A New SCTN Digital Low Power Spiking Neuron

Moshe Bensimon, Shlomo Greenberg, Yehuda Ben-Shimol, Moshe Haiut

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This brief presents an innovative reconfigurable parametric model of a digital Spiking Neuron (SN). The proposed neuron model is based on the classical Leaky Integrate and Fire (LIF) model with some modifications, allowing neuron configuration using seven different leak modes and three activation functions with a dynamic threshold setting. A complementary online learning model based on adjustable Spike Timing Dependent Plasticity (STDP) learning rules has been developed as part of the proposed neuron architecture. Efficient hardware implementation of the proposed SN significantly reduces area and power costs. The proposed SN model consumes less than 10nW and requires only 700 ASIC 2-input NAND gates for implementation using ten neuron-inputs. Simulation results show an average power consumption of about 3.5 mW/cm2. Simulation of the proposed digital SN demonstrates its ability to replicate accurately the behavior of a biological neuron model.

Original languageEnglish
Article number9377478
Pages (from-to)2937-2941
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume68
Issue number8
DOIs
StatePublished - 1 Aug 2021

Keywords

  • Izhikevich behaviors
  • LIF model
  • STDP learning rules
  • Spiking neuron
  • digital neuron
  • low power
  • neuromorphic

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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