  Training data is always finite, making it unclear how to generalise to unseen
situations. But, animals do generalise, wielding Occam's razor to select a
parsimonious explanation of their observations. How they do this is called
their inductive bias, and it is implicitly built into the operation of animals'
neural circuits. This relationship between an observed circuit and its
inductive bias is a useful explanatory window for neuroscience, allowing design
choices to be understood normatively. However, it is generally very difficult
to map circuit structure to inductive bias. Here, we present a neural network
tool to bridge this gap. The tool meta-learns the inductive bias by learning
functions that a neural circuit finds easy to generalise, since
easy-to-generalise functions are exactly those the circuit chooses to explain
incomplete data. In systems with analytically known inductive bias, i.e. linear
and kernel regression, our tool recovers it. Generally, we show it can flexibly
extract inductive biases from supervised learners, including spiking neural
networks, and show how it could be applied to real animals. Finally, we use our
tool to interpret recent connectomic data illustrating its intended use:
understanding the role of circuit features through the resulting inductive
bias.
