Which cellular mechanism yields compatibility between brain states, synaptic plasticity and neuromodulation?

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. On the other hand, brain information processing is shaped by fluctuations in neuronal rhythms at the cellular and population levels, each defining distinctive brain states. Switches between these brain states, driven by neuromodulation, can be fast and localized, such as those observed in Parkinson disease patients stopping, almost instantaneously, tremor symptoms when deep-brain stimulation is turned on. Switches can also be 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 constant rewiring of neuron connectivity and how is synaptic plasticity a!ected by switches in brain states?
Here, we highlight the role of slow regenerativity, a cellular dynamical property, in the generation of brain state switches that are robust to cellular heterogeneity, independent from network connectivity, and thus compatible with synaptic plasticity. This key mechanism is accessible by all neurons that embed slowly activating voltage-gated calcium channels or slowly inactivating potassium channels in their membrane. Yet, in computational neuron models, this channel dynamics is often considered as an instantaneous event and it is absent from all available hybrid models.
To demonstrate this point, we compare the robustness of 6 published thalamic neuron conductance-based models at the cellular, circuit and population levels [Destexhe, 1996; Destexhe, 1998; Drion, 2018; Huguenard and McCormick, 1992; Rush and Rinzel, 1994; Wang, 1994]. We show that the robustness of rhythms at the population level correlates with the presence of slow regenerativity at the cellular level. Our work confirms that slow regenerativity is independent on the type of neurons, the intrinsic frequency of the firing pattern and valid for global and lasting brain state switches as well as local and fast. Second, we show that this mechanism can be embedded in simple hybrid neuron models without increasing the model complexity. These results open the possibility to study the interactions between switches in network rhythmic activity and synaptic plasticity in large neuronal populations.