Dendritic Morphology of Caudal Periaqueductal Gray Projecting Retinal Ganglion Cells in Mongolian Gerbil (Meriones unguiculatus)

In this study we investigated the morphological features of the caudal periaqueductal gray (cPAG)-projecting retinal ganglion cells (RGCs) in Mongolian gerbils using retrograde labeling, in vitro intracellular injection, confocal microscopy and three-dimensional reconstruction approaches. cPAG-projecting RGCs exhibit small somata (10–17 µm) and irregular dendritic fields (201–298 µm). Sizes of somata and dendritic fields do not show obvious variation at different distance from the optic disk (eccentricity). Dendrites are moderately branched. Morphological analysis (n = 23) reveals that cPAG-projecting RGCs ramified in sublamina a and b in the inner plexiform layer. These cells exhibit different stratification patterns based on the thickness of dendritic bands in sublaminas a and b: majority of analyzed cells (16 out of 23) have two bands of arborizations share similar thickness. The rest of analyzed cells (7 out of 23) exhibit thinner band in sublamina a than in sublamina b. Together, the present study suggests that cPAG of Mongolian gerbil could receive direct retinal inputs from two types of bistratified RGCs. Furthermore, a small subset of melanopsin-expressing RGCs (total 41 in 6 animals) is shown to innervate the rostral PAG (rPAG). Functional characteristics of these non-visual center projecting RGCs remain to be determined.published_or_final_versio

A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC