  Backpropagation algorithms on recurrent artificial neural networks require an
unfolding of accumulated states over time. These states must be kept in memory
for an undefined period of time which is task-dependent. This paper uses the
reservoir computing paradigm where an untrained recurrent neural network layer
is used as a preprocessor stage to learn temporal and limited data. These
so-called reservoirs require either extensive fine-tuning or neuroplasticity
with unsupervised learning rules. We propose a new local plasticity rule named
P-CRITICAL designed for automatic reservoir tuning that translates well to
Intel's Loihi research chip, a recent neuromorphic processor. We compare our
approach on well-known datasets from the machine learning community while using
a spiking neuronal architecture. We observe an improved performance on tasks
coming from various modalities without the need to tune parameters. Such
algorithms could be a key to end-to-end energy-efficient neuromorphic-based
machine learning on edge devices.
