# Dendritic predictive coding: A theory of cortical computation with spiking neurons

arXiv: https://arxiv.org/abs/2205.05303 · HTML: https://mlsanity.com/paper/2205.05303/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2022.

## Built on by (most cited first, 12 of 12)

- [Brain-inspired Computational Intelligence via Predictive Coding](https://mlsanity.com/paper/2308.07870/) (2023)
- [A General Framework for Interpretable Neural Learning based on Local Information-Theoretic Goal Functions](https://mlsanity.com/paper/2306.02149/) (2023)
- [Leveraging dendritic properties to advance machine learning and neuro-inspired computing](https://mlsanity.com/paper/2306.08007/) (2023)
- [Bidirectional predictive coding](https://mlsanity.com/paper/2505.23415/) (2025)
- [Normative framework for deriving neural networks with multi-compartmental neurons and non-Hebbian plasticity](https://mlsanity.com/paper/2302.10051/) (2023)
- [What should a neuron aim for? Designing local objective functions based on information theory](https://mlsanity.com/paper/2412.02482/) (2024)
- [Learning beyond sensations: how dreams organize neuronal representations](https://mlsanity.com/paper/2308.01830/) (2023)
- [pyhgf: A neural network library for predictive coding](https://mlsanity.com/paper/2410.09206/) (2024)
- [Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning](https://mlsanity.com/paper/2505.14125/) (2025)
- [Advancing Perception in Artificial Intelligence through Principles of Cognitive Science](https://mlsanity.com/paper/2310.08803/) (2023)
- [Spike-based alignment learning solves the weight transport problem](https://mlsanity.com/paper/2503.02642/) (2025)
- [Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature](https://mlsanity.com/paper/2606.05206/) (2026)

## Abstract (arXiv)

Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experimental evidence for error units, which are central to the theory, is inconclusive, and it remains unclear how hPC can be implemented with spiking neurons. To address this, we connect hPC to existing work on efficient coding in balanced networks with lateral inhibition, and predictive computation at apical dendrites. Together, this work points to an efficient implementation of hPC with spiking neurons, where prediction errors are computed not in separate units, but locally in dendritic compartments. The implied model shows a remarkable correspondence to experimentally observed cortical connectivity patterns, plasticity and dynamics, and at the same time can explain hallmarks of predictive processing, such as mismatch responses, in cortex. We thus propose dendritic predictive coding as one of the main organizational principles of cortex.