STDP and STDP Variations with Memristors for Spiking Neuromorphic Learning Systems (bibtex)
by , , , ,
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.
Reference:
STDP and STDP Variations with Memristors for Spiking Neuromorphic Learning Systems (T. Serrano-Gotarredona, T. Masquelier, T. Prodromakis, G. Indiveri, B. Linares-Barranco), In Frontiers in Neuroscience, volume 7, 2013.
Bibtex Entry:
@Article{Serrano-Gotarredona_etal13,
author		= {T. Serrano-Gotarredona and T. Masquelier and T.
		  Prodromakis and G. Indiveri and B. Linares-Barranco},
title		= {{STDP} and {STDP} Variations with Memristors for Spiking
		  Neuromorphic Learning Systems},
journal		= {Frontiers in Neuroscience},
year		= {2013},
volume		= {7},
number		= {2},
issn		= {1662-453X},
doi		= {10.3389/fnins.2013.00002},
url		= {http://www.frontiersin.org/neuroscience/10.3389/fnins.2013.00002/full}
		  ,
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.}
}
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