Do biological constraints impair dendritic computation?

TL;DR

This paper investigates whether biological constraints on dendritic computations limit their ability to perform complex tasks, finding that such constraints do not impair and may even enhance computational performance.

Contribution

The study demonstrates through simulations that biological constraints on dendritic conductances do not reduce, and may improve, the computational capabilities of dendritic trees.

Findings

Dendritic models perform well on machine learning tasks despite biological constraints.

Constraints do not impair, and may enhance, dendritic computational performance.

Single dendritic trees can learn a broad range of tasks.

Abstract

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.