Restricting plasticity to large, coordinated synaptic updates keeps biological learning cheap — unrestricted backprop would need ~100,000x more synaptic updates in macaque V1

Learning is metabolically cheap in the brain not only by accident of biophysics but because the brain actively limits which synapses change. van Rossum & Pache model plasticity-restricting rules — "only modify synapses with large updates" plus confining change to coordinated subnetworks — and estimate that unrestricted backpropagation would require on the order of 100,000 times more synaptic updates than the selective rule in a model of macaque primary visual cortex (V1). Learning energy scales with the number and magnitude of weight changes, so restricting plasticity to the few high-impact synapses is the brain's route to cheap learning — the mechanism behind the low per-event cost that Karbowski measures (claim-synaptic-plasticity-cheap-fraction-of-transmission-energy).

The authors close the loop back to artificial networks: the same restriction rules can be applied to reduce the energy of training artificial neural networks. Framed in machine-learning terms, this is gradient sparsification — updating only the largest-gradient parameters rather than the full weight matrix on every step. It is a biologically motivated argument for the kind of sparse, selective weight updates that AI reaches for to cut training cost, and it stands against the every-weight-every-step character of standard backpropagation (claim-training-inference-compute-asymmetry-mechanism, claim-update-locking-backprop-constraint). It also complements the representational/mechanism-mismatch thesis of backpropagation-gap: the brain that approximates backprop's principles (claim-brain-approximates-backprop-core-principles-ngrad) does so under an energy budget that forbids backprop's literal update volume.