# Simulated Language Acquisition in a Biologically Realistic Model of the Brain

arXiv: https://arxiv.org/abs/2507.11788 · HTML: https://mlsanity.com/paper/2507.11788/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 42 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [A Biologically Plausible Parser](https://mlsanity.com/paper/2108.02189/) (2021)
- [The Quest for an Integrated Set of Neural Mechanisms Underlying Object Recognition in Primates](https://mlsanity.com/paper/2312.05956/) (2023)
- [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)
- [Computation with Sequences in a Model of the Brain](https://mlsanity.com/paper/2306.03812/) (2023)
- [Planning with Biological Neurons and Synapses](https://mlsanity.com/paper/2112.08186/) (2021)

## Abstract (arXiv)

Despite tremendous progress in neuroscience, we do not have a compelling narrative for the precise way whereby the spiking of neurons in our brain results in high-level cognitive phenomena such as planning and language. We introduce a simple mathematical formulation of six basic and broadly accepted principles of neuroscience: excitatory neurons, brain areas, random synapses, Hebbian plasticity, local inhibition, and inter-area inhibition. We implement a simulated neuromorphic system based on this formalism, which is capable of basic language acquisition: Starting from a tabula rasa, the system learns, in any language, the semantics of words, their syntactic role (verb versus noun), and the word order of the language, including the ability to generate novel sentences, through the exposure to a modest number of grounded sentences in the same language. We discuss several possible extensions and implications of this result.