# Dendritic error backpropagation in deep cortical microcircuits

arXiv: https://arxiv.org/abs/1801.00062 · HTML: https://mlsanity.com/paper/1801.00062/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Dec 2017.

## Built on by (most cited first, 4 of 4)

- [Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons](https://mlsanity.com/paper/2110.14549/) (2021)
- [Assemblies of neurons learn to classify well-separated distributions](https://mlsanity.com/paper/2110.03171/) (2021)
- [Computation with Sequences in a Model of the Brain](https://mlsanity.com/paper/2306.03812/) (2023)
- [Guiding Sparse Neural Networks with Neurobiological Principles to Elicit Biologically Plausible Representations](https://mlsanity.com/paper/2603.03234/) (2026)

## Abstract (arXiv)

Animal behaviour depends on learning to associate sensory stimuli with the desired motor command. Understanding how the brain orchestrates the necessary synaptic modifications across different brain areas has remained a longstanding puzzle. Here, we introduce a multi-area neuronal network model in which synaptic plasticity continuously adapts the network towards a global desired output. In this model synaptic learning is driven by a local dendritic prediction error that arises from a failure to predict the top-down input given the bottom-up activities. Such errors occur at apical dendrites of pyramidal neurons where both long-range excitatory feedback and local inhibitory predictions are integrated. When local inhibition fails to match excitatory feedback an error occurs which triggers plasticity at bottom-up synapses at basal dendrites of the same pyramidal neurons. We demonstrate the learning capabilities of the model in a number of tasks and show that it approximates the classical error backpropagation algorithm. Finally, complementing this cortical circuit with a disinhibitory mechanism enables attention-like stimulus denoising and generation. Our framework makes several experimental predictions on the function of dendritic integration and cortical microcircuits, is consistent with recent observations of cross-area learning, and suggests a biological implementation of deep learning.