Year: 2026
Type: preprint
Abstract: The dentate gyrus’ principal neuron, the granule cell (GC), plays a key role in pattern separation, transforming similar inputs into dissimilar outputs. We used a new measure based on information theory to evaluate this transformation in an experimentally derived in silico GC model with realistic ion channel distributions across its dendrites. We found that model performance aligned well with current-clamp recordings from mouse brain slices. However, it was also possible to find artificial parameter combinations in the model that functionally outperformed the recorded cells while matching their action potential shapes. Rather than maximising computational power, the biologically realistic GC model balanced energy efficiency against increased functionality in a Pareto optimal way: No possible parameter combination was found to improve one of the objectives without impairing the other. Interestingly, immature GCs adopted a different weighted trade-off with a stronger emphasis on energy efficiency than pattern separation, but they remained Pareto optimal over their putative developmental trajectory. Our results support the idea that evolution favours neurons that can satisfy functional and energetic demands simultaneously. Abstract Figure In brief We find that dentate gyrus granule cells are Pareto optimal for the simultaneous performance of pattern separation and energy economy, adopting a differently weighted trade-off depending on their developmental stage. Highlights An experimentally constrained single granule cell (GC) model can perform temporal pattern separation in line with real data. Artificial channel configurations can improve pattern separation, but strictly at the cost of increased ATP consumption. GCs with realistic channel configurations appear Pareto optimal for the simultaneous performance of pattern separation and energy economy. Immature GCs remain on the Pareto front but prioritise low energy expenditure over pattern separation.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W7220389213