An invariant schema emerges within a neural network during hierarchical learning of visual boundaries

Abstract

Neural circuits must balance plasticity and stability to enable continual learning without catastrophic forgetting, a pervasive feature of artificial neural networks trained using end-to-end learning (e.g. backpropagation). Here, we apply an alternative, hierarchical learning algorithm to the cognitive task of boundary detection in video clips. In contrast to backpropagation, hierarchical training converges to a network executing a fixed schema and generates firing statistics consistent with single-neuron recordings from human subjects performing the same task. The hierarchically trained network's schema circuit remains invariant following training on sparse data, with additional data serving to refine the upstream representation.

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