Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity


    The impressive lifelong learning in animal brains is primarily enabled by plastic changes in synaptic connectivity. Importantly, these changes are not passive, but are actively controlled by neuromodulation, which is itself under the control of the brain. The resulting self-modifying abilities of the brain play an important role in learning and adaptation, and are a major basis for biological reinforcement learning. Here we show for the first time that artificial neural networks with such neuromodulated plasticity can be trained with gradient descent. Extending previous work on differentiable Hebbian plasticity, we propose a differentiable formulation for the neuromodulation of plasticity. We show that neuromodulated plasticity improves the performance of neural networks on both reinforcement learning and supervised learning tasks. In one task, neuromodulated plastic LSTMs with millions of parameters outperform standard LSTMs on a benchmark language modeling task (controlling for the number of parameters). We conclude that differentiable neuromodulation of plasticity offers a powerful new framework for training neural networks.


    Thomas Miconi, Aditya Rawal, Jeff Clune, Kenneth O. Stanley


    ICLR 2019

    Full Paper

    ‘Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity’ (PDF)

    Uber AI

    Previous articleLSQ++: lower running time and higher recall in multi-codebook quantization
    Next articleSingle Image Intrinsic Decomposition Without a Single Intrinsic Image
    Aditya Rawal
    Aditya Rawal is a research scientist at Uber AI Labs. His interests lies at the convergence of two research fields - neuroevolution and deep learning. His belief is that evolutionary search can replace human ingenuity in creating next generation of deep networks. Previously, Aditya received his MS/PhD in Computer Science from University of Texas at Austin, advised by Prof. Risto Miikkulainen. During his PhD, he developed neuroevolution algorithms to evolve recurrent architectures for sequence-prediction problems and construct multi-agent systems that cooperate, compete and communicate.
    Jeff Clune
    Jeff Clune is the former Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming, a Senior Research Manager and founding member of Uber AI Labs, and currently a Research Team Leader at OpenAI. Jeff focuses on robotics and training neural networks via deep learning and deep reinforcement learning. He has also researched open questions in evolutionary biology using computational models of evolution, including studying the evolutionary origins of modularity, hierarchy, and evolvability. Prior to becoming a professor, he was a Research Scientist at Cornell University, received a PhD in computer science and an MA in philosophy from Michigan State University, and received a BA in philosophy from the University of Michigan. More about Jeff’s research can be found at
    Kenneth O. Stanley
    Before joining Uber AI Labs full time, Ken was an associate professor of computer science at the University of Central Florida (he is currently on leave). He is a leader in neuroevolution (combining neural networks with evolutionary techniques), where he helped invent prominent algorithms such as NEAT, CPPNs, HyperNEAT, and novelty search. His ideas have also reached a broader audience through the recent popular science book, Why Greatness Cannot Be Planned: The Myth of the Objective.