Stephanie E. Palmer

The paper develops a biophysical model of chromatophore arrays to generate dynamic skin patterns similar to those in cephalopods, showing that minimal central control can produce complex behaviors.

This study shows retinal population codes adjust their predictive timescale to input temporal structure using the information bottleneck framework.

The paper shows humans adjust touch strategies based on perceptual goals, with different physical signals supporting texture perception.

The paper investigates how retinal ganglion cell activity can be compressed while preserving predictive information for downstream brain areas.

The paper analyzes retinal ganglion cell activity to show how sensory population codes balance normative optimality with variability through stiff and sloppy modes.

The paper develops a dynamic scale-mixture model to analyze motion in natural scenes, finding that velocity distributions are heavy-tailed and can be modeled with a Gaussian process multiplied by a dynamic scaling factor.

The paper identifies collective variables in retinal ganglion cells that best predict future neural activity using an information bottleneck method.

The paper identifies sexually dimorphic eye features in Heliconius butterflies, showing differences in UV opsins and screening pigments linked to courtship and oviposition behaviors.

This paper introduces a method using synchrotron-source micro-x-ray computed tomography combined with machine learning to rapidly image and analyze butterfly eye structures, revealing morphological variation and supporting evolutionary insights.

The paper shows that heavy-tailed neuronal connectivity and clustering emerge from a minimal model of Hebbian self-organization, confirmed in animal connectomes.

The paper analyzes retinal population codes in natural scenes to show that stimulus-invariant population structure contributes to scene identity encoding.

The paper investigates how spatially displaced excitation influences the direction selectivity of On-Off DSGCs during interrupted motion, showing its role in encoding visual features.

The paper shows the blowfly's visual system uses efficient prediction to enable fast evasive maneuvers despite processing delays.

The paper shows nonlinear mixed selectivity enables reliable neural computation with lower metabolic energy than pure selectivity.

This study simulates the evolution of tetrachromatic color vision from a trichromatic ancestor, showing that ancestral constraints limit computational evolution.