  Temporal models based on recurrent neural networks have proven to be quite
powerful in a wide variety of applications. However, training these models
often relies on back-propagation through time, which entails unfolding the
network over many time steps, making the process of conducting credit
assignment considerably more challenging. Furthermore, the nature of
back-propagation itself does not permit the use of non-differentiable
activation functions and is inherently sequential, making parallelization of
the underlying training process difficult. Here, we propose the Parallel
Temporal Neural Coding Network (P-TNCN), a biologically inspired model trained
by the learning algorithm we call Local Representation Alignment. It aims to
resolve the difficulties and problems that plague recurrent networks trained by
back-propagation through time. The architecture requires neither unrolling in
time nor the derivatives of its internal activation functions. We compare our
model and learning procedure to other back-propagation through time
alternatives (which also tend to be computationally expensive), including
real-time recurrent learning, echo state networks, and unbiased online
recurrent optimization. We show that it outperforms these on sequence modeling
benchmarks such as Bouncing MNIST, a new benchmark we denote as Bouncing
NotMNIST, and Penn Treebank. Notably, our approach can in some instances
outperform full back-propagation through time as well as variants such as
sparse attentive back-tracking. Significantly, the hidden unit correction phase
of P-TNCN allows it to adapt to new datasets even if its synaptic weights are
held fixed (zero-shot adaptation) and facilitates retention of prior generative
knowledge when faced with a task sequence. We present results that show the
P-TNCN's ability to conduct zero-shot adaptation and online continual sequence
modeling.
