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Updated: Feb 7, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
Published on: March 2, 2015
Evaluation of the computational capabilities of a memristive random network (MN3) under the context of reservoir
Laura E Suarez1, Jack D Kendall2, Juan C Nino2
1Department of Industrial Engineering, Universidad de los Andes, Bogotá, Colombia; Department of Materials Science and Engineering, University of Florida, Gainesville, FL 32601, USA.
Abstract:
This work presents the simulation results of a novel recurrent, memristive neuromorphic architecture, the MN3 and explores its computational capabilities in the performance of a temporal pattern recognition task by considering the principles of the reservoir computing approach. A simple methodology based on the definitions of ordered and chaotic dynamical systems was used to determine the separation and fading memory properties of the architecture. The results show the potential use of this architecture as a reservoir for the on-line processing of time-varying inputs.
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