Neurons show different complexity depending on measurement scale

How Long Is the Coast of a Neuron? Resolution-Dependent Interaction Order and Computational Capacity

Neural and Evolutionary Computing

Summary

Measuring how neurons process information depends a lot on the detail level scientists look at, like how a coastline looks longer or shorter depending on the ruler used. The authors show that by changing the scale of observation, complex neural interactions can sometimes appear simpler without losing important information. This means that models treating neurons as single units might capture some, but not all, of the neuron's computational details. They explain this with a model where small parts of neurons interact locally before sending signals to the cell body, affecting how the whole neuron’s activity is understood.

What this means in practice

  • For computational neuroscientists: Model neural circuits at multiple resolutions to better capture information processing without overcomplicating models.
  • For neural data analysts: Select observation scales that balance model simplicity and predictive power when analyzing neural recordings.

Tested on simulated data.

Authors

Nima Dehghani

Abstract

Mandlebrot's coastline paradox reframes measurement as a joint property of an object and its ruler. Here we ask how observational resolution shapes the apparent structure of neural computation. We introduce a scale-indexed decomposition that separates the predictive information captured by direct dependencies from the residual outside that model family. Exact constructions show that projection can convert higher-order dependence into pairwise structure. Projection can increase the direct fraction of surviving information even as total predictive information decreases. A schematic branch-to-soma cascade grounds these effects in local coincidence detection, saturating integration and somatic mixing. Resolving organized nonlinear subunits improves approximation and raises effective representational rank. In a nested hierarchy, these gains occur without increasing information about the target. This distinction separates information availability from access by a restricted readout. A point-neuron or direct population model therefore establishes an effective description at a specified scale. It does not uniquely identify the finer computation that generated the observed relation. Which resolution is adequate depends on the observable and perturbation of interest.
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