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.

生物体为维持生存，必须从过往经历中提取与未来相关的信息。这一过程在神经层面的具体表达机制目前仍不明确。我们基于第一性原理（first principles）开发了一种全新方法以解决该问题。本文阐明了如何生成过往经历的低复杂度表征，从而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）对偏差（oddball）序列的编码过程，对该框架进行了实例验证。研究发现，初级听觉皮层（primary auditory cortex）的多数神经元中，其反应的逐试次波动与基于刺激序列短期过往经历计算得到的理论预测误差存在相关性，且该过往序列的表征需满足复杂度约束条件。在部分神经元中，预测误差对反应变异性的解释占比超过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少量细节。