  Artificial neural networks (ANNs) with recurrence and self-attention have
been shown to be Turing-complete (TC). However, existing work has shown that
these ANNs require multiple turns or unbounded computation time, even with
unbounded precision in weights, in order to recognize TC grammars. However,
under constraints such as fixed or bounded precision neurons and time, ANNs
without memory are shown to struggle to recognize even context-free languages.
In this work, we extend the theoretical foundation for the $2^{nd}$-order
recurrent network ($2^{nd}$ RNN) and prove there exists a class of a $2^{nd}$
RNN that is Turing-complete with bounded time. This model is capable of
directly encoding a transition table into its recurrent weights, enabling
bounded time computation and is interpretable by design. We also demonstrate
that $2$nd order RNNs, without memory, under bounded weights and time
constraints, outperform modern-day models such as vanilla RNNs and gated
recurrent units in recognizing regular grammars. We provide an upper bound and
a stability analysis on the maximum number of neurons required by $2$nd order
RNNs to recognize any class of regular grammar. Extensive experiments on the
Tomita grammars support our findings, demonstrating the importance of tensor
connections in crafting computationally efficient RNNs. Finally, we show
$2^{nd}$ order RNNs are also interpretable by extraction and can extract state
machines with higher success rates as compared to first-order RNNs. Our results
extend the theoretical foundations of RNNs and offer promising avenues for
future explainable AI research.
