Data from: The representation of prediction error in auditory cortex

To survive, organisms must extract information from the past that is relevant for their future. How this process is expressed at the neural level remains unclear. We address this problem by developing a novel approach from first principles. We show here how to generate low-complexity representations of the past that produce optimal predictions of future events. We then illustrate this framework by studying the coding of ‘oddball’ sequences in auditory cortex. We find that for many neurons in primary auditory cortex, trial-by-trial fluctuations of neuronal responses correlate with the theoretical prediction error calculated from the short-term past of the stimulation sequence, under constraints on the complexity of the representation of this past sequence. In some neurons, the effect of prediction error accounted for more than 50% of response variability. Reliable predictions often depended on a representation of the sequence of the last ten or more stimuli, although the representation kept only few details of that sequence.

为维持生存，生物体需从过往经历中提取与自身未来相关的信息。这一过程在神经层面的具体表征机制至今仍不明确。我们从第一性原理出发构建全新研究方法，着手解决该问题。本研究阐明了如何生成过往经历的低复杂度表征，以此实现对未来事件的最优预测。随后，我们通过研究听觉皮层（auditory cortex）对“异常序列（oddball sequences）”的编码机制，对该理论框架进行实例阐释。研究发现，在初级听觉皮层（primary auditory cortex）的多数神经元中，神经元反应的逐试次波动与基于该刺激序列短期过往经历计算得到的理论预测误差（prediction error）具有相关性，且该关联受限于过往序列表征的复杂度约束。在部分神经元中，预测误差对响应变异性的解释度超过50%。可靠的预测通常依赖于对最近10个及以上刺激序列的表征，尽管该表征仅保留了该序列的极少细节信息。