Abstract
The electrical and computational properties of neurons in our brains are determined by a rich repertoire of membrane-bound ion channels and elaborate dendritic trees. However, the precise reason for this inherent complexity remains unknown. Here, we generated large stochastic populations of biophysically realistic hippocampal granule cell models comparing those with all 15 ion channels to their reduced but functional counterparts containing only 5 ion channels. Strikingly, valid parameter combinations in the full models were more frequent and more stable in the face of perturbations. Scaling up the numbers of ion channels artificially in the reduced models recovered these advantages confirming the key contribution of the actual number of ion channels. We conclude that the diversity of ion channels allows a neuron to achieve a target excitability through random channel expression with increased robustness and higher flexibility.
Significance statement Over the course of billions of years, evolution has led to a wide variety of biological systems. The emergence of the more complex among these seems surprising in the light of the high demands on searching viable solutions in a correspondingly high-dimensional parameter space. In realistic neuron models with their inherently complex ion channel composition, we find a surprisingly large number of viable solutions when selecting parameters randomly. This effect is strongly reduced in models with less ion channel types but is recovered when inserting additional artificial ion channels. Because concepts from probability theory provide a plausible explanation for such an improved arrangement of valid model parameters, we propose that this generalises to evolutionary selection in other complex biological systems.
In brief Studying ion channel diversity in neuronal models we show how robust biological systems may evolve not despite but through their complexity.
Highlights
15 channel model of hippocampal granule cells (GCs) reduces to 5 ion channels without loss of spiking behaviour.
But knocking out ion channels can be compensated only in the full model.
Random sampling leads to ∼ 6% solutions in full but only ∼ 1% in reduced model.
Law of large numbers generalises our observations to other complex biological systems.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
↵1 Joint senior authors
