An important problem in the study of the mammalian visual system is whether functionally different retinal ganglion cell types are anatomically segregated further up along the central visual pathway. It was previously demonstrated that, in a New World diurnal monkey (marmoset), the neurones carrying signals from the short-wavelength-sensitive (S) cones [blue–yellow (B/Y)-opponent cells] are predominantly located in the koniocellular layers of the dorsal lateral geniculate nucleus (LGN), whereas the red–green (R/G)-opponent cells carrying signals from the medium- and long-wavelength-sensitive cones are segregated in the parvocellular layers. Here, we used extracellular single-unit recordings followed by histological reconstruction to investigate the distribution of color-selective cells in the LGN of the macaque, an Old World diurnal monkey. Cells were classified using cone-isolating stimuli to identify their cone inputs. Our results indicate that the majority of cells carrying signals from S-cones are located either in the koniocellular layers or in the ‘koniocellular bridges’ that fully or partially span the parvocellular layers. By contrast, the R/G-opponent cells are located in the parvocellular layers. We conclude that anatomical segregation of B/Y- and R/G-opponent afferent signals for color vision is common to the LGNs of New World and Old World diurnal monkeys.

Corneal and visual changes found in this study confirm previous reports of the rapid effects of short-term OK lens wear. Older lens wearers showed a reduced or delayed response to reverse-geometry lens wear in the short term, suggesting a reduced corneal epithelial response to interventions with increasing age.

Understanding of neuronal circuitry at cellular resolution within the brain has relied on neuron tracing methods which involve careful observation and interpretation by experienced neuroscientists. With recent developments in imaging and digitization, this approach is no longer feasible with the large scale (terabyte to petabyte range) images. Machine learning based techniques, using deep networks, provide an efficient alternative to the problem. However, these methods rely on very large volumes of annotated images for training and have error rates that are too high for scientific data analysis, and thus requires a significant volume of human-in-the-loop proofreading. Here we introduce a hybrid architecture combining prior structure in the form of topological data analysis methods, based on discrete Morse theory, with the best-in-class deep-net architectures for the neuronal connectivity analysis. We show significant performance gains using our hybrid architecture on detection of topological structure (e.g. connectivity of neuronal processes and local intensity maxima on axons corresponding to synaptic swellings) with precision/recall close to 90% compared with human observers. We have adapted our architecture to a high performance pipeline capable of semantic segmentation of light microscopic whole-brain image data into a hierarchy of neuronal compartments. We expect that the hybrid architecture incorporating discrete Morse techniques into deep nets will generalize to other data domains. Author ContributionsThe idea of using topological priors in the pipeline was conceptualized by Y.W. and P.M. Algorithmic design and development was performed by S. B. and L. M.. Proof reading assistance and neuroanatomical expertise for neuroanatomical ground truth data was provided by J. J. and K. M.. Data preparation, including quality control and acquisition were performed by B. H., J. J. and K. M. under the supervision of J. H. and P. M.. The ALBU baseline was tested by D. W.. Evaluation of the algorithm was conducted by S. B., D.