Stability of the Memory State of a Time-Varying Memristive Neural Network Model

Resumen: Memristors are resistive memory devices, where the resistive memory state is a function of the memristor’s initial conditions and the history of the voltage across its terminals. Applications of these devices are in neuromorphic circuits. In particular, as representations of the open-close dynamics of the ionic channels in neurophysiological models. We use a memristive version of the Integrate and Fire neuron to construct a time-varying memristive neural network. In this model, a memory state is a stable unique equilibrium point. We show that the existence of a memory state depends uniformly on properties of the network topology and description of the memristive characteristic function. We illustrate our results using numerical simulations.

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Illiani Carro Pérez & Juan Gonzalo Barajas-Ramirez. Stability of the Memory State of a Time-Varying Memristive Neural Network Model. Memorias del Congreso Nacional de Control Automático, pp. 1-5, 2020.

Palabras clave

Memristors, Neural network models, Neuron models, Resistive memory

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