# Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments

arXiv: https://arxiv.org/abs/2201.00042 · HTML: https://mlsanity.com/paper/2201.00042/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 106 references that are in the map.
- First posted on arXiv 31 Dec 2021.

## Builds on (most cited first, 30 of 37)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Gradient Surgery for Multi-Task Learning](https://mlsanity.com/paper/2001.06782/) (2020)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks](https://mlsanity.com/paper/1711.02257/) (2017)
- [Multi-Task Learning as Multi-Objective Optimization](https://mlsanity.com/paper/1810.04650/) (2018)
- [An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks](https://mlsanity.com/paper/1312.6211/) (2013)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [Policy Distillation](https://mlsanity.com/paper/1511.06295/) (2015)
- [The Natural Language Decathlon: Multitask Learning as Question Answering](https://mlsanity.com/paper/1806.08730/) (2018)
- [Supermasks in Superposition](https://mlsanity.com/paper/2006.14769/) (2020)
- [How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned](https://mlsanity.com/paper/2102.02915/) (2021)
- [Modular Multitask Reinforcement Learning with Policy Sketches](https://mlsanity.com/paper/1611.01796/) (2016)
- [Attentive Single-Tasking of Multiple Tasks](https://mlsanity.com/paper/1904.08918/) (2019)
- [Multi-Task Reinforcement Learning with Soft Modularization](https://mlsanity.com/paper/2003.13661/) (2020)
- [Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer](https://mlsanity.com/paper/1609.07088/) (2016)
- [Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning](https://mlsanity.com/paper/1711.01239/) (2017)
- [Task-Driven Modular Networks for Zero-Shot Compositional Learning](https://mlsanity.com/paper/1905.05908/) (2019)
- [Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization](https://mlsanity.com/paper/1802.01569/) (2018)
- [Adapting Auxiliary Losses Using Gradient Similarity](https://mlsanity.com/paper/1812.02224/) (2018)

## Built on by (most cited first, 25 of 25)

- [Continual Learning of Diffusion Models with Generative Distillation](https://mlsanity.com/paper/2311.14028/) (2023)
- [Sparse Coding in a Dual Memory System for Lifelong Learning](https://mlsanity.com/paper/2301.05058/) (2022)
- [Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals](https://mlsanity.com/paper/2203.11560/) (2022)
- [Sparse Distributed Memory is a Continual Learner](https://mlsanity.com/paper/2303.11934/) (2023)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Continual Learning of Dynamical Systems with Competitive Federated Reservoir Computing](https://mlsanity.com/paper/2206.13336/) (2022)
- [Leveraging dendritic properties to advance machine learning and neuro-inspired computing](https://mlsanity.com/paper/2306.08007/) (2023)
- [Breaking Neural Network Scaling Laws with Modularity](https://mlsanity.com/paper/2409.05780/) (2024)
- [Artificial Neuronal Ensembles with Learned Context Dependent Gating](https://mlsanity.com/paper/2301.07187/) (2023)
- [Theories of synaptic memory consolidation and intelligent plasticity for continual learning](https://mlsanity.com/paper/2405.16922/) (2024)
- [Replay4NCL: An Efficient Memory Replay-based Methodology for Neuromorphic Continual Learning in Embedded AI Systems](https://mlsanity.com/paper/2503.17061/) (2025)
- [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)
- [Foundations of a Developmental Design Paradigm for Integrated Continual Learning, Deliberative Behavior, and Comprehensibility](https://mlsanity.com/paper/2502.13935/) (2025)
- [Contrastive Consolidation of Top-Down Modulations Achieves Sparsely Supervised Continual Learning](https://mlsanity.com/paper/2505.14125/) (2025)
- [A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning](https://mlsanity.com/paper/2304.06738/) (2023)
- [The Role Of Biology In Deep Learning](https://mlsanity.com/paper/2209.04425/) (2022)
- [Context selectivity with dynamic availability enables lifelong continual learning](https://mlsanity.com/paper/2306.01690/) (2023)
- [Mitigating Communication Costs in Neural Networks: The Role of Dendritic Nonlinearity](https://mlsanity.com/paper/2306.11950/) (2023)
- [Task agnostic continual learning with Pairwise layer architecture](https://mlsanity.com/paper/2405.13632/) (2024)
- [Kernel Operator-Theoretic Bayesian Filter for Nonlinear Dynamical Systems](https://mlsanity.com/paper/2411.00198/) (2024)
- [Multitask Reinforcement Learning for Quadcopter Attitude Stabilization and Tracking using Graph Policy](https://mlsanity.com/paper/2503.08259/) (2025)
- [Evolutionary Developmental Biology Can Serve as the Conceptual Foundation for a New Design Paradigm in Artificial Intelligence](https://mlsanity.com/paper/2506.12891/) (2025)
- [Updating the standard neuron model in artificial neural networks](https://mlsanity.com/paper/2605.30370/) (2026)
- [Replay in the Silent Degrees of Freedom: Continual Learning Without an Offline Phase](https://mlsanity.com/paper/2609.31630/) (2026)

## Abstract (arXiv)

A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios. In these settings, error signals from multiple contexts can interfere with one another, ultimately leading to a phenomenon known as catastrophic forgetting. In this article we investigate biologically inspired architectures as solutions to these problems. Specifically, we show that the biophysical properties of dendrites and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. Our key contributions are as follows. First, we propose a novel artificial neural network architecture that incorporates active dendrites and sparse representations into the standard deep learning framework. Next, we study the performance of this architecture on two separate benchmarks requiring task-based adaptation: Meta-World, a multi-task reinforcement learning environment where a robotic agent must learn to solve a variety of manipulation tasks simultaneously; and a continual learning benchmark in which the model's prediction task changes throughout training. Analysis on both benchmarks demonstrates the emergence of overlapping but distinct and sparse subnetworks, allowing the system to fluidly learn multiple tasks with minimal forgetting. Our neural implementation marks the first time a single architecture has achieved competitive results on both multi-task and continual learning settings. Our research sheds light on how biological properties of neurons can inform deep learning systems to address dynamic scenarios that are typically impossible for traditional ANNs to solve.