Neural Prediction Errors Reveal a Risk-Sensitive Reinforcement-Learning Process in the Human Brain

Humans and animals are exquisitely, though idiosyncratically, sensitive to risk or variance in the outcomes of their actions. Economic, psychological, and neural aspects of this are well studied when information about risk is provided explicitly. However, we must normally learn about outcomes from experience, through trial and error. Traditional models of such reinforcement learning focus on learning about the mean reward value of cues and ignore higher order moments such as variance. We used fMRI to test whether the neural correlates of human reinforcement learning are sensitive to experienced risk. Our analysis focused on anatomically delineated regions of a priori interest in the nucleus accumbens, where blood oxygenation level-dependent (BOLD) signals have been suggested as correlating with quantities derived from reinforcement learning. We first provide unbiased evidence that the raw BOLD signal in these regions corresponds closely to a reward prediction error. We then derive from this signal the learned values of cues that predict rewards of equal mean but different variance and show that these values are indeed modulated by experienced risk. Moreover, a close neurometric–psychometric coupling exists between the fluctuations of the experience-based evaluations of risky options that we measured neurally and the fluctuations in behavioral risk aversion. This suggests that risk sensitivity is integral to human learning, illuminating economic models of choice, neuroscientific models of affective learning, and the workings of the underlying neural mechanisms

Constitutive Association of Tie1 and Tie2 with Endothelial Integrins is Functionally Modulated by Angiopoietin-1 and Fibronectin

Functional cross-talk between Tie2 and Integrin signaling pathways is essential to coordinate endothelial cell adhesion and migration in response to the extracellular matrix, yet the mechanisms behind this phenomenon are unclear. Here, we examine the possibility that receptor cross-talk is driven through uncharacterized Tie-integrin interactions on the endothelial surface. Using a live cell FRET-based proximity assay, we monitor Tie-integrin receptor recognition and demonstrate that both Tie1 and Tie2 readily associate with integrins α5ß1 and αVß3 through their respective ectodomains. Although not required, Tie2-integrin association is significantly enhanced in the presence of the extracellular component and integrin ligand fibronectin. In vitro binding assays with purified components reveal that Tie-integrin recognition is direct, and further demonstrate that the receptor binding domain of the Tie2 ligand Ang-1, but not the receptor binding domain of Ang-2, can independently associate with α5ß1 or αVß3. Finally, we reveal that cooperative Tie/integrin interactions selectively stimulate ERK/MAPK signaling in the presence of both Ang-1 and fibronectin, suggesting a molecular mechanism to sensitize Tie2 to extracellular matrix. We provide a mechanistic model highlighting the role of receptor localization and association in regulating distinct signaling cascades and in turn, the angiogenic switch

Fourier-based Function Secret Sharing with General Access Structure

Function secret sharing (FSS) scheme is a mechanism that calculates a function f(x) for x in {0,1}^n which is shared among p parties, by using distributed functions f_i:{0,1}^n -> G, where G is an Abelian group, while the function f:{0,1}^n -> G is kept secret to the parties. Ohsawa et al. in 2017 observed that any function f can be described as a linear combination of the basis functions by regarding the function space as a vector space of dimension 2^n and gave new FSS schemes based on the Fourier basis. All existing FSS schemes are of (p,p)-threshold type. That is, to compute f(x), we have to collect f_i(x) for all the distributed functions. In this paper, as in the secret sharing schemes, we consider FSS schemes with any general access structure. To do this, we observe that Fourier-based FSS schemes by Ohsawa et al. are compatible with linear secret sharing scheme. By incorporating the techniques of linear secret sharing with any general access structure into the Fourier-based FSS schemes, we show Fourier-based FSS schemes with any general access structure.Comment: 12 page

The contribution of star formation and merging to stellar mass buildup in galaxies

We present a formalism to infer the presence of merging by comparing the time derivative of the observed galaxy stellar mass function (MF) to the change of the MF expected from the star formation rate (SFR) in galaxies as a function of mass and time. We present the SFR in as a function of stellar mass and time spanning 9=3 the average SFR, is a power law of stellar mass (SFR~M^0.6). The average SFR in the most massive objects at this redshift is 100-500 Msun/yr. At z~3, the SFR starts to drop at the high mass end. As z decreases further, the SFR drops at progressively lower masses (downsizing), dropping most rapidly for high mass (logM>11) galaxies. The mass at which the SFR starts to deviate from the power-law form (break mass) progresses smoothly from logM~13 at z~5 to logM~10.9 at z~0.5. The break mass evolves with redshift as M(z)=2.7x10^10 (1+z)^2.1. We directly observe a relationship between SFH and mass. More massive galaxies have steeper and earlier onsets of SF, their SFR peaks earlier and higher, and the following decay has a shorter e-folding time. The SFR observed in high mass galaxies at z~4 is sufficient to explain their rapid increase in number density. Within large uncertainties, at most 0.8 major mergers per Gyr are consistent with the high-z data, yet enough to transform most high mass objects into ellipticals contemporaneously with their major star formation episode. In contrast, at z11, mergers contribute 0.1-0.2 Gyr^-1 to the relative increase in number density (~1 major merger per massive object at 1.5>z>0). At 10<logM<11, galaxies are being preferably destroyed in mergers at high z, while at later times the change in their numbers turns positive. This shows the top-down buildup of the red sequence suggested by other observations.Comment: Accepted for publication in Ap

SDSS-IV MaNGA: Identification of active galactic nuclei in optical integral field unit surveys

In this paper, we investigate 2727 galaxies observed by MaNGA as of June 2016 to develop spatially resolved techniques for identifying signatures of active galactic nuclei (AGN). We identify 303 AGN candidates. The additional spatial dimension imposes challenges in identifying AGN due to contamination from diffuse ionized gas, extra-planar gas and photoionization by hot stars. We show that the combination of spatially-resolved line diagnostic diagrams and additional cuts on H$\alpha$ surface brighness and H$\alpha$ equivalent width can distinguish between AGN-like signatures and high-metallicity galaxies with LINER-like spectra. Low mass galaxies with high specific star formation rates are particularly difficult to diagnose and routinely show diagnostic line ratios outside of the standard star-formation locus. We develop a new diagnostic -- the distance from the standard diagnostic line in the line-ratios space -- to evaluate the significance of the deviation from the star-formation locus. We find 173 galaxies that would not have been selected as AGN candidates based on single-fibre spectral measurements but exhibit photoionization signatures suggestive of AGN activity in the MaNGA resolved observations, underscoring the power of large integral field unit (IFU) surveys. A complete census of these new AGN candidates is necessary to understand their nature and probe the complex co-evolution of supermassive black holes and their hosts.Comment: 18 pages, 11 figures, accepted to MNRA

Statistical Computations Underlying the Dynamics of Memory Updating

Psychophysical and neurophysiological studies have suggested that memory is not simply a carbon copy of our experience: Memories are modified or new memories are formed depending on the dynamic structure of our experience, and specifically, on how gradually or abruptly the world changes. We present a statistical theory of memory formation in a dynamic environment, based on a nonparametric generalization of the switching Kalman filter. We show that this theory can qualitatively account for several psychophysical and neural phenomena, and present results of a new visual memory experiment aimed at testing the theory directly. Our experimental findings suggest that humans can use temporal discontinuities in the structure of the environment to determine when to form new memory traces. The statistical perspective we offer provides a coherent account of the conditions under which new experience is integrated into an old memory versus forming a new memory, and shows that memory formation depends on inferences about the underlying structure of our experience.Templeton FoundationAlfred P. Sloan Foundation (Fellowship)National Science Foundation (U.S.) (NSF Graduate Research Fellowship)National Institute of Mental Health (U.S.) (NIH Award Number R01MH098861