Modeling Inhibitory and Excitatory Synapse Learning in the Memristive Neuron Model
Max Talanov, E. Yu. Zykov, Victor Erokhin, Evgeni Magid, Salvatore Distefano, Yuriy Gerasimov, Jordi Vallverdú
- Year
- 2017
- Citations
- 6
Abstract
© 2017 by SCITEPRESS - Science and Technology Publications, Lda. All Rights Reserved. In this paper we present the results of simulation of exitatory Hebbian and inhibitory "sombrero" learning of a hardware architecture based on organic memristive elements and operational amplifiers implementing an artificial neuron we recently proposed. This is a first step towards the deployment on robots of a bioplausible simulation, currently developed in the neuro-biologically inspired cognitive architecture (NeuCogAr) implementing basic emotional states or affects in a computational system, in the context of our "Robot dream" project. The long term goal is to re-implement dopamine, serotonin and noradrenaline pathways of NeuCogAr in a memristive hardware.
Keywords
Related papers
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991