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

arXiv: https://arxiv.org/abs/2006.05252 · HTML: https://mlsanity.com/paper/2006.05252/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 8 of 8)

- [Recurrent networks, hidden states and beliefs in partially observable environments](https://mlsanity.com/paper/2208.03520/) (2022)
- [Warming up recurrent neural networks to maximise reachable multistability greatly improves learning](https://mlsanity.com/paper/2106.01001/) (2021)
- [Parallelizable memory recurrent units](https://mlsanity.com/paper/2601.09495/) (2026)
- [Churn prediction in online gambling](https://mlsanity.com/paper/2201.02463/) (2022)
- [Spike-based computation using classical recurrent neural networks](https://mlsanity.com/paper/2306.03623/) (2023)
- [Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications](https://mlsanity.com/paper/2605.11855/) (2026)
- [Light Alignment Improves LLM Safety via Model Self-Reflection with a Single Neuron](https://mlsanity.com/paper/2602.02027/) (2026)
- [Train Short, Act Long: Multistability and the Horizon Independence of Recurrent Policies](https://mlsanity.com/paper/2605.12206/) (2026)

## Abstract (arXiv)

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.