# Computation with Sequences in a Model of the Brain

arXiv: https://arxiv.org/abs/2306.03812 · HTML: https://mlsanity.com/paper/2306.03812/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 40 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [Dendritic cortical microcircuits approximate the backpropagation algorithm](https://mlsanity.com/paper/1810.11393/) (2018)
- [Towards deep learning with segregated dendrites](https://mlsanity.com/paper/1610.00161/) (2016)
- [Continuous online sequence learning with an unsupervised neural network model](https://mlsanity.com/paper/1512.05463/) (2015)
- [A Biologically Plausible Parser](https://mlsanity.com/paper/2108.02189/) (2021)
- [Dendritic error backpropagation in deep cortical microcircuits](https://mlsanity.com/paper/1801.00062/) (2017)
- [Assemblies of neurons learn to classify well-separated distributions](https://mlsanity.com/paper/2110.03171/) (2021)
- [Center-Embedding and Constituency in the Brain and a New Characterization of Context-Free Languages](https://mlsanity.com/paper/2206.13217/) (2022)

## Built on by (most cited first, 3 of 3)

- [Simulated Language Acquisition in a Biologically Realistic Model of the Brain](https://mlsanity.com/paper/2507.11788/) (2025)
- [Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks](https://mlsanity.com/paper/2509.10077/) (2025)
- [Causal Learning with Neural Assemblies](https://mlsanity.com/paper/2604.26919/) (2026)

## Abstract (arXiv)

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.