Bi-sigmoid spike-timing dependent plasticity learning rule for magnetic tunnel junction-based snn

HIGHLIGHTS

• What: The authors demonstrate the careful selection of signal shapes to emulate pre- and post-synaptic spikes, enabling the implementation of STDP in hardware (Daddinounou and Vatajelu, 2022). The authors aim to demonstrate the suitability of MTJ-based synapses for enabling unsupervised on-chip learning in SNNs. The authors characterize the MTJ device by conducting multiple electrical simulations to obtain the distribution of_(W, V) values required for switching. The authors propose the Bi-Sigmoid STDP which is a modified version of the classic STDP curve seen in biology.
• Who: Salah Daddinounou from the Polytechnic . . .
  

   

  

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