仅仅是过去真实情况的简单保留,还必须不断整合新信息,以确保在动态环境中生存。因此,神经放电模式的“漂移”,通常被认为是具有破坏性的“不稳定性”或噪声的不良后果,实际上可能对更新记忆有用。在我们看来,记忆表征的持续修正调和了稳定记忆痕迹的经典理论与神经漂移。在这里,我们回顾了记忆是如何通过在学习时基于兴奋性和功能连接动态招募神经元集合来进行更新的。总的来说,我们强调了考虑记忆不是作为静态实体,而是作为灵活的网络状态的重要性,这些状态会在时间和经验中重新激活和演变。