  A major challenge in both neuroscience and machine learning is the
development of useful tools for understanding complex information processing
systems. One such tool is probes, i.e., supervised models that relate features
of interest to activation patterns arising in biological or artificial neural
networks. Neuroscience has paved the way in using such models through numerous
studies conducted in recent decades. In this work, we draw insights from
neuroscience to help guide probing research in machine learning. We highlight
two important design choices for probes $-$ direction and expressivity $-$ and
relate these choices to research goals. We argue that specific research goals
play a paramount role when designing a probe and encourage future probing
studies to be explicit in stating these goals.
