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Bernabe Linares-Barranco
Researcher at Spanish National Research Council
Publications - 280
Citations - 10991
Bernabe Linares-Barranco is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Neuromorphic engineering & CMOS. The author has an hindex of 46, co-authored 257 publications receiving 9125 citations. Previous affiliations of Bernabe Linares-Barranco include Texas A&M University & University of Seville.
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Journal ArticleDOI
Neuromorphic Silicon Neuron Circuits
Giacomo Indiveri,Bernabe Linares-Barranco,Tara Julia Hamilton,André van Schaik,Ralph Etienne-Cummings,Tobi Delbruck,Shih-Chii Liu,Piotr Dudek,Philipp Hafliger,Sylvie Renaud,Johannes Schemmel,Gert Cauwenberghs,John V. Arthur,Kai Hynna,Fopefolu Folowosele,Sylvain Saïghi,Teresa Serrano-Gotarredona,Jayawan H B Wijekoon,Yingxue Wang,Kwabena Boahen +19 more
TL;DR: The most common building blocks and techniques used to implement these circuits, and an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models.
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On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex
C. Zamarreno-Ramos,Luis A. Camunas-Mesa,Jose A. Pérez-Carrasco,Timothée Masquelier,Teresa Serrano-Gotarredona,Bernabe Linares-Barranco +5 more
TL;DR: The aim of this paper is to present, in a tutorial manner, an initial framework for the possible development of fully asynchronous STDP learning neuromorphic architectures exploiting two or three-terminal memristive type devices.
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Integration of nanoscale memristor synapses in neuromorphic computing architectures
Giacomo Indiveri,Bernabe Linares-Barranco,Robert Legenstein,George Deligeorgis,Themistoklis Prodromakis,Themistoklis Prodromakis +5 more
TL;DR: In this paper, a novel hybrid memristor-CMOS neuromorphic circuit is proposed, which represents a radical departure from conventional neuro-computing approaches, as it uses memristors to directly emulate the biophysics and temporal dynamics of real synapses.
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STDP and STDP variations with memristors for spiking neuromorphic learning systems.
Teresa Serrano-Gotarredona,Timothée Masquelier,Themistoklis Prodromakis,Giacomo Indiveri,Bernabe Linares-Barranco +4 more
TL;DR: This paper reviews several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristor as synapses, and shows how to implement these rules in cross-bar architectures that comprise massive arrays of memristors.
Journal ArticleDOI
CAVIAR: A 45k Neuron, 5M Synapse, 12G Connects/s AER Hardware Sensory–Processing– Learning–Actuating System for High-Speed Visual Object Recognition and Tracking
R. Serrano-Gotarredona,M. Oster,P. Lichtsteiner,Alejandro Linares-Barranco,R. Paz-Vicente,F. Gomez-Rodriguez,Luis A. Camunas-Mesa,R Berner,M. Rivas-Perez,Tobi Delbruck,Shih-Chii Liu,Rodney J. Douglas,Philipp Hafliger,Gabriel Jimenez-Moreno,A.C. Ballcels,Teresa Serrano-Gotarredona,A.J. Acosta-Jimenez,Bernabe Linares-Barranco +17 more
TL;DR: CAVIAR is a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system that achieves millisecond object recognition and tracking latencies.