  Equilibrium Propagation (EP) is a powerful and more bio-plausible alternative
to conventional learning frameworks such as backpropagation. The effectiveness
of EP stems from the fact that it relies only on local computations and
requires solely one kind of computational unit during both of its training
phases, thereby enabling greater applicability in domains such as bio-inspired
neuromorphic computing. The dynamics of the model in EP is governed by an
energy function and the internal states of the model consequently converge to a
steady state following the state transition rules defined by the same. However,
by definition, EP requires the input to the model (a convergent RNN) to be
static in both the phases of training. Thus it is not possible to design a
model for sequence classification using EP with an LSTM or GRU like
architecture. In this paper, we leverage recent developments in modern hopfield
networks to further understand energy based models and develop solutions for
complex sequence classification tasks using EP while satisfying its convergence
criteria and maintaining its theoretical similarities with recurrent
backpropagation. We explore the possibility of integrating modern hopfield
networks as an attention mechanism with convergent RNN models used in EP,
thereby extending its applicability for the first time on two different
sequence classification tasks in natural language processing viz. sentiment
analysis (IMDB dataset) and natural language inference (SNLI dataset).
