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We propose a novel {\it Equilibrated Recurrent Neural Network} (ERNN) tocombat the issues of inaccuracy and instability in conventional RNNs. Drawingupon the concept of autapse in neuroscience, we propose augmenting an RNN witha time-delayed self-feedback loop. Our sole purpose is to modify the dynamicsof each internal RNN state and, at any time, enforce it to evolve close to theequilibrium point associated with the input signal at that time. We show thatsuch self-feedback helps stabilize the hidden state transitions leading to fastconvergence during training while efficiently learning discriminative latentfeatures that result in state-of-the-art results on several benchmark datasetsat test-time. We propose a novel inexact Newton method to solve fixed-pointconditions given model parameters for generating the latent features at eachhidden state. We prove that our inexact Newton method converges locally withlinear rate (under mild conditions). We leverage this result for efficienttraining of ERNNs based on backpropagation.

Equilibrated Recurrent Neural Network: Neuronal Time-Delayed Self-Feedback Improves Accuracy and Stability