Tatyana O. Sharpee

The paper shows that telomerase elongates a second substrate formed by spontaneous replication fork collapse in budding yeast, significantly contributing to telomere length homeostasis.

The paper analyzes C. elegans neural activity in response to bacterial stimuli, showing that hyperbolic embedding better describes neural dynamics than Euclidean space.

The paper introduces MuH-MDS, a hyperbolic embedding method that scales to large datasets and improves hierarchical structure analysis in biological data.

The paper defines neural code symbols as variable-duration packets in auditory spike trains, showing this approach enables real-time readout and maximizes information capacity.

The paper investigates the dynamics of spinal CPG neurons in scratch reflex rhythmogenesis and develops a neuromechanical model to explain their interactions.

The paper identifies a low-dimensional hyperbolic map of fruit odor mixtures across ripening and fermentation stages, revealing spiral trajectories influenced by microbial processes.

This paper introduces a Bayesian non-metric hyperbolic MDS method to embed data in hyperbolic space, showing robustness to hyperparameters and capturing non-linear structures.

The paper examines how odor representations change across layers of the fly olfactory circuit, finding significant restructuring in the mushroom body that correlates with natural odor distribution patterns.

This study shows that incorporating quadratic computations into multi-stage models improves predictive power and reveals neural computation motifs that enhance selectivity to natural stimuli across visual areas.

The paper introduces ImAge, an image-based biomarker of aging and rejuvenation using chromatin and epigenetic patterns, showing correlation with chronological age and response to interventions.

The paper shows that inhibitory neurons reduce the negative effects of threshold modulation in neural circuits by selectively modulating sparsely responding neurons, enhancing information transmission.

This paper shows that gene expression data can be better understood using hyperbolic geometry instead of Euclidean geometry, improving visualization and analysis.

This paper introduces global t-SNE (g-SNE), a modified t-SNE method that preserves global inter-cluster structure during dimensionality reduction, and shows it outperforms standard t-SNE on synthetic and real datasets.

The paper shows that cortical columns decode neural activity with minimal information loss, outperforming simpler decoders and predicting quadratic cortex size dependence on subcortical brain parts.