Just by the mathematical definition of interpolation (https://en.wikipedia.org/wiki/Interpolation), any function which approximates the given regression points (training data), and is defined for values in between (unseed data), will interpolate it. Maybe you think about linear interpolation specifically, but there are many types of interpolation, and any mathematical function, any neural network is just another form of interpolation.

Interpolation is also related to extrapolation. In higher dimensional spaces, the distinction is not so clear. In terms of machine learning, you would call this generalization.

The question is more, is it a good type of interpolation/extrapolation/generalization. You measure that on a test set.

And mathematically speaking, your brain also is just doing another type of interpolation/extrapolation.