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.

该研究针对机器学习和认知神经科学中的中心课题提出了一种形式化的神经活动模型，该模型可以通过神经元集合的创建和操作实现某些简单的认知操作，并具有较强的泛化、鲁棒性和快速性，还可以捕捉时间和空间序列，实现通用计算。