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Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics
Alexey Mikhaylov1, Alexey Pimashkin1, Yana Pigareva1
1Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.
Frontiers in Neuroscience
|May 16, 2020
Summary
This study presents a neurohybrid memristive chip concept, integrating living neural networks with memristive devices and CMOS electronics for advanced brain-on-chip systems. This innovation paves the way for next-generation AI and personalized medicine.
Area of Science:
- Neuroscience
- Materials Science
- Electrical Engineering
- Computer Science
Background:
- Living neural networks offer unique computational capabilities.
- Memristive devices provide efficient, non-volatile memory and processing.
- Integrating biological and artificial components is a frontier in computing.
Purpose of the Study:
- To conceptualize a neurohybrid memristive chip.
- To explore bidirectional neurointerfaces.
- To outline a roadmap for future development.
Main Methods:
- Cultivating and spatially ordering dissociated hippocampal neuron cells in microfluidic/microelectrode systems.
- Fabricating large-scale memristive cross-bar arrays using device engineering and resistive state programming.
- Implementing spiking neural networks (SNNs) with memristive devices and CMOS electronics.
Main Results:
- A conceptual framework for a brain-on-chip system was established.
- Integration strategies for living neurons and memristive circuits were detailed.
- The potential for bidirectional neurointerfaces was demonstrated.
Conclusions:
- Neurohybrid memristive systems represent a significant advancement in computing.
- This technology holds promise for applications in robotics, AI, and personalized medicine.
- A clear roadmap for the next decade of research and development was proposed.
