  Recurrent neural networks (RNNs) provide state-of-the-art performances in a
wide variety of tasks that require memory. These performances can often be
achieved thanks to gated recurrent cells such as gated recurrent units (GRU)
and long short-term memory (LSTM). Standard gated cells share a layer internal
state to store information at the network level, and long term memory is shaped
by network-wide recurrent connection weights. Biological neurons on the other
hand are capable of holding information at the cellular level for an arbitrary
long amount of time through a process called bistability. Through bistability,
cells can stabilize to different stable states depending on their own past
state and inputs, which permits the durable storing of past information in
neuron state. In this work, we take inspiration from biological neuron
bistability to embed RNNs with long-lasting memory at the cellular level. This
leads to the introduction of a new bistable biologically-inspired recurrent
cell that is shown to strongly improves RNN performance on time-series which
require very long memory, despite using only cellular connections (all
recurrent connections are from neurons to themselves, i.e. a neuron state is
not influenced by the state of other neurons). Furthermore, equipping this cell
with recurrent neuromodulation permits to link them to standard GRU cells,
taking a step towards the biological plausibility of GRU.
