Adrián G. Palacios

This study develops a next-generation AAV vector expressing optimized XBP1s to improve proteostasis, reducing Alzheimer's disease features in mouse models.

This paper applies multi-domain signal processing to ERG data from AD patients and controls, identifying significant biomarkers that improve AD detection accuracy.

This study links cognitive resilience in aging degus to preserved GABAergic function in the hippocampal CA3 region, identified through behavioral and electrophysiological assessments.

This study shows that sustained XBP1s expression preserves retinal function and complexity in aging and Alzheimer's disease models.

The paper shows that manipulating the UPR pathway can restore brain function and reduce aging-related deficits.

This study uses multiscale entropy analysis to show reduced retinal signal complexity in a mouse model of Alzheimer's disease, suggesting complexity loss with aging and disease.

The paper introduces an automated method for detecting neuronal ensembles in spiking neural networks, showing it accurately identifies ensembles in both real and synthetic data.

The paper studies how retinal neuronal ensembles process natural images by analyzing their synchronized activity in response to visual stimuli.

The paper shows that retinal ganglion cells adjust their response to complex naturalistic stimuli more efficiently than to simple artificial ones, suggesting the retina encodes motion information more effectively with complex stimuli.

This paper introduces a dimensionality reduction framework for spatio-temporal maximum entropy models of spiking networks, showing significant reduction in retinal data.

This study identifies functional differences between central and peripheral retinal ganglion cells in diurnal rodents, showing distinct receptive field sizes, response dynamics, and temporal sensitivity.