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STDP and STDP variations with memristors for spiking neuromorphic learning systems.
Front Neurosci 2013; 7:2FN

Abstract

In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original "moving wall" or to the "filament creation and annihilation" models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision.

Authors+Show Affiliations

Department of Analog and Mixed-Signal Design, Instituto de Microelectrónica de Sevilla, IMSE-CNM-CSIC Sevilla, Spain.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

23423540

Citation

Serrano-Gotarredona, T, et al. "STDP and STDP Variations With Memristors for Spiking Neuromorphic Learning Systems." Frontiers in Neuroscience, vol. 7, 2013, p. 2.
Serrano-Gotarredona T, Masquelier T, Prodromakis T, et al. STDP and STDP variations with memristors for spiking neuromorphic learning systems. Front Neurosci. 2013;7:2.
Serrano-Gotarredona, T., Masquelier, T., Prodromakis, T., Indiveri, G., & Linares-Barranco, B. (2013). STDP and STDP variations with memristors for spiking neuromorphic learning systems. Frontiers in Neuroscience, 7, p. 2. doi:10.3389/fnins.2013.00002.
Serrano-Gotarredona T, et al. STDP and STDP Variations With Memristors for Spiking Neuromorphic Learning Systems. Front Neurosci. 2013;7:2. PubMed PMID: 23423540.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - STDP and STDP variations with memristors for spiking neuromorphic learning systems. AU - Serrano-Gotarredona,T, AU - Masquelier,T, AU - Prodromakis,T, AU - Indiveri,G, AU - Linares-Barranco,B, Y1 - 2013/02/18/ PY - 2012/10/13/received PY - 2013/01/06/accepted PY - 2013/2/21/entrez PY - 2013/2/21/pubmed PY - 2013/2/21/medline KW - artificial-learning-synapses KW - memristor/cmos KW - spike-timing-dependent-plasticity KW - spiking-neural-networks SP - 2 EP - 2 JF - Frontiers in neuroscience JO - Front Neurosci VL - 7 N2 - In this paper we review several ways of realizing asynchronous Spike-Timing-Dependent-Plasticity (STDP) using memristors as synapses. Our focus is on how to use individual memristors to implement synaptic weight multiplications, in a way such that it is not necessary to (a) introduce global synchronization and (b) to separate memristor learning phases from memristor performing phases. In the approaches described, neurons fire spikes asynchronously when they wish and memristive synapses perform computation and learn at their own pace, as it happens in biological neural systems. We distinguish between two different memristor physics, depending on whether they respond to the original "moving wall" or to the "filament creation and annihilation" models. Independent of the memristor physics, we discuss two different types of STDP rules that can be implemented with memristors: either the pure timing-based rule that takes into account the arrival time of the spikes from the pre- and the post-synaptic neurons, or a hybrid rule that takes into account only the timing of pre-synaptic spikes and the membrane potential and other state variables of the post-synaptic neuron. We show how to implement these rules in cross-bar architectures that comprise massive arrays of memristors, and we discuss applications for artificial vision. SN - 1662-4548 UR - https://www.unboundmedicine.com/medline/citation/23423540/STDP_and_STDP_variations_with_memristors_for_spiking_neuromorphic_learning_systems_ L2 - https://doi.org/10.3389/fnins.2013.00002 DB - PRIME DP - Unbound Medicine ER -