["The Journal of Physiology, Volume 604, Issue 7, Page 3005-3031, 1 April 2026. ", "\nAbstract figure legend We studied activity‐dependent adaptation in the fruit fly Drosophila’s memory centre, the mushroom body. Here, excitatory Kenyon cells (KCs) receive feedback inhibition from the anterior paired lateral (APL) neuron. When KCs are artificially overactivated for 24 h, both KCs and APL reduce their sensitivity to excitation (‘↓ Exc’, ‘↓ Na+ channels’), resulting in conflicting adaptations on KCs, that is, both reduced excitation and reduced inhibition. \n\n\n\n\n\n\n\n\n\nAbstract\nNeural networks maintain stable activity levels by compensating for perturbations through homeostatic plasticity. However, homeostatic mechanisms operating at different levels may conflict with each other. For example, in inhibitory feedback circuits, if inhibitory neurons receive excess excitation, compensation at a ‘local’ level (e.g. reducing inhibitory neurons’ activity) could conflict with ‘network‐level’ compensation (e.g. suppressing the excitatory neurons responsible for overexciting the inhibitory neurons). We studied this problem in the Drosophila mushroom body, where excitatory Kenyon cells (KCs) receive feedback inhibition from the anterior paired lateral (APL) neuron. Dual‐colour calcium imaging revealed that prolonged (24 h) artificial activation of KCs causes APL to become less sensitive to KC activity. Meanwhile, KCs compensate for their excess activity by reducing excitation, yet this change is opposed by reduced inhibition from APL. This conflict meant that KCs did not consistently show the expected homeostatic reduction in odour responses. Our findings show that neurons sometimes adapt their activity locally in a way that counteracts broader adaptations in the network.\n\n\n\n\n\n\n\n\n\nKey points\n\nNeural networks maintain stable activity levels through homeostatic plasticity – but what physiological variables are stabilised?\nIn inhibitory feedback circuits, local and network‐level compensation might conflict. For example, if excitatory neurons are overactive, they might compensate by becoming less excitable. But if inhibitory neurons compensate for the excess excitation by also becoming less excitable, this would decrease inhibition onto the excitatory neurons and increase their activity.\nWe tested this idea in the fruit fly brain, where excitatory Kenyon cells (KCs) get negative feedback from an inhibitory neuron called anterior paired lateral (APL).\nAfter overactivation of KCs, APL becomes less sensitive to KCs. The resulting loss of inhibition onto KCs counteracts KCs’ attempts to reduce their activity.\nThese results show that adaptation at local and network levels can conflict with each other.\n\n\n"]