Echo state and band-pass networks with aqueous memristors: Leaky reservoir computing with a leaky substrate

Recurrent Neural Networks (RNNs) are extensively employed for processing sequential data such as time series. Reservoir computing (RC) has drawn attention as an RNN framework due to its fixed network that does not require training, making it an attractive platform for hardware-based machine learning. We establish an explicit correspondence between the well-established mathematical RC implementations of echo state networks and band-pass networks with leaky integrator nodes on the one hand and a physical circuit containing iontronic simple volatile memristors on the other. These aqueous iontronic devices employ ion transport through water as signal carriers and feature a voltage-dependent (memory) conductance. The activation function and the dynamics of the leaky integrator nodes naturally materialize as the (dynamic) conductance properties of iontronic memristors, while a simple fixed local current-to-voltage update rule at the memristor terminals facilitates the relevant matrix coupling between nodes. We process various time series, including pressure data from simulated airways during breathing that can be directly fed into the network due to the intrinsic responsiveness of iontronic devices to applied pressures. We accomplish this by employing established physical equations of motion of iontronic memristors for the internal dynamics of the circuit.