With nonlinear student and teacher dynamics, mismatches can be even more extreme, leading to the spurious discovery of limit cycles and incorrectly identified stable fixed points. This can occur over a wide range of fitting methods. (9/n)

When the teacher network’s connectivity is non-normal, a data-constrained student under partial observation may spuriously fit transient dynamics using attractor-like dynamics. We show this analytically in the case of feedforward chain (top) and low-rank teacher connectivity (bottom). (8/n)

As a motivating example, we show that fitting a low-dimensional linear dynamical system to simulated recordings of a feedforward chain performing an integration task leads to the spurious discovery of line attractor-like dynamics. (6/n)

Here we show that observing only a subset of neurons in a circuit can create mechanistic mismatches between a simulated teacher network and a data-constrained student. (5/n)