  A possible spintronic route to hardware implementation for decision making
involves injecting a domain wall into a bifurcated magnetic nanostrip
resembling a Y-shaped junction. A decision is made when the domain wall chooses
a particular path through the bifurcation. Recently, it was shown that a
structure like a nanomagnetic Galton Board, which is essentially an array of
interconnected Y-shaped junctions, produces outcomes that are stochastic and
therefore relevant to artificial neural networks. However, the exact mechanism
leading to the robust nature of randomness is unknown. Here, we directly image
the decision-making process in nanomagnetic Galton Boards using Lorentz
transmission electron microscopy. We identify that the stochasticity in
nanomagnetic Galton Boards arises as a culmination of: (1) topology of the
injected domain wall, (2) local disorder, and (3) strength of the applied
field. Our results pave the way to a detailed understanding of stochasticity in
nanomagnetic networks.
