Retinal adaptation to spatial correlations

The classical center-surround retinal ganglion cell receptive field is thought to remove the strong spatial correlations in natural scenes, enabling efficient use of limited bandwidth. While early studies with drifting gratings reported robust surrounds (Enroth-Cugell and Robson, 1966), recent measurements with white noise reveal weak surrounds (Chichilnisky and Kalmar, 2002). This might be evidence for dynamical weakening of the retinal surround in response to decreased spatial correlations, which would be predicted by efficient coding theory. Such adaptation is reported in LGN (Lesica et al., 2007), but whether the retina also adapts to correlations is unknown. 

We tested for adaptation by recording simultaneously from ~40 ganglion cells on a multi-electrode array while presenting white and exponentially correlated checkerboards and strips. Measuring from ~200 cells responding to 90 minutes each of white and correlated stimuli, we were able to extract precise spatiotemporal receptive fields (STRFs). We found that a difference-of-Gaussians was not a good fit and the surround was generally displaced from the center. Thus, to assess surround strength we found the center and surround regions and the total weight on the pixels in each region. The relative surround strength was then defined as the ratio of surround weight to center weight. Surprisingly, we found that the majority of recorded cells have a stronger surround under white noise than under correlated noise (p<.05), contrary to naive expectation from theory. The conclusion was robust to different methods of extracting STRFs and persisted with checkerboard and strip stimuli.

To test, without assuming a model, whether the retina decorrelates stimuli, we also measured the pairwise correlations between spike trains of simultaneously recorded neurons under three conditions: white checkerboard, exponentially correlated noise, and scale-free noise. The typical amount of pairwise correlation increased with extent of input correlation, in line with our STRF measurements

Franz Joseph Rosinack (1748-1823): A Bohemian Oboist and Music Arranger at the Fürstenberg Hofkapelle

The oboist Franz Joseph Rosinack worked at the Fürstenberg princely court in Donaueschingen from 1777 to 1823. He had a range of duties, including performing with the Hof-orchester, Harmonie, and other ensembles, as well as supplying music for court occasions. Chapter I presents a survey of the court\u27s musical activities and principal musicians under Princes Joseph Wenzel (r. 1762-83), Joseph Maria Benedict (r. 1783-96), Karl Joachim (1796-1804), and Karl Egon II (r. 1817-54). Fürstenberg ties to the major cultural centers of eighteenth-century Europe supplied repertoire allowing the court orchestra to perform some of the best contemporary operas often within months of their premieres. Rosinack\u27s involvement in these and other performances gave him a familiarity with pieces he would then arrange as chamber music to accompany banquets, hunting parties, and other court festivities. Over fifty of Rosinack\u27s arrangements are preserved as manuscripts in the Fürstenberg Musicalien Sammlung now housed in the Badische Landesbibliothek in Karlsruhe, Germany. Rosinack arranged music from across the broad spectrum of eighteenth-century genres, from chamber music and symphonies to operas and other works for the stage. Chapter III elucidates the techniques he used to create these works by examining excerpts from three representative pieces. These pieces include versions of Mozart\u27s string quartet K. 575 and wind serenade K. 361, both for oboe, violin, two violas, and cello, as well as an arrangement of Haydn\u27s opera Orlando Paladino for Harmonie octet. Generally, the further afield the genre of the original lay from its arranged form, the more far-reaching were the changes that Rosinack made to bring the music to its new setting. Arrangements of chamber music revolve mostly around issues of texture, tone color, and instrumental capacity. Arrangements of operas, however, can involve changes of form and harmonic structure to bring the music from its original stage genre to a chamber setting. Appendices present a complete list of Rosinack\u27s arrangements as well as a score to the first movement of Rosinack\u27s adaptation of K. 361 for oboe and strings

Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks

Calcium imaging is an important technique for monitoring the activity of thousands of neurons simultaneously. As calcium imaging datasets grow in size, automated detection of individual neurons is becoming important. Here we apply a supervised learning approach to this problem and show that convolutional networks can achieve near-human accuracy and superhuman speed. Accuracy is superior to the popular PCA/ICA method based on precision and recall relative to ground truth annotation by a human expert. These results suggest that convolutional networks are an efficient and flexible tool for the analysis of large-scale calcium imaging data.Comment: 9 pages, 5 figures, 2 ancillary files; minor changes for camera-ready version. appears in Advances in Neural Information Processing Systems 29 (NIPS 2016

Genome-Wide Association Study of Alzheimer's Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Dataset

Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline

Transformation of Stimulus Correlations by the Retina

Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible.</p

Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate

Genome-Wide Association Study of Alzheimer's Disease Brain Imaging Biomarkers and Neuropsychological Phenotypes in the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Dataset

Alzheimer's disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements