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Leti shows memristive devices cana minic different kind of synaptic plasticity inspired by biology

GRENOBLE, France – Dec. 6, 2016 – Leti researchers have demonstrated that memristive
devices are excellent candidates to emulate synaptic plasticity, the capability of synapses to
enhance or diminish their connectivity between neurons, which is widely believed to be the
cellular basis for learning and memory.
Published on 6 December 2016

The breakthrough was presented today at IEDM 2016 in San Francisco in the paper,
“Experimental Demonstration of Short and Long Term Synaptic Plasticity Using OxRAM Multi kbit
Arrays for Reliable Detection in Highly Noisy Input Data”.

Neural systems such as the human brain exhibit various types and time periods of plasticity, e.g.
synaptic modifications can last anywhere from seconds to days or months. However, prior
research in utilizing synaptic plasticity using memristive devices relied primarily on simplified rules
for plasticity and learning.

The project team, which includes researchers from Leti’s sister institute at CEA Tech, List, along
with INSERM and Clinatec, proposed an architecture that implements both short- and long-term
plasticity (STP and LTP) using RRAM devices.

“While implementing a learning rule for permanent modifications – LTP, based on spike-timingdependent
plasticity – we also incorporated the possibility of short-term modifications with STP,
based on the Tsodyks/Markram model,” said Elisa Vianello, Leti non-volatile memories and
cognitive computing specialist/research engineer. “We showed the benefits of utilizing both kinds
of plasticity with visual pattern extraction and decoding of neural signals. LTP allows our artificial
neural networks to learn patterns, and STP makes the learning process very robust against
environmental noise.”

Resistive random-access memory (RRAM) devices coupled with a spike-coding scheme are key
to implementing unsupervised learning with minimal hardware footprint and low power
consumption. Embedding neuromorphic learning into low-power devices could enable design of
autonomous systems, such as a brain-machine interface that makes decisions based on realtime,
on-line processing of in-vivo recorded biological signals. Biological data are intrinsically
highly noisy and the proposed combined LTP and STP learning rule is a powerful technique to
improve the detection/recognition rate. This approach may enable the design of autonomous
implantable devices for rehabilitation purposes.

Leti, which has worked on RRAM to develop hardware neuromorphic architectures since 2010, is
the coordinator of the H2020 European project NeuRAM3. That project is working on fabricating a
chip with architecture that supports state-of-the-art machine-learning algorithms and spike-based
learning mechanisms.

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