Color and Spatial Frequency Provide Functional Signatures of Retinotopic Visual Areas.

Primate vision relies on retinotopically organized cortical parcels defined by representations of hemifield (upper vs lower visual field), eccentricity (fovea vs periphery), and area (V1, V2, V3, V4). Here we test for functional signatures of these organizing principles. We used functional magnetic resonance imaging to measure responses to gratings varying in spatial frequency, color, and saturation across retinotopically defined parcels in two macaque monkeys, and we developed a Sparse Supervised Embedding (SSE) analysis to identify stimulus features that best distinguish cortical parcels from each other.

Discovering optimal kinetic pathways for self-assembly using automatic differentiation.

Macromolecular complexes are often composed of diverse subunits. The self-assembly of these subunits is inherently nonequilibrium and must avoid kinetic traps to achieve high yield over feasible timescales. We show how the kinetics of self-assembly benefits from diversity in subunits because it generates an expansive parameter space that naturally improves the "expressivity" of self-assembly, much like a deeper neural network.

Discovering optimal kinetic pathways for self-assembly using automatic differentiation.

During self-assembly of macromolecules ranging from ribosomes to viral capsids, the formation of long-lived intermediates or kinetic traps can dramatically reduce yield of the functional products. Understanding biological mechanisms for avoiding traps and efficiently assembling is essential for designing synthetic assembly systems, but learning optimal solutions requires numerical searches in high-dimensional parameter spaces. Here, we exploit powerful automatic differentiation algorithms commonly employed by deep learning frameworks to optimize physical models of reversible self-assembly, discovering diverse solutions in the space of rate constants for 3-7 subunit complexes.

NERDSS: A Nonequilibrium Simulator for Multibody Self-Assembly at the Cellular Scale.

Currently, a significant barrier to building predictive models of cellular self-assembly processes is that molecular models cannot capture minutes-long dynamics that couple distinct components with active processes, whereas reaction-diffusion models cannot capture structures of molecular assembly. Here, we introduce the nonequilibrium reaction-diffusion self-assembly simulator (NERDSS), which addresses this spatiotemporal resolution gap. NERDSS integrates efficient reaction-diffusion algorithms into generalized software that operates on user-defined molecules through diffusion, binding and orientation, unbinding, chemical transformations, and spatial localization.