# Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks

arXiv: https://arxiv.org/abs/1703.10371 · HTML: https://mlsanity.com/paper/1703.10371/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2017.

## Built on by (most cited first, 29 of 29)

- [An Investigation of Replay-based Approaches for Continual Learning](https://mlsanity.com/paper/2108.06758/) (2021)
- [Brain-inspired learning in artificial neural networks: a review](https://mlsanity.com/paper/2305.11252/) (2023)
- [Lifelong Reinforcement Learning with Modulating Masks](https://mlsanity.com/paper/2212.11110/) (2022)
- [Meta-Learning Bidirectional Update Rules](https://mlsanity.com/paper/2104.04657/) (2021)
- [Sharing Lifelong Reinforcement Learning Knowledge via Modulating Masks](https://mlsanity.com/paper/2305.10997/) (2023)
- [Brain-inspired Artificial Intelligence: A Comprehensive Review](https://mlsanity.com/paper/2408.14811/) (2024)
- [Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?](https://mlsanity.com/paper/2101.00232/) (2021)
- [Overcoming Catastrophic Forgetting with Gaussian Mixture Replay](https://mlsanity.com/paper/2104.09220/) (2021)
- [Self-Activating Neural Ensembles for Continual Reinforcement Learning](https://mlsanity.com/paper/2301.00141/) (2022)
- [Continual Learning with Fully Probabilistic Models](https://mlsanity.com/paper/2104.09240/) (2021)
- [Short-Term Plasticity Neurons Learning to Learn and Forget](https://mlsanity.com/paper/2206.14048/) (2022)
- [Evolving Decomposed Plasticity Rules for Information-Bottlenecked Meta-Learning](https://mlsanity.com/paper/2109.03554/) (2021)
- [Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning](https://mlsanity.com/paper/2112.08588/) (2021)
- [Minimal Neural Network Models for Permutation Invariant Agents](https://mlsanity.com/paper/2205.07868/) (2022)
- [Exploring the Intersection between Neural Architecture Search and Continual Learning](https://mlsanity.com/paper/2206.05625/) (2022)
- [The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning](https://mlsanity.com/paper/2302.10887/) (2023)
- [Evolving Self-Assembling Neural Networks: From Spontaneous Activity to Experience-Dependent Learning](https://mlsanity.com/paper/2406.09787/) (2024)
- [Epigenetic opportunities for Evolutionary Computation](https://mlsanity.com/paper/2108.04546/) (2021)
- [Learning to Act through Evolution of Neural Diversity in Random Neural Networks](https://mlsanity.com/paper/2305.15945/) (2023)
- [Evolved Developmental Artificial Neural Networks for Multitasking with Advanced Activity Dependence](https://mlsanity.com/paper/2407.10359/) (2024)
- [Self-building Neural Networks](https://mlsanity.com/paper/2304.01086/) (2023)
- [Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots](https://mlsanity.com/paper/2503.12406/) (2025)
- [Learning and evolution: factors influencing an effective combination](https://mlsanity.com/paper/2306.11761/) (2023)
- [A Versatile Hub Model For Efficient Information Propagation And Feature Selection](https://mlsanity.com/paper/2307.02398/) (2023)
- [Neuron-centric Hebbian Learning](https://mlsanity.com/paper/2403.12076/) (2024)
- [Breaching the Bottleneck: Evolutionary Transition from Reward-Driven Learning to Reward-Agnostic Domain-Adapted Learning in Neuromodulated Neural Nets](https://mlsanity.com/paper/2404.12631/) (2024)
- [Personality Requires Struggle: Three Regimes of the Baldwin Effect in Neuroevolved Chess Agents](https://mlsanity.com/paper/2604.03565/) (2026)
- [Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity](https://mlsanity.com/paper/2606.20817/) (2026)
- [Environmental requirements for the use of social information by artificial life agents using evolved plastic artificial neural networks](https://mlsanity.com/paper/2609.35018/) (2026)

## Abstract (arXiv)

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented.