ARNI Biological Learning Working Group

Title: Brain-like learning with exponentiated gradients and Learning to live with Dale’s principle: ANNs with separate excitatory and inhibitory units

Meeting Summary: Our focus will be on answering the following question, which may be a focus for the next few meetings: To what degree are different learning algorithms entangled with a particular neural architecture? Can we find neural architectures that interact better with certain learning algorithms?

Meeting link: http://meet.google.com/stu-ozga-syi

More about the Biological Learning Working Group: The biological learning WG is interested in better understanding how biological neural networks perform credit assignment (i.e. how they determine which synapses should change to get better at a task). The success of credit assignment algorithms in AI, such as backpropagation-of-error, have revealed that the traditional Hebbian plasticity rules used in computational neuroscience were not nearly as powerful as is possible for learning in distributed networks. This has spurred a new field of research in neuroscience that seeks to uncover the mechanisms used for credit assignment in the brain, as many researchers expect they are quite powerful, similar to those used in AI. The goal of this WG is to explore this new field of research and consider new potential directions for explaining credit assignment in the brain. Additionally, this could inspire new mechanisms for credit assignment in AI that are more efficient from an energy perspective than backpropagation-of-error.