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Abstract:
Many processes in the brain require a constant supply of energy. Notably, spiking and maintenance of ion concentration gradients by the Na/K pump require active mechanisms and consumes energy in the form of ATP. Though the brain has a safety margin on energy production compared to the consumption during maximum activity, this margin is small and energy stores can be quickly exhausted, for instance in the context of neuronal disorders [1]. Once energy becomes insufficient, neuronal response can change drastically, leading to intermittent spikes, bursts, network oscillations, or seizures [2].
This crucial impact of metabolic disruption is not addressed in any of the standard neuronal models used in computational neuroscience. We fill this significant gap in current modeling frameworks, by introducing two novel model-neurons that account for energetic constraints while remaining computationally efficient, analytically tractable, and biologically interpretable. Using these new types of neurons, we can reproduce both crucial single-cell behaviors, such as depolarization blocks or bistability, and complex changes in collective dynamics that were not capture by previous integrate-and-fire neurons (shown on panels A and B of the figure).
In this presentation, we will discuss the main mechanisms that define the models’ equations: the role of pumps that degrade ATP into ADP to maintain ion gradients (most notably the Na/K and calcium pumps) and ATP-gated potassium (K-ATP) channels, which open or close depending on the ATP/ADP ratio.
We explain how these mechanisms shape the relationship between energy availability and neuronal excitability [3-4] and illustrate some of the potential consequences. In particular we discuss the influence of metabolic disruption on the information processing capabilities of neurons (see panel A for an example of disease progression on a network in the well-known AI state).
These novel models enable for the first time the theoretical study of energy-associated disorders over the whole time-course of disease progression, instead of only comparing the initially healthy condition with the final diseased state. This advancement is essential to model multiple neurological disorders, such as epilepsy or Parkinson’s disease, as it enables theoretical and computational studies to assess the opportunities of early diagnostics and the potential of energy-centered approaches to improve therapies.
