Reservoir computing with a heterogeneous distribution of ionic nanofluidic memristors
Abstract
Abstract Nanofluidic memristive systems exhibit the nonlinear behavior and the short-time plasticity needed for reservoir computing (RC) networks. They use ions as information carriers and operate in an electrochemical environment, in resemblance to the biological synapses. Here we present simulation results of an RC model implementation using a parallel array of memristive nanopores as reservoir. Each nanopore of the array is simulated under distinct chemical conditions using an experimentally justified theoretical model. We demonstrate the potential of the proposed network by performing three different RC tasks: sine wave nonlinear transformation, waveform classification, and forecasting of the Mackey–Glass chaotic time series.
Fields of Study & Subjects
Advanced Memory and Neural Computing
Neural Networks and Reservoir Computing
Nanopore and Nanochannel Transport Studies
Publication details
- Journal / source
- Neuromorphic Computing and Engineering
- Publisher
- IOP Publishing
- Volume
- 6
- Issue
- 1
- Pages
- 014013–014013
- Publication date
- 11 February 2026
- Language
- EN