Even as machine learning exceeds human-level performance on many applications, the generality, robustness, and rapidity of the brain's learning capabilities remain unmatched. How cognition arises from neural activity is a central open question in neuroscience, inextricable from the study of intelligence itself. A simple formal model of neural activity was proposed in Papadimitriou [2020] and has been subsequently shown, through both mathematical proofs and simulations, to be capable of implementing certain simple cognitive operations via the creation and manipulation of assemblies of neurons. However, many intelligent behaviors rely on the ability to recognize, store, and manipulate temporal sequences of stimuli (planning, language, navigation, to list a few). Here we show that, in the same model, time can be captured naturally as precedence through synaptic weights and plasticity, and, as a result, a range of computations on sequences of assemblies can be carried out. In particular, repeated presentation of a sequence of stimuli leads to the memorization of the sequence through corresponding neural assemblies: upon future presentation of any stimulus in the sequence, the corresponding assembly and its subsequent ones will be activated, one after the other, until the end of the sequence. Finally, we show that any finite state machine can be learned in a similar way, through the presentation of appropriate patterns of sequences. Through an extension of this mechanism, the model can be shown to be capable of universal computation. We support our analysis with a number of experiments to probe the limits of learning in this model in key ways. Taken together, these results provide a concrete hypothesis for the basis of the brain's remarkable abilities to compute and learn, with sequences playing a vital role.

翻译：尽管机器学习在许多应用中的表现已超越人类水平，但大脑学习能力的通用性、鲁棒性和快速性仍无可匹敌。神经活动如何产生认知是神经科学的核心未解之谜，与智能研究本身密不可分。Papadimitriou [2020]提出了一种简单的神经活动形式化模型，后续通过数学证明和模拟表明，该模型能够通过创建和操纵神经元集群来实现某些简单的认知操作。然而，许多智能行为依赖于识别、存储和操作刺激的时间序列（例如规划、语言、导航等）。本文证明，在该模型中，时间可以通过突触权重和可塑性自然地体现为优先级，从而能够对序列集群执行一系列计算。特别地，重复呈现刺激序列会导致通过相应神经集群对序列进行记忆：当未来呈现序列中的任一刺激时，对应的集群及其后续集群将依次激活，直至序列结束。最后，我们证明任何有限状态机均可通过呈现适当的序列模式以类似方式学习。通过扩展该机制，该模型能够实现通用计算。我们通过一系列关键实验验证了该模型中学习的极限。综合来看，这些结果为大脑卓越的计算与学习能力（其中序列扮演关键角色）提供了具体假设基础。