Magnetic Tunnel Junction Mimics Stochastic Cortical Spiking Neurons

TL;DR

This paper demonstrates how Magnetic Tunnel Junctions can emulate the probabilistic spiking behavior of cortical neurons, enabling efficient neuromorphic pattern recognition and probabilistic computing.

Contribution

It introduces a novel mapping of cortical neuron stochasticity to MTJ switching behavior, advancing neuromorphic hardware design.

Findings

MTJ-based neurons successfully perform pattern recognition tasks.

Stochastic MTJ neurons exhibit thermal noise-driven probabilistic spiking.

Potential for direct implementation of probabilistic models like Belief Networks.

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.