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A memristor SPICE model accounting for synaptic activity dependence.

Qingjiang Li1, Alexander Serb2, Themistoklis Prodromakis2

  • 1College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, China.

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|March 19, 2015
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This study introduces a new memristor SPICE model simulating synaptic plasticity. The model accurately replicates volatile and non-volatile changes, supporting spike-timing-dependent plasticity (STDP) and biological activity dynamics.

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Area of Science:

  • Neuroscience
  • Materials Science
  • Computer Engineering

Background:

  • Memristive devices offer promising hardware for neuromorphic computing due to their synaptic plasticity.
  • Existing SPICE models often lack the fidelity to capture complex synaptic behaviors observed in practical memristors.
  • Accurate device modeling is crucial for simulating and designing advanced neural network architectures.

Purpose of the Study:

  • To develop a novel SPICE model for memristors that incorporates realistic synaptic characteristics.
  • To validate the model's ability to simulate both volatile and non-volatile memristance changes.
  • To demonstrate the model's capability in simulating spike-timing-dependent plasticity (STDP) and activity-dependent synaptic dynamics.

Main Methods:

  • Proposed a new memristor SPICE model incorporating key synaptic features.
  • Simulated memristance variations under different stimuli to assess volatility.
  • Utilized simple non-overlapping digital pulse pairs to test STDP simulation.
  • Investigated activity-dependent synaptic modification dynamics using the developed model.

Main Results:

  • The proposed model successfully accounts for both volatile and non-volatile memristance changes.
  • The model supports typical STDP with basic digital pulse inputs.
  • Simulated results for activity-dependent synaptic modification closely match biological data.

Conclusions:

  • The developed memristor SPICE model provides a more accurate representation of synaptic behavior.
  • This model is suitable for simulating neuromorphic systems and understanding synaptic plasticity.
  • The findings contribute to the advancement of hardware-based neural network research.