Sequence learning in a spiking neuronal network with memristive synapses

Neuromorphic Computing and Engineering, vol. 3, pp. 034014

Abstract

Abstract Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies at the heart of brain computation is sequence learning and prediction. This form of computation is essential for almost all our daily tasks such as movement generation, perception, and language. Understanding how the brain performs such a computation is not only important to advance neuroscience, but also to pave the way to new technological brain-inspired applications. A previously developed spiking neural network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. An emerging type of hardware that may efficiently run this type of algorithm is neuromorphic hardware. It emulates the way the brain processes information and maps neurons and synapses directly into a physical substrate. Memristive devices have been identified as potential synaptic elements in neuromorphic hardware. In particular, redox-induced resistive random access memories (ReRAM) devices stand out at many aspects. They permit scalability, are energy efficient and fast, and can implement biological plasticity rules. In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model. We implement and simulate the model including the ReRAM plasticity using the neural network simulator NEST. We investigate two types of ReRAM memristive devices: (i) a gradual, analog switching device, and (ii) an abrupt, binary switching device. We study the effect of different device properties on the performance characteristics of the sequence learning model, and demonstrate that, in contrast to many other artificial neural networks, this architecture is resilient with respect to changes in the on-off ratio and the conductance resolution, device variability, and device failure.

Authors 6

1. Younes Bouhadjar corresponding Aachen

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6), & Institute for Advanced Simulation (IAS-6), & JARA BRAIN Institute Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany

   Peter Grünberg Institute (PGI-7,10), Jülich Research Centre and JARA, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

2. Sebastian Siegel Aachen

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Peter Grünberg Institute (PGI-7,10), Jülich Research Centre and JARA, Jülich, Germany

   RWTH Aachen University, Aachen, Germany

3. Tom Tetzlaff

   Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6), & Institute for Advanced Simulation (IAS-6), & JARA BRAIN Institute Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany

4. Markus Diesmann Aachen Department of Physics Faculty 1 Department of Psychiatry, Psychotherapy and Psychosomatics Medical School

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Department of Physics, Faculty 1, & Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany

   Institute of Neuroscience and Medicine (INM-6), & Institute for Advanced Simulation (IAS-6), & JARA BRAIN Institute Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany

5. Rainer Waser Aachen Institute of Electronic Materials (IWE2) and JARA-FIT

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · RWTH Aachen University

   Affiliation as printed

   Institute of Electronic Materials (IWE 2) & JARA-FIT, RWTH Aachen University, Aachen, Germany

   Peter Grünberg Institute (PGI-7,10), Jülich Research Centre and JARA, Jülich, Germany

6. Dirk J. Wouters Aachen Institute of Electronic Materials (IWE2) and JARA-FIT

   RWTH Aachen University

   Affiliation as printed

   Institute of Electronic Materials (IWE 2) & JARA-FIT, RWTH Aachen University, Aachen, Germany