A bio-inspired bistable recurrent cell allows for long-lasting memory (Paper Explained)

Even though LSTMs and GRUs solve the vanishing and exploding gradient problems, they have trouble learning to remember things over very long time spans. Inspired from bistability, a property of biological neurons, this paper constructs a recurrent cell with an inherent memory property, with only minimal modification to existing architectures. OUTLINE: 0:00 - Intro & Overview 1:10 - Recurrent Neural Networks 6:00 - Gated Recurrent Unit 14:40 - Neuronal Bistability 22:50 - Bistable Recurrent Cell 31:00 - Neuromodulation 32:50 - Copy First Benchmark 37:35 - Denoising Benchmark 48:00 - Conclusion & Comments Paper: https://arxiv.org/abs/2006.05252 Code: https://github.com/nvecoven/BRC Abstract: 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