Continual Learning in a Multi-Layer Network of an Electric Fish

Distributing learning across multiple layers has proven extremely powerful in artificial neural networks. However, little is known about how multi-layer learning is implemented in the brain. Here, we provide an account of learning across multiple processing layers in the electrosensory lobe (ELL) of mormyrid fish and report how it solves problems well known from machine learning. Because the ELL operates and learns continuously, it must reconcile learning and signaling functions without switching its mode of operation. We show that this is accomplished through a functional compartmentalization within intermediate layer neurons in which inputs driving learning differentially affect dendritic and axonal spikes. We also find that connectivity based on learning rather than sensory response selectivity assures that plasticity at synapses onto intermediate-layer neurons is matched to the requirements of output neurons. The mechanisms we uncover have relevance to learning in the cerebellum, hippocampus, and cerebral cortex, as well as in artificial systems. [Display omitted] •Biological solutions to general problems in multi-layer learning are shown•Intermediate layer function requires compartmentalization of learning and signaling•Circuit organization based on learning solves the credit assignment problem Using a cerebellum-like structure in an electric fish as a model system for investigating mechanisms of learning in multi-layer networks, Muller et al. observed that functional compartmentalization within individual neurons allows synaptic plasticity at an intermediate processing layer to adaptively shape network output.