# Do biological constraints impair dendritic computation?

arXiv: https://arxiv.org/abs/2103.03274 · HTML: https://mlsanity.com/paper/2103.03274/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks](https://mlsanity.com/paper/2306.16922/) (2023)
- [Connectionism, Complexity, and Living Systems: a comparison of Artificial and Biological Neural Networks](https://mlsanity.com/paper/2103.15553/) (2021)

## Abstract (arXiv)

Computations on the dendritic trees of neurons have important constraints. Voltage dependent conductances in dendrites are not similar to arbitrary direct-current generation, they are the basis for dendritic nonlinearities and they do not allow converting positive currents into negative currents. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong, we do not know if that computational strength is preserved under these biological constraints. Here we simulate models of dendritic computation with and without these constraints. We find that dendritic model performance on interesting machine learning tasks is not hurt by these constraints but may benefit from them. Our results suggest that single real dendritic trees may be able to learn a surprisingly broad range of tasks.