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Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
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Short-term synaptic plasticity in emerging devices for neuromorphic computing.

Chao Li1,2,3, Xumeng Zhang1,4,5, Pei Chen1

  • 1State Key Laboratory of Integrated Chip and System, Frontier Institute of Chip and System, Fudan University, Shanghai 200433, China.

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|March 23, 2023
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Summary
This summary is machine-generated.

Short-term plasticity (STP) is crucial for advanced neuromorphic computing, enabling complex functions beyond long-term plasticity. This review bridges device physics and biology to explore STP implementation challenges and opportunities in emerging synaptic devices.

Keywords:
Applied computingDevicesEngineering

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Area of Science:

  • Neuromorphic Engineering
  • Computational Neuroscience
  • Materials Science

Background:

  • Neuromorphic computing leverages synaptic plasticity for artificial intelligence.
  • Short-term plasticity (STP) is vital for computational functions, unlike long-term plasticity (LTP) for memory.
  • Hardware implementation of STP remains a significant challenge compared to LTP.

Purpose of the Study:

  • To review the development of short-term plasticity (STP) in neuromorphic computing.
  • To bridge the understanding between the physics of emerging devices and biological STP behaviors.
  • To explore computational functions of STP and discuss implementation challenges and future directions.

Main Methods:

  • Review of existing literature on synaptic plasticity in neuromorphic computing.
  • Analysis of biological STP functions and their computational relevance.
  • Investigation of emerging device physics and their potential for STP emulation.
  • Discussion of challenges and potential approaches for hardware implementation of STP.

Main Results:

  • STP plays a critical role in information processing for advanced AI.
  • Emerging devices offer potential pathways for emulating biological STP.
  • Understanding the physics-device-biology interface is key to STP implementation.
  • Challenges include device variability, scalability, and integration.

Conclusions:

  • Bridging device physics and biological STP is essential for neuromorphic computing.
  • STP implementation in synaptic devices can significantly enhance system capabilities.
  • Further research is needed to overcome challenges and realize the potential of STP.
  • This review provides insights for developing next-generation neuromorphic machines.