Roberto Sotero

Trainable spiking neural networks have treated energy as a generic penalty blind to synapse type, even though cortical glutamatergic transmission costs substantially more ATP than GABAergic transmission. Embedding that cost asymmetry as a Lagrangian penalty inside surrogate-gradient training lets a single scalar penalty exert stronger gradient pressure on excitatory pathways in a Dale's-law-compliant recurrent microcircuit. On the Spiking Heidelberg Digits benchmark, the unpenalised network settled at 70.9% in an inhibition-dominated regime, while mild metabolic pressure raised accuracy to 79.5% and cut energy from 86,519 to 16,019 ATP units per sample.

Trainable spiking neural networks have treated energy as a generic penalty blind to synapse type, even though cortical glutamatergic transmission costs substantially more ATP than GABAergic transmission. Embedding that cost asymmetry as a Lagrangian penalty inside surrogate-gradient training lets a single scalar penalty exert stronger gradient pressure on excitatory pathways in a Dale's-law-compliant recurrent microcircuit. On the Spiking Heidelberg Digits benchmark, the unpenalised network settled at 70.9% in an inhibition-dominated regime, while mild metabolic pressure raised accuracy to 79.5% and cut energy from 86,519 to 16,019 ATP units per sample.