Neuromorphic Time-Dependent Pattern Classification with Organic Electrochemical Transistor Arrays
Sébastien Pecqueur,Maurizio Mastropasqua Talamo,David Guerin,Philippe Blanchard,Jean Roncali,Dominique Vuillaume,Fabien Alibart +6 more
TLDR
In this article, an innovative approach that relies on both iono-electronic materials and intrinsic device physics to show pattern classification out of a 12-unit biosensing array is presented.Abstract:
Based on bottom‐up assembly of highly variable neural cells units, the nervous system can reach unequalled level of performances with respect to standard materials and devices used in microelectronic. Reproducing these basic concepts in hardware could potentially revolutionize materials and device engineering which are used for information processing. Here, an innovative approach that relies on both iono‐electronic materials and intrinsic device physics to show pattern classification out of a 12‐unit biosensing array is presented. The reservoir computing and learning concept to demonstrate relevant computing based on the ionic dynamics in 400 nm channel‐length organic electrochemical transistor is used. It is shown that this approach copes efficiently with the high level of variability obtained by bottom‐up fabrication using a new electropolymerizable polymer, which enables iono‐electronic device functionality and material stability in the electrolyte. The effect of the array size and variability on the performances for a real‐time classification task paving the way to new embedded sensing and processing approaches is investigatedread more
Citations
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Physics for neuromorphic computing
TL;DR: Striking results that leverage physics to enhance the computing capabilities of artificial neural networks, using resistive switching materials, photonics, spintronics and other technologies are reviewed.
Journal ArticleDOI
Towards organic neuromorphic devices for adaptive sensing and novel computing paradigms in bioelectronics
TL;DR: An overview of the latest studies on organic neuromorphic and sensing devices can be found in this article, where the authors highlight the need for smart and adaptive sensing and highlight the potential of these concepts to enhance the interaction efficiency between electronics and biological substances.
Journal ArticleDOI
Reservoir computing with biocompatible organic electrochemical networks for brain-inspired biosignal classification
Matteo Cucchi,Christopher Gruener,Lautaro Petrauskas,Peter Steiner,Hsin Tseng,Axel Fischer,Bogdan Penkovsky,Christian D. Matthus,Peter Birkholz,Hans Kleemann,Karl Leo +10 more
TL;DR: In this article, brain-inspired networks composed of organic electrochemical transistors were used for time-series predictions and classification tasks using the reservoir computing approach, and the results showed their potential use for biofluid monitoring and biosignal analysis.
Posted Content
Physics for Neuromorphic Computing
TL;DR: In this paper, the authors make the case that building this new hardware necessitates reinventing electronics, and they show that research in physics and material science will be key to create artificial nano-neurons and synapses, to connect them together in huge numbers, to organize them in complex systems.
Journal ArticleDOI
Organic electrochemical transistors in bioelectronic circuits
TL;DR: The organic electrochemical transistor (OECT) represents a versatile and impactful electronic building block in the areas of printed electronics, bioelectronics, and neuromorphic computing as mentioned in this paper.
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