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Recurrent neural networks (RNNs) provide state-of-the-art performances in awide variety of tasks that require memory. These performances can often beachieved thanks to gated recurrent cells such as gated recurrent units (GRU)and long short-term memory (LSTM). Standard gated cells share a layer internalstate to store information at the network level, and long term memory is shapedby network-wide recurrent connection weights. Biological neurons on the otherhand are capable of holding information at the cellular level for an arbitrarylong amount of time through a process called bistability. Through bistability,cells can stabilize to different stable states depending on their own paststate and inputs, which permits the durable storing of past information inneuron state. In this work, we take inspiration from biological neuronbistability to embed RNNs with long-lasting memory at the cellular level. Thisleads to the introduction of a new bistable biologically-inspired recurrentcell that is shown to strongly improves RNN performance on time-series whichrequire very long memory, despite using only cellular connections (allrecurrent connections are from neurons to themselves, i.e. a neuron state isnot influenced by the state of other neurons). Furthermore, equipping this cellwith recurrent neuromodulation permits to link them to standard GRU cells,taking a step towards the biological plausibility of GRU.

A bio-inspired bistable recurrent cell allows for long-lasting memory