Memristor networks for real-time neural activity analysis

The ability to efficiently analyze the activities of biological neural networks can significantly promote our understanding of neural communications and functionalities. However, conventional neural signal analysis approaches need to transmit and store large amounts of raw recording data, followed b...

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Bibliographic Details
Published in:Nature communications 2020-05, Vol.11 (1), p.2439-2439, Article 2439
Main Authors: Zhu, Xiaojian, Wang, Qiwen, Lu, Wei D.
Format: Article
Language:English
Subjects:
639/166/987
639/925/927
Action Potentials - physiology
Artificial neural networks
Electricity
Energy efficiency
Feedback
Feedback control
Firing pattern
Humanities and Social Sciences
Memristors
multidisciplinary
Neural networks
Neural Networks, Computer
Neurons - physiology
Perovskites
Real time
Science
Science (multidisciplinary)
Signal analysis
Spikes
Synchronism
Synchronization
Temporal variations
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Summary:The ability to efficiently analyze the activities of biological neural networks can significantly promote our understanding of neural communications and functionalities. However, conventional neural signal analysis approaches need to transmit and store large amounts of raw recording data, followed by extensive processing offline, posing significant challenges to the hardware and preventing real-time analysis and feedback. Here, we demonstrate a memristor-based reservoir computing (RC) system that can potentially analyze neural signals in real-time. We show that the perovskite halide-based memristor can be directly driven by emulated neural spikes, where the memristor state reflects temporal features in the neural spike train. The RC system is successfully used to recognize neural firing patterns, monitor the transition of the firing patterns, and identify neural synchronization states among different neurons. Advanced neuroelectronic systems with such memristor networks can enable efficient neural signal analysis with high spatiotemporal precision, and possibly closed-loop feedback control. Designing energy efficient artificial neural networks for real-time analysis remains a challenge. Here, the authors report the development of a perovskite halide (CsPbI3) memristor-based Reservoir Computing system for real-time recognition of neural firing patterns and neural synchronization states.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-16261-1