Miller Paul
Volen National Center for Complex Systems, Brandeis University, Waltham, MA 02454-9110, USA.
Curr Opin Neurobiol. 2016 Oct;40:14-22. doi: 10.1016/j.conb.2016.05.005. Epub 2016 Jun 16.
Converging evidence from neural, perceptual and simulated data suggests that discrete attractor states form within neural circuits through learning and development. External stimuli may bias neural activity to one attractor state or cause activity to transition between several discrete states. Evidence for such transitions, whose timing can vary across trials, is best accrued through analyses that avoid any trial-averaging of data. One such method, hidden Markov modeling, has been effective in this context, revealing state transitions in many neural circuits during many tasks. Concurrently, modeling efforts have revealed computational benefits of stimulus processing via transitions between attractor states. This review describes the current state of the field, with comments on how its perceived limitations have been addressed.
来自神经、感知和模拟数据的越来越多的证据表明，离散吸引子状态通过学习和发育在神经回路中形成。外部刺激可能会使神经活动偏向一种吸引子状态，或导致活动在几种离散状态之间转换。这种转换的证据，其时间在不同试验中可能会有所不同，最好通过避免对数据进行任何试验平均的分析来积累。一种这样的方法，即隐马尔可夫模型，在这种情况下已经很有效，揭示了许多任务期间许多神经回路中的状态转换。同时，建模工作揭示了通过吸引子状态之间的转换进行刺激处理的计算优势。本综述描述了该领域的当前状态，并对如何解决其被认为的局限性进行了评论。
