Magnetic tunnel junction mimics stochastic cortical spiking neurons

Abhronil Sengupta, Priyadarshini Panda, Parami Wijesinghe, Yusung Kim, Kaushik Roy

Research output: Contribution to journalArticlepeer-review

64 Scopus citations

Abstract

Brain-inspired computing architectures attempt to mimic the computations performed in the neurons and the synapses in the human brain in order to achieve its efficiency in learning and cognitive tasks. In this work, we demonstrate the mapping of the probabilistic spiking nature of pyramidal neurons in the cortex to the stochastic switching behavior of a Magnetic Tunnel Junction in presence of thermal noise. We present results to illustrate the efficiency of neuromorphic systems based on such probabilistic neurons for pattern recognition tasks in presence of lateral inhibition and homeostasis. Such stochastic MTJ neurons can also potentially provide a direct mapping to the probabilistic computing elements in Belief Networks for performing regenerative tasks.

Original languageEnglish (US)
Article number30039
JournalScientific reports
Volume6
DOIs
StatePublished - Jul 21 2016

All Science Journal Classification (ASJC) codes

  • General

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