Humans and other animals are able to quickly generalize latent dynamics of spatiotemporal sequences, often from a minimal number of previous experiences. Additionally, internal representations of external stimuli must remain stable, even in the presence of sensory noise, in order to be useful for informing behavior. In contrast, typical machine learning approaches require many thousands of samples, and generalize poorly to unexperienced examples, or fail completely to predict at long timescales. Here, we propose a novel neural network module which incorporates hierarchy and recurrent feedback terms, constituting a simplified model of neocortical microcircuits. This microcircuit predicts spatiotemporal trajectories at the input layer using a temporal error minimization algorithm. We show that this module is able to predict with higher accuracy into the future compared to traditional models. Investigating this model we find that successive predictive models learn representations which are increasingly removed from the raw sensory space, namely as successive temporal derivatives of the positional information. Next, we introduce a spiking neural network model which implements the rate-model through the use of a recently proposed biological learning rule utilizing dual-compartment neurons. We show that this network performs well on the same tasks as the mean-field models, by developing intrinsic dynamics that follow the dynamics of the external stimulus, while coordinating transmission of higher-order dynamics. Taken as a whole, these findings suggest that hierarchical temporal abstraction of sequences, rather than feed-forward reconstruction, may be responsible for the ability of neural systems to quickly adapt to novel situations.

人类和其他动物能够快速概括时空序列的潜在动态，通常来自最少数量的先前经验。此外，即使存在感官噪声，外部刺激的内部表征也必须保持稳定，以便有助于告知行为。相比之下，典型的机器学习方法需要数千个样本，并且对于没有经验的示例的泛化能力很差，或者完全无法在长时间尺度上进行预测。在这里，我们提出了一种新颖的神经网络模块，它结合了层次结构和循环反馈项，构成了新皮质微电路的简化模型。该微电路使用时间误差最小化算法来预测输入层的时空轨迹。我们证明，与传统模型相比，该模块能够以更高的准确度预测未来。通过研究这个模型，我们发现连续的预测模型学习的表示越来越多地从原始感觉空间中移除，即作为位置信息的连续时间导数。接下来，我们介绍一种尖峰神经网络模型，该模型通过使用最近提出的利用双室神经元的生物学习规则来实现速率模型。我们表明，通过开发遵循外部刺激动力学的内在动力学，同时协调高阶动力学的传输，该网络在与平均场模型相同的任务上表现良好。总的来说，这些发现表明序列的分层时间抽象，而不是前馈重建，可能是神经系统快速适应新情况的能力的原因。