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Artificial synapse network on inorganic proton conductor for neuromorphic systems
Li Qiang Zhu1, Chang Jin Wan1, Li Qiang Guo2
11] Jiangsu Provincial Key Laboratory of Photonic and Electronic Materials, School of Electronic Science & Engineering, Nanjing University, Nanjing 210093, People's Republic of China [2] Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, People's Republic of China.
Nature Communications
|January 24, 2014
Summary
Researchers developed a novel artificial synapse network using oxide materials and proton neurotransmitters. This breakthrough mimics short-term plasticity for advanced neuromorphic computing systems.
Area of Science:
- Materials Science
- Neuroscience
- Computer Engineering
Background:
- Neurons form the brain's basic units with over 1,000 synapse connections each.
- Synapses are crucial for information transfer and exhibit short-term plasticity for computational functions.
- Developing artificial synapse networks is a key challenge for neuromorphic computing hardware.
Purpose of the Study:
- To create a self-assembled, room-temperature oxide-based artificial synapse network.
- To investigate proton neurotransmitter coupling for lateral modulation.
- To mimic short-term plasticity behaviors for neuromorphic applications.
Main Methods:
- Self-assembly of in-plane lateral-coupled oxide-based artificial synapses on glass substrates.
- Utilizing proton neurotransmitters for coupling and lateral modulation.
- Characterizing proton-related electrical-double-layer effects.
Main Results:
- Successful room-temperature self-assembly of the artificial synapse network.
- Observation of strong lateral modulation due to proton-related effects.
- Mimicry of short-term plasticity behaviors like paired-pulse facilitation and dynamic filtering.
Conclusions:
- A novel laterally coupled oxide-based protonic/electronic hybrid artificial synapse network was demonstrated.
- The network effectively mimics essential short-term plasticity functions.
- This technology shows promise for the development of future neuromorphic systems.
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