[en] Animal performance relies on their ability to quickly process, analyze and react to incoming events, as well as to learn from experience to constantly increase their knowledge about the environment. Learning and memory are attributed to the ability of neurons to modify their connections with other cells based on experience, a property called synaptic plasticity. Synaptic plasticity mechanisms often exploit the level of correlation in the activity of connected neurons, and can therefore be affected by abrupt changes in neuronal excitability. On the other hand, brain information processing is constantly shaped by fluctuations in neuronal rhythmic activities at the cellular and population levels, each defining distinctive brain states. Switches between these brain states can be fast and localized, such as e.g. those observed in different brain areas prior to movement initiation, or global and long lasting, such as those observed during the wake-sleep transition. The coexistence of these two mechanisms raises challenging questions: how can switches in brain states remain reliable despite of constant rewiring of neuron connectivity, and how is synaptic plasticity affected by switches in brain states? In this work, we highlight the critical role of a cellular dynamical property in the generation of switches in brain states that are compatible with changes in network connectivity and cellular heterogeneity. This dynamical property, called slow regenerativity, is accessible to all neurons that embed slowly activating voltage-gated calcium channels or slowly inactivating potassium channels in their membrane, yet it is largely overlooked in computational and mathematical neuron models and absent from all available hybrid models. To demonstrate this point, we compare the robustness of 5 published thalamic neuron models at the cellular, circuit and population levels. We show that the robustness of rhythms at the population level correlates with the presence or absence of slow regenerativity at the cellular level.
