Active Sensing as Bayes-Optimal Sequential Decision Making

Sensory inference under conditions of uncertainty is a major problem in both machine learning and computational neuroscience. An important but poorly understood aspect of sensory processing is the role of active sensing. Here, we present a Bayes-optimal inference and control framework for active sensing, C-DAC (Context-Dependent Active Controller). Unlike previously proposed algorithms that optimize abstract statistical objectives such as information maximization (Infomax) [Butko & Movellan, 2010] or one-step look-ahead accuracy [Najemnik & Geisler, 2005], our active sensing model directly minimizes a combination of behavioral costs, such as temporal delay, response error, and effort. We simulate these algorithms on a simple visual search task to illustrate scenarios in which context-sensitivity is particularly beneficial and optimization with respect to generic statistical objectives particularly inadequate. Motivated by the geometric properties of the C-DAC policy, we present both parametric and non-parametric approximations, which retain context-sensitivity while significantly reducing computational complexity. These approximations enable us to investigate the more complex problem involving peripheral vision, and we notice that the difference between C-DAC and statistical policies becomes even more evident in this scenario.Comment: Scheduled to appear in UAI 201

Stop paying attention: the need for explicit stopping in inhibitory control

Inhibitory control, the ability to stop inappropriate actions, isan important cognitive function often investigated via the stop-signal task, in which an infrequent stop signal instructs the sub-ject to stop a default go response. Previously, we proposed arational decision-making model for stopping, suggesting theobserver makes a repeated Go versus Wait choice at each in-stant, so that a Stop response is realized by repeatedly choosingto Wait. We propose an alternative model here that incorpo-rates a third choice, Stop. Critically, unlike the Wait action,choosing the Stop action not only blocks a Go response at thecurrent moment but also for the remainder of the trial – thedisadvantage of losing this flexibility is balanced by the bene-fit of not having to pay attention anymore. We show that thisnew model both reproduces known behavioral effects and hasinternal dynamics resembling presumed Go neural activationsin the brain

Rational Decision-Making in Inhibitory Control

An important aspect of cognitive flexibility is inhibitory control, the ability to dynamically modify or cancel planned actions in response to changes in the sensory environment or task demands. We formulate a probabilistic, rational decision-making framework for inhibitory control in the stop signal paradigm. Our model posits that subjects maintain a Bayes-optimal, continually updated representation of sensory inputs, and repeatedly assess the relative value of stopping and going on a fine temporal scale, in order to make an optimal decision on when and whether to go on each trial. We further posit that they implement this continual evaluation with respect to a global objective function capturing the various reward and penalties associated with different behavioral outcomes, such as speed and accuracy, or the relative costs of stop errors and go errors. We demonstrate that our rational decision-making model naturally gives rise to basic behavioral characteristics consistently observed for this paradigm, as well as more subtle effects due to contextual factors such as reward contingencies or motivational factors. Furthermore, we show that the classical race model can be seen as a computationally simpler, perhaps neurally plausible, approximation to optimal decision-making. This conceptual link allows us to predict how the parameters of the race model, such as the stopping latency, should change with task parameters and individual experiences/ability

Global-scale pattern of peatland Sphagnum growth driven by photosynthetically active radiation and growing season length

Journal Article© Author(s) 2012. This work is distributed under the Creative Commons Attribution 3.0 License.High-latitude peatlands contain about one third of the world's soil organic carbon, most of which is derived from partly decomposed Sphagnum (peat moss) plants. We conducted a meta-analysis based on a global data set of Sphagnum growth measurements collected from published literature to investigate the effects of bioclimatic variables on Sphagnum growth. Analysis of variance and general linear models were used to relate Sphagnum magellanicum and S. fuscum growth rates to photosynthetically active radiation integrated over the growing season (PAR0) and a moisture index. We found that PAR0 was the main predictor of Sphagnum growth for the global data set, and effective moisture was only correlated with moss growth at continental sites. The strong correlation between Sphagnum growth and PAR0 suggests the existence of a global pattern of growth, with slow rates under cool climate and short growing seasons, highlighting the important role of growing season length in explaining peatland biomass production. Large-scale patterns of cloudiness during the growing season might also limit moss growth. Although considerable uncertainty remains over the carbon balance of peatlands under a changing climate, our results suggest that increasing PAR0 as a result of global warming and lengthening growing seasons, without major change in cloudiness, could promote Sphagnum growth. Assuming that production and decomposition have the same sensitivity to temperature, this enhanced growth could lead to greater peat-carbon sequestration, inducing a negative feedback to climate change. © 2012 Author(s). CC Attribution 3.0 License

NCR+ ILC3 maintain larger STAT4 reservoir via T-BET to regulate type 1 features upon IL-23 stimulation in mice

Innate lymphoid cells (ILCs) producing IL-22 and/or IL-17, designated as ILC3, comprise a heterogeneous subset of cells involved in regulation of gut barrier homeostasis and inflammation. Exogenous environmental cues in conjunction with regulated expression of endogenous factors are key determinants of plasticity of ILC3 towards the type 1 fate. Herein, by using mouse models and transcriptomic approaches, we defined at the molecular level, initial events driving ILC3 expressing natural cytotoxicity receptors (NCR+ ILC3) to acquire type 1 features. We observed that NCR+ ILC3 exhibited high basal expression of the signal-dependent transcription factor STAT4 due to T-BET, leading to predisposed potential for the type 1 response. We found that the prototypical inducer of type 3 response, IL-23, played a predominant role over IL-12 by accessing STAT4 and preferentially inducing its phosphorylation in ILC3 expressing T-BET. The early effector program driven by IL-23 was characterized by the expression of IL-22, followed by a production of IFN-γ, which relies on STAT4, T-BET and required chromatin remodeling of the Ifng locus. Altogether, our findings shed light on a feed-forward mechanism involving STAT4 and T-BET that modulates the outcome of IL-23 signaling in ILC3. This article is protected by copyright. All rights reserved

Biologically Plausible Neural Circuits for Realization of Maximum Operations

Object recognition in the visual cortex is based on a hierarchical architecture, in which specialized brain regions along the ventral pathway extract object features of increasing levels of complexity, accompanied by greater invariance in stimulus size, position, and orientation. Recent theoretical studies postulate a non-linear pooling function, such as the maximum (MAX) operation could be fundamental in achieving such invariance. In this paper, we are concerned with neurally plausible mechanisms that may be involved in realizing the MAX operation. Four canonical circuits are proposed, each based on neural mechanisms that have been previously discussed in the context of cortical processing. Through simulations and mathematical analysis, we examine the relative performance and robustness of these mechanisms. We derive experimentally verifiable predictions for each circuit and discuss their respective physiological considerations

Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences

Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having the complete geometry of objects aids in their tracking, and thus propose to jointly infer the complete geometry of objects as well as track them, for rigidly moving objects over time. Our key insight is that inferring the complete geometry of the objects significantly helps in tracking. By hallucinating unseen regions of objects, we can obtain additional correspondences between the same instance, thus providing robust tracking even under strong change of appearance. From a sequence of RGB-D frames, we detect objects in each frame and learn to predict their complete object geometry as well as a dense correspondence mapping into a canonical space. This allows us to derive 6DoF poses for the objects in each frame, along with their correspondence between frames, providing robust object tracking across the RGB-D sequence. Experiments on both synthetic and real-world RGB-D data demonstrate that we achieve state-of-the-art performance on dynamic object tracking. Furthermore, we show that our object completion significantly helps tracking, providing an improvement of 6.5% in mean MOTA

Peatlands and Climate Change

This is the author's manuscript version and this version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works.This material is forthcoming in Peatland Restoration and Ecosystem Services Science, Policy and Practice, 9781107619708, © Cambridge University PressThe fundamental reason for the presence of peatlands is a positive balance between plant production and decomposition. Organic matter accumulates in these systems because prolonged waterlogged conditions result in soil anoxia (i.e., exclusion of oxygen), and under these conditions decomposition rates can be lower than those of primary production. Climate therefore plays an important role in peat accumulation, both directly by affecting productivity and decomposition processes, and indirectly through its effects on hydrology/water balance and vegetation (for a summary, refer to Yu, Beilman & Jones 2009). Climate provides broad-scale constraints or controls on peatland extent, types and vegetation, and ultimately, ecosystem functioning, carbon accumulation, greenhouse gas exchange and all of the other ecosystem services that peatlands provide. Peatlands can play a vital role in helping society mitigate and adapt to climate change, because of their carbon and water regulating functions, while at the same time, the climate sensitivity of peatlands makes them potentially vulnerable to future global warming and changes in spatial and temporal patterns of precipitation, especially if they are in a degraded state. Climate change is likely to alter the hydrology and soil temperature of peatlands, with far- reaching consequences for their biodiversity, ecology and biogeochemistry. Their involvement in the global carbon cycle will also be affected, with the possibility of drier conditions allowing peatland erosion and increases in CO2 emissions that would result in a positive feedback to climate change (Turetsky 2010). This highlights all the more the need for restoration to ensure peatlands are resilient to change so that they continue to deliver ecosystem services for human well-being. This chapter describes the interactions between climate and peatlands, in three sections. The first section explains how present climate influences peatlands, by documenting how climate limits peatland geographical extent globally, and how bioclimatic envelope models can predict peatland extent. We indicate how each type of peatland is linked to a specific climate range, and introduce the concept of ecosystem function in relation to climate. The second section looks into the past. It describes how peat preserves a record of past climates and environmental conditions that can be deciphered to reveal the history of peatland vegetation, hydrology and carbon accumulation changes in relation to past changes in climate. We highlight lessons that can be learned from the palaeorecord preserved in peat. The final section discusses the potential effects of present and future climate change on peatlands, their extent, carbon accumulation rates, fire frequency, water table and greenhouse gas exchanges. We also consider how increases in sea level and CO2 concentration, and decreases in the extent of permafrost, are likely to affect peatlands

A human lung tumor microenvironment interactome identifies clinically relevant cell-type cross-talk.

BackgroundTumors comprise a complex microenvironment of interacting malignant and stromal cell types. Much of our understanding of the tumor microenvironment comes from in vitro studies isolating the interactions between malignant cells and a single stromal cell type, often along a single pathway.ResultTo develop a deeper understanding of the interactions between cells within human lung tumors, we perform RNA-seq profiling of flow-sorted malignant cells, endothelial cells, immune cells, fibroblasts, and bulk cells from freshly resected human primary non-small-cell lung tumors. We map the cell-specific differential expression of prognostically associated secreted factors and cell surface genes, and computationally reconstruct cross-talk between these cell types to generate a novel resource called the Lung Tumor Microenvironment Interactome (LTMI). Using this resource, we identify and validate a prognostically unfavorable influence of Gremlin-1 production by fibroblasts on proliferation of malignant lung adenocarcinoma cells. We also find a prognostically favorable association between infiltration of mast cells and less aggressive tumor cell behavior.ConclusionThese results illustrate the utility of the LTMI as a resource for generating hypotheses concerning tumor-microenvironment interactions that may have prognostic and therapeutic relevance

Aberrant ATRX protein expression is associated with poor overall survival in NF1-MPNST

Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are aggressive soft tissue sarcomas that can occur sporadically or in the setting of the Neurofibromatosis type 1 (NF1) cancer predisposition syndrome. These tumors carry a dismal overall survival. Previous work in our lab had identified ATRX chromatin remodeler