Decoding neuronal variability: bridging shape and behavior

Variability is not a flaw but a fundamental feature of biological entities, including neurons. It serves as a safeguard against chaotic hyper synchronizations, such as epileptic seizures, and enables the encoding of a staggering amount of information in neural circuits. While prior computational investigations often settled for rudimentary neuron models, this study plunges into the rich intricacies of detailed biophysical neuron models.

Blue Brain researchers employed a powerful statistical tool known as Markov chain Monte Carlo (MCMC) to navigate the complex landscape of electrical models. “This method marks a departure from earlier random sampling approaches. It ensures a comprehensive sampling of model parameters, providing a robust foundation for generating accurate model morphology pairs” explains lead author Alexis Arnaudon.

Creating faithful representations of neurons requires a delicate balance between morphology and electrophysiology. By aligning the input resistances of various cellular segments, the researchers sculpted a compatible set of morphologies and electrical models that capture a specific morpho-electrical type. This meticulous approach is demonstrated on layer 5 pyramidal cells (L5PCs) with continuous adapting firing behavior, highlighting that relying solely on morphological variability falls short in reproducing electrical variability. “Studying the variability of L5PCs is crucial to understanding their functional diversity and potential role in neural circuit dynamics”, adds senior author Lida Kanari.

Defining a cell type goes beyond enumerating specific electrophysiological features; it encompasses acknowledging their inherent variability. This variability, which varies across species, age, and brain regions, is a crucial factor for modeling studies. It allows for a deeper exploration of ion channel conductances and their influence on firing types. Moreover, it enables the selection of sub-populations with specific properties, providing a nuanced understanding of the interplay between morphology and electrophysiology.

Senior author Werner Van Geit, group leader at BBP notes “An innovative facet of this study involved the integration of machine learning methods to enhance the assessment process of models and forecast their applicability." This research not only advances our knowledge of neuronal variability but also illuminates the constraints within which models must operate to faithfully represent experimental data. The study hints at low-dimensional and high-dimensional constraints, shedding light on the intricacies of modeling neuronal behavior. As we delve deeper into specific firing types, the need for even more precise constraints, such as specific apical ion channels, becomes apparent.

The study suggests a promising avenue for creating more targeted morphological types of neurons based on specific features. This could ensure that all morphologies of a particular type perform optimally with a single set of parameters. Additionally, it provides a robust framework for assessing the quality of electrical models, both at the individual morphology level and within populations. This innovation could significantly change the way neural modeling is approached.