Long-term memory and synapse-like plasticity in carbon-based nanofluidic channels

Fine-tuned ion transport across nanoscale pores is key to many biological processes such as the process of information in the brain. Such advanced functionalities are still far outside the reach of bio-inspired nanofluidic systems. However, a milestone has been reached very recently with the design of nanoscale carbon-based slits, whose conductance exhibits long-term memory emerging from the slow dynamics of the system's surface [1,2,3]. In this talk, I will show that this plastic memory can be harnessed to mimic that of biological synapses. In addition, we designed an experiment recreating a form of Hebbian learning, where the system’s conductance is updated according to the relative activation times of two neurons. This allows us to implement an elementary, bio-inspired learning algorithm with nanofluidic building blocks, paving the way for the development of advanced iontronics.

[1] P Robin, N Kavokine, L Bocquet, Science (2021)

[2] T Emmerich, KS Vasu, A Niguès, A Keerthi, B Radha, A Siria, L Bocquet, under review

[3] T Emmerich, A Ismail, P Robin, A Keerthi, B Radha, A Siria, A Geim, L Bocquet, in prep

This work received funding from the EU H2020 Framework Programme/ERC Advanced Grant agreement number 785911-Shadoks and ANR project Neptune.