# Introducing Neuromodulation in Deep Neural Networks to Learn Adaptive Behaviours

arXiv: https://arxiv.org/abs/1812.09113 · HTML: https://mlsanity.com/paper/1812.09113/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Dec 2018.

## Built on by (most cited first, 16 of 16)

- [NEST: A Neuromodulated Small-world Hypergraph Trajectory Prediction Model for Autonomous Driving](https://mlsanity.com/paper/2412.11682/) (2024)
- [Learning to Modulate Random Weights: Neuromodulation-inspired Neural Networks For Efficient Continual Learning](https://mlsanity.com/paper/2204.04297/) (2022)
- [Three-Factor Learning in Spiking Neural Networks: An Overview of Methods and Trends from a Machine Learning Perspective](https://mlsanity.com/paper/2504.05341/) (2025)
- [Churn prediction in online gambling](https://mlsanity.com/paper/2201.02463/) (2022)
- [Improving Performance in Continual Learning Tasks using Bio-Inspired Architectures](https://mlsanity.com/paper/2308.04539/) (2023)
- [Look-Ahead Selective Plasticity for Continual Learning of Visual Tasks](https://mlsanity.com/paper/2311.01617/) (2023)
- [Augmenting learning in neuro-embodied systems through neurobiological first principles](https://mlsanity.com/paper/2407.04525/) (2024)
- [Improving the adaptive and continuous learning capabilities of artificial neural networks: Lessons from multi-neuromodulatory dynamics](https://mlsanity.com/paper/2501.06762/) (2025)
- [Life-inspired Interoceptive Artificial Intelligence for Autonomous and Adaptive Agents](https://mlsanity.com/paper/2309.05999/) (2023)
- [Where Cognition Lives: Dissecting Emergent from Computed Function in a Minimal Complete Cognitive Architecture](https://mlsanity.com/paper/2608.22347/) (2026)
- [Description of Structural Biases and Associated Data in Sensor-Rich Environments](https://mlsanity.com/paper/2104.04885/) (2021)
- [Neuromodulation Gated Transformer](https://mlsanity.com/paper/2305.03232/) (2023)
- [Randomly Weighted Neuromodulation in Neural Networks Facilitates Learning of Manifolds Common Across Tasks](https://mlsanity.com/paper/2401.02437/) (2023)
- [Walking the Weight Manifold: a Topological Approach to Conditioning Inspired by Neuromodulation](https://mlsanity.com/paper/2505.22994/) (2025)
- [Context-dependent manifold learning: A neuromodulated constrained autoencoder approach](https://mlsanity.com/paper/2603.11673/) (2026)
- [Can a Dynamic Internal Field Govern a Transformer's Cognition? Certifiability, not Superiority, in Homeostatic Compute Control](https://mlsanity.com/paper/2608.24319/) (2026)

## Abstract (arXiv)

Animals excel at adapting their intentions, attention, and actions to the environment, making them remarkably efficient at interacting with a rich, unpredictable and ever-changing external world, a property that intelligent machines currently lack. Such an adaptation property relies heavily on cellular neuromodulation, the biological mechanism that dynamically controls intrinsic properties of neurons and their response to external stimuli in a context-dependent manner. In this paper, we take inspiration from cellular neuromodulation to construct a new deep neural network architecture that is specifically designed to learn adaptive behaviours. The network adaptation capabilities are tested on navigation benchmarks in a meta-reinforcement learning context and compared with state-of-the-art approaches. Results show that neuromodulation is capable of adapting an agent to different tasks and that neuromodulation-based approaches provide a promising way of improving adaptation of artificial systems.