Why brain-scanning statistics can mislead you about neural codes

Neuroscientists often compare neural activity patterns across brains or conditions using representational similarity matrices—essentially measuring which neurons fire together. But hidden symmetries in incoming signals can fool these analyses: networks can encode information in multiple functionally equivalent ways that produce wildly different similarity patterns. This matters because researchers comparing neural codes across studies or species could mistake identical computations for different ones.