# The computational role of structure in neural activity and connectivity

arXiv: https://arxiv.org/abs/2308.16772 · HTML: https://mlsanity.com/paper/2308.16772/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2023.

## Built on by (most cited first, 6 of 6)

- [From Lazy to Rich: Exact Learning Dynamics in Deep Linear Networks](https://mlsanity.com/paper/2409.14623/) (2024)
- [Disentangling Representations through Multi-task Learning](https://mlsanity.com/paper/2407.11249/) (2024)
- [The Global Empirical NTK: Self-Referential Bias and Dimensionality of Gradient Descent Learning](https://mlsanity.com/paper/2605.08746/) (2026)
- [Unveiling and Steering Connectome Organization with Interpretable Latent Variables](https://mlsanity.com/paper/2505.13011/) (2025)
- [JEDI: Jointly Embedded Inference of Neural Dynamics](https://mlsanity.com/paper/2603.10489/) (2026)
- [Winner-Take-All bottlenecks enforce disentangled symbolic representations in multi-task learning](https://mlsanity.com/paper/2605.22472/) (2026)

## Abstract (arXiv)

One major challenge of neuroscience is finding interesting structures in a seemingly disorganized neural activity. Often these structures have computational implications that help to understand the functional role of a particular brain area. Here we outline a unified approach to characterize these structures by inspecting the representational geometry and the modularity properties of the recorded activity, and show that this approach can also reveal structures in connectivity. We start by setting up a general framework for determining geometry and modularity in activity and connectivity and relating these properties with computations performed by the network. We then use this framework to review the types of structure found in recent works on model networks performing three classes of computations.