# Cortical microcircuits as gated-recurrent neural networks

arXiv: https://arxiv.org/abs/1711.02448 · HTML: https://mlsanity.com/paper/1711.02448/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

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

- [Learning efficient backprojections across cortical hierarchies in real time](https://mlsanity.com/paper/2212.10249/) (2022)
- [Cortico-cerebellar networks as decoupling neural interfaces](https://mlsanity.com/paper/2110.11501/) (2021)
- [Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece](https://mlsanity.com/paper/2105.05382/) (2021)
- [Abrupt and spontaneous strategy switches emerge in simple regularised neural networks](https://mlsanity.com/paper/2302.11351/) (2023)
- [Oja's plasticity rule overcomes several challenges of training neural networks under biological constraints](https://mlsanity.com/paper/2408.08408/) (2024)
- [The brain-AI convergence: Predictive and generative world models for general-purpose computation](https://mlsanity.com/paper/2512.02419/) (2025)

## Abstract (arXiv)

Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas. Such stereotyped structure suggests the existence of common computational principles. However, such principles have remained largely elusive. Inspired by gated-memory networks, namely long short-term memory networks (LSTMs), we introduce a recurrent neural network in which information is gated through inhibitory cells that are subtractive (subLSTM). We propose a natural mapping of subLSTMs onto known canonical excitatory-inhibitory cortical microcircuits. Our empirical evaluation across sequential image classification and language modelling tasks shows that subLSTM units can achieve similar performance to LSTM units. These results suggest that cortical circuits can be optimised to solve complex contextual problems and proposes a novel view on their computational function. Overall our work provides a step towards unifying recurrent networks as used in machine learning with their biological counterparts.