Gradient modelling of memristive systems

2025/04/14 by Fulvio Forni, Rodolphe Sepulchre, Forni, Fulvio +1
Engineering · Neuroscience · Physics and Astronomy · #Advanced Memory and Neural Computing #Neural dynamics and brain function #stochastic dynamics and bifurcation

paper · pdf · doi:10.48550/arxiv.2504.10093

Abstract

We introduce a gradient modeling framework for memristive systems. Our focus is on memristive systems as they appear in neurophysiology and neuromorphic systems. Revisiting the original definition of Chua, we regard memristive elements as gradient operators of quadratic functionals with respect to a metric determined by the memristance. We explore the consequences of gradient properties for the analysis and design of neuromorphic circuits.

Citations

• An Operator-Theoretic Framework to Simulate Neuromorphic Circuits
• Spiking Control Systems
• Neuromorphic Control
• Neurons with graded response have collective computational properties like those of two-state neurons.
• Memristor-The missing circuit element
• A quantitative description of membrane current and its application to conduction and excitation in nerve

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