Brains are not free to wire themselves however they like. Synapses cost energy to maintain, connectivity is limited, and anatomy imposes its own shape. Those constraints push toward codes that squeeze the behaviourally useful information into patterns with little redundancy, simply because redundancy is expensive.

This study asks whether a competitive Hebbian rule, restricted to excitatory connections, can allocate that limited synaptic budget sensibly under such pressure. The comparison is not accuracy alone but the trade between representational cost and performance, measured with mutual-information-based quantities, against reference learning rules.

Framing it as an allocation problem is what makes the question sharp. Any rule can look good with unlimited resources. The useful question is which rule spends a fixed and genuinely scarce budget well, which is the situation biology is actually in.