Explain why geometry-only models capture macroscale cortical dynamics despite fast non-local projections

Determine the mechanisms and conditions under which geometrically constrained neural field models that assume spatially homogeneous and isotropic connectivity can successfully capture macroscale cortical dynamics, despite neglecting the specificity of fast-conducting, non-local projections (FNPs) known to mediate rapid, long-range interactions between remote neural populations.

Background

Substantial empirical evidence shows that fast-conducting, non-local projections (FNPs) provide rapid communication between distributed cortical populations and are considered essential to cognition and behavior. In contrast, many macroscale properties of cortical activity are successfully modeled by geometrically constrained neural field formulations that assume spatially homogeneous and isotropic connectivity on the cortical surface, effectively ignoring the specific wiring captured by connectomic data.

This apparent discrepancy motivates a central question addressed by the paper: if FNPs are crucial for rapid, non-local propagation, why do geometry-only models reproduce many observed features of macroscale cortical dynamics? The authors introduce a hybrid model incorporating both geometric propagation and discrete FNPs and show that FNPs primarily influence fast, stimulus-evoked dynamics on millisecond timescales and spatially precise inputs, whereas slower, spontaneous fluctuations on longer timescales resemble geometric dynamics. The open question focuses on establishing a principled account of when and why geometric models can suffice despite the presence of specific FNP connectivity.