Topic: song production

An important problem in systems neuroscience is to understand how animals transform complex, high-dimensional sensory inputs into neural signals that drive behavior. Here we propose a novel modeling approach for this sensorimotor transformation to identify a one-to-one mapping between real and model neurons. Our approach involves training a deep neural network to produce behavioral data from a range of different inactivation experiments. Crucially, we train the model while perturbing its hidden units with the same perturbations applied to neural activity—a process we call ‘knockout’ training.