Publication date: 23rd February 2022
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 [1]. An emerging type of hardware that holds promise for efficiently
running this type of algorithm is analog neuromorphic hardware (ANH). It emulates the brain architecture and
maps neurons and synapses directly into a physical substrate. Memristive devices have been identified as potential
synaptic elements in ANH. 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 learning
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 simulator NEST. We investigate two types of ReRAM devices: (i) an analog switching memristive device,
where the conductance gradually changes between a low conductance (LCS) and a high conductance states (HCS)
and (ii) a binary switch memristive device, where the conductance abruptly changes between the LCS
and the HCS. We study the performance characteristics of the sequence learning model as a function of different
device properties and demonstrate resilience with respect to different on/off ratios, conductance resolutions,
device variability, and synaptic failure.
This project was funded by the Helmholtz Association Initiative and Networking Fund (project number SO-092, Advanced Computing Architectures), and the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No.~785907 (Human Brain Project SGA2) and No.~945539 (Human Brain Project SGA3).
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