Neural system identification for large populations separating "what" and "where"

Neuroscientists classify neurons into different types that perform similar computations at different locations in the visual field. Traditional methods for neural system identification do not capitalize on this separation of 'what' and 'where'. Learning deep convolutional feature spaces that are shared among many neurons provides an exciting path forward, but the architectural design needs to account for data limitations: While new experimental techniques enable recordings from thousands of neurons, experimental time is limited so that one can sample only a small fraction of each neuron's response space. Here, we show that a major bottleneck for fitting convolutional neural networks (CNNs) to neural data is the estimation of the individual receptive field locations, a problem that has been scratched only at the surface thus far. We propose a CNN architecture with a sparse readout layer factorizing the spatial (where) and feature (what) dimensions. Our network scales well to thousands of neurons and short recordings and can be trained end-to-end. We evaluate this architecture on ground-truth data to explore the challenges and limitations of CNN-based system identification. Moreover, we show that our network model outperforms current state-of-the art system identification models of mouse primary visual cortex.Comment: NIPS 201

How Are Red and Blue Quasars Different? The Radio Properties

A non-negligible fraction of quasars are red at optical wavelengths, indicating (in the majority of cases) that the accretion disc is obscured by a column of dust which extinguishes the shorter-wavelength blue emission. In this paper, we summarize recent work by our group, where we find fundamental differences in the radio properties of SDSS optically-selected red quasars. We also present new analyses, using a consistent color-selected quasar parent sample matched to four radio surveys (FIRST, VLA Stripe 82, VLA COSMOS 3 GHz, and LoTSS DR1) across a frequency range 144 MHz–3 GHz and four orders of magnitude in radio flux. We show that red quasars have enhanced small-scale radio emission (∼kpc) that peaks around the radio-quiet threshold (defined as the ratio of 1.4 GHz luminosity to 6 μm luminosity) across the four radio samples. Exploring the potential mechanisms behind this enhancement, we rule out star-formation and propose either small-scale synchrotron jets, frustrated jets, or dusty winds interacting with the interstellar medium; the latter two scenarios would provide a more direct connection between opacity (dust; gas) and the production of the radio emission. In our future study, using new multi-band uGMRT data, we aim to robustly distinguish between these scenarios

Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles

Systems neuroscience relies on two complementary views of neural data, characterized by single neuron tuning curves and analysis of population activity. These two perspectives combine elegantly in neural latent variable models that constrain the relationship between latent variables and neural activity, modeled by simple tuning curve functions. This has recently been demonstrated using Gaussian processes, with applications to realistic and topologically relevant latent manifolds. Those and previous models, however, missed crucial shared coding properties of neural populations. We propose feature sharing across neural tuning curves, which significantly improves performance and leads to better-behaved optimization. We also propose a solution to the problem of ensemble detection, whereby different groups of neurons, i.e., ensembles, can be modulated by different latent manifolds. This is achieved through a soft clustering of neurons during training, thus allowing for the separation of mixed neural populations in an unsupervised manner. These innovations lead to more interpretable models of neural population activity that train well and perform better even on mixtures of complex latent manifolds. Finally, we apply our method on a recently published grid cell dataset, recovering distinct ensembles, inferring toroidal latents and predicting neural tuning curves all in a single integrated modeling framework

Efficient coding of natural scenes improves neural system identification

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage normative principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the “stand-alone” system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing

Host Dark Matter Halos of SDSS Red and Blue Quasars: No Significant Difference in Large-scale Environment

The observed optical colors of quasars are generally interpreted in one of two frameworks: unified models that attribute the color to the random orientation of the accretion disk along the line of sight, and evolutionary models that invoke connections between quasar systems and their environments. We test these schemas by probing the dark matter halo environments of optically selected quasars as a function of g − i optical color by measuring the two-point correlation functions of ∼0.34 million eBOSS quasars as well as the gravitational deflection of cosmic microwave background photons around ∼0.66 million XDQSO photometric quasar candidates. We do not detect a trend of halo bias with optical color through either analysis, finding that optically selected quasars at 0.8 < z < 2.2 occupy halos of characteristic mass Mh ∼ 3 × 1012 h−1 M⊙ regardless of their color. This result implies that a quasar's large-scale halo environment is not strongly connected to its observed optical color. We also confirm the findings of fundamental differences in the radio properties of red and blue quasars by stacking 1.4 GHz FIRST images at their positions, suggesting the observed differences cannot be attributed to orientation. Instead, the differences between red and blue quasars likely arise on nuclear-galactic scales, perhaps owing to reddening by a nuclear dusty wind. Finally, we show that optically selected quasars' halo environments are also independent of their r − W2 optical–infrared colors, while previous work has suggested that mid-infrared-selected obscured quasars occupy more massive halos. We discuss the implications of this result for models of quasar and galaxy coevolution

Chandra Observations of NuSTAR Serendipitous Sources near the Galactic Plane

The Nuclear Spectroscopic Telescope Array (NuSTAR) serendipitous survey has already uncovered a large number of active galactic nuclei (AGNs), providing new information about the composition of the cosmic X-ray background. For AGNs off the Galactic plane, it has been possible to use existing X-ray archival data to improve source localizations, identify optical counterparts, and classify the AGNs with optical spectroscopy. However, near the Galactic plane, better X-ray positions are necessary to achieve optical or near-IR identifications due to the higher levels of source crowding. Thus, we have used observations with the Chandra X-ray Observatory to obtain the best possible X-ray positions. With eight observations, we have obtained coverage for 19 NuSTAR serendips within 12° of the plane. One or two Chandra sources are detected within the error circle of 15 of the serendips, and we report on these sources and search for optical counterparts. For one source (NuSTAR J202421+3350.9), we obtained a new optical spectrum and detected the presence of hydrogen emission lines. The source is Galactic, and we argue that it is likely a cataclysmic variable. For the other sources, the Chandra positions will enable future classifications in order to place limits on faint Galactic populations, including high-mass X-ray binaries and magnetars