Extent to which connectomics constrains network function

Ascertain the extent to which anatomical connectome structure constrains or determines functional neural computations, including how structured versus random connectivity components influence the operation and perturbability of latent processing units embedded in neural dynamics.

Background

In simulations adding random connections to a low-rank structured network, the authors show that functional LPU dynamics can be obscured by dense random connectivity while remaining operational. This motivates a broader question about the relationship between anatomical connectivity and functional computation.

Quantifying how connectomic structure constrains function, especially in the presence of redundant tuning and curved neural manifolds, would inform interpretation of connectome datasets and guide perturbation strategies targeting effective coding subspaces.

References

The extent to which connectomics constrains network function remains an open question in the field .

— Latent computing by biological neural networks: A dynamical systems framework  (2502.14337 - Dinc et al., 20 Feb 2025) in Results, The geometry of the neural code: Principal, tuning, and coding dimensions

For instance, a topological model may represent global mathematical constraints on anatomical connectivity in the brain, but without an accompanying dynamical model it remains unclear how signals propagate through anatomical networks (Woodward 2025).

— Scientific understanding across disciplines: in varietate concordia?  (2608.14378 - Regt et al., 14 Aug 2026) in Footnote 3, Section 3, Biology subsection