  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.
